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20 pages, 34237 KiB  
Article
Spatiotemporal Analysis of Atmospheric Chemical Potential Anomalies Associated with Major Seismic Events (Ms ≥ 7) in Western China: A Multi-Case Study
by Qijun Jiao, Qinqin Liu, Changgui Lin, Feng Jing, Jiajun Li, Yuxiang Tian, Zhenxia Zhang and Xuhui Shen
Remote Sens. 2025, 17(2), 311; https://doi.org/10.3390/rs17020311 - 16 Jan 2025
Viewed by 288
Abstract
Focusing on major earthquakes (EQs; MS ≥ 7) in Western China, this study primarily analyzes the fluctuation in Atmospheric Chemical Potential (ACP) before and after the Wenchuan, Yushu, Lushan, Jiuzhaigou, and Maduo EQs via Climatological Analysis of Seismic Precursors Identification (CAPRI). The distribution [...] Read more.
Focusing on major earthquakes (EQs; MS ≥ 7) in Western China, this study primarily analyzes the fluctuation in Atmospheric Chemical Potential (ACP) before and after the Wenchuan, Yushu, Lushan, Jiuzhaigou, and Maduo EQs via Climatological Analysis of Seismic Precursors Identification (CAPRI). The distribution of vertical ACP revealed distinct altitude-dependent characteristics. The ACP at lower atmospheric layers (100–2000 m) exhibited a high correlation, and this correlation decreased with increasing altitude. Anomalies were detected within one month prior to each of the five EQs studied, with the majority occurring 14 to 30 days before the events, followed by a few additional anomalies. The spatial distribution of anomalies is consistent with the distribution of fault zones, with noticeable fluctuation in surrounding areas. The ACP at an altitude of 200 m gave a balance between sensitivity to seismic signals and minimal surface interference and proved to be optimal for EQ monitoring in Western China. The results offer a significant reference for remote sensing studies related to EQ monitoring and the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model, thereby advancing our understanding of pre-seismic atmospheric variations in Western China. Full article
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Figure 1

Figure 1
<p>The epicenters, average altitudes, and associated fault zones of the five selected earthquakes (EQs) in this study. The average altitude data were derived by calculating the mean value within a 700 km half-side length centered on the epicenter, using the mid-layer height data from each model layer of MERRA-2. The red dots represent the epicenters, the blue solid lines represent the fault zones, and the yellow solid lines represent the provincial boundaries.</p>
Full article ">Figure 2
<p>During the Wenchuan (<b>a</b>), Yushu (<b>b</b>), Lushan (<b>c</b>), Jiuzhaigou (<b>d</b>), and Maduo (<b>e</b>) EQs, Atmospheric Chemical Potential (ACP) variations were observed across eight distinct altitudinal strata during the EQ period, with data points recorded every 3 h. The ACP values in the figure represent the spatial average with the epicenter as the center and a half-side length of 700 km. The red dashed vertical line on the right represents the EQ occurrence.</p>
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<p>PCC (Pearson Correlation Coefficient) of ACPs at eight distinct altitudinal strata during the Wenchuan (<b>a</b>), Yushu (<b>b</b>), Lushan (<b>c</b>), Jiuzhaigou (<b>d</b>), and Maduo (<b>e</b>) EQ periods.</p>
Full article ">Figure 4
<p>Monitoring maps of ACP anomalous (200 m) response at 18:00 during the EQ periods for Wenchuan (<b>a</b>), Yushu (<b>b</b>), Lushan (<b>c</b>), Jiuzhaigou (<b>d</b>), and Maduo (<b>e</b>) after removing the global warming effect using the CAPRI algorithm. Comparison of the time series (dashed red line) concerning the historical mean (continuous blue line). The stripes indicate 1.0 (cyan), 1.5 (green), and 2.0 (yellow) times the standard deviation. The red vertical line on the right represents EQ occurrence. The red circles indicate that anomalies greater than 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> appeared.</p>
Full article ">Figure 5
<p>ACP anomaly distribution maps during the period of the 2008 Wenchuan EQ. These maps were obtained by subtracting the spatial distribution on the reference date (5 May) from the distributions on the anomaly dates 28 February (<b>a</b>), 1 March (<b>b</b>), and 24 April (<b>c</b>). “Mean” represents the spatial average of the figure. The epicenter is indicated by an asterisk in the figure, and grey lines indicate major faults in the study area.</p>
Full article ">Figure 6
<p>ACP anomaly distribution maps during the period of the 2010 Yushu EQ. These maps were obtained by subtracting the spatial distribution on the reference date (8 April) from the distributions on the anomaly dates 15 March (<b>a</b>), 18 March (<b>b</b>), 20 March (<b>c</b>), 7 April (<b>d</b>), 26 April (<b>e</b>), and 27 April (<b>f</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure 7
<p>ACP anomaly distribution maps during the period of the 2013 Lushan EQ. These maps were obtained by subtracting the spatial distribution on the reference date (26 March) from the distributions on the anomaly dates 4 March (<b>a</b>), 7 March (<b>b</b>), and 12 March (<b>c</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure 8
<p>ACP anomaly distribution maps during the period of the 2017 Jiuzhaigou EQ. These maps were obtained by subtracting the spatial distribution on the reference date (14 August) from the distributions on the anomaly dates of 9 July (<b>a</b>), 10 July (<b>b</b>), and 9 August (<b>c</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure 9
<p>ACP anomaly distribution maps during the period of the 2017 Maduo EQ. These maps were obtained by subtracting the spatial distribution on the reference date (25 May) from the distributions on the anomaly dates of 14 March (<b>a</b>), 21 March (<b>b</b>), 22 March (<b>c</b>), and 7 May (<b>d</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure A1
<p>Monitoring maps of ACP anomalous (200 m) response at 18:00 in 2020 during the EQ periods for Wenchuan (<b>a</b>), Yushu (<b>b</b>), Lushan (<b>c</b>), Jiuzhaigou (<b>d</b>), and Maduo (<b>e</b>) after removing the global warming effect using the CAPRI algorithm. Labeled as shown in <a href="#remotesensing-17-00311-f004" class="html-fig">Figure 4</a>.</p>
Full article ">Figure A2
<p>ACP anomaly distribution maps during the period of the 2008 Wenchuan EQ. These maps were obtained by subtracting the spatial distribution on the reference date (7 March) from the distributions on the anomaly dates of 28 February (<b>a</b>), 1 March (<b>b</b>), and 24 April (<b>c</b>). “Mean” represents the spatial average of the figure. Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure A3
<p>ACP anomaly distribution maps during the period of the 2010 Yushu EQ. These maps were obtained by subtracting the spatial distribution on the reference date (27 February) from the distributions on the anomaly dates of 15 March (<b>a</b>), 18 March (<b>b</b>), 20 March (<b>c</b>), 7 April (<b>d</b>), 26 April (<b>e</b>), and 27 April (<b>f</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure A4
<p>ACP anomaly distribution maps during the period of the 2013 Lushan EQ. These maps were obtained by subtracting the spatial distribution on the reference date (20 March) from the distributions on the anomaly dates of 4 March (<b>a</b>), 7 March (<b>b</b>), and 12 March (<b>c</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure A5
<p>ACP anomaly distribution maps during the period of the 2017 Jiuzhaigou EQ. These maps were obtained by subtracting the spatial distribution on the reference date (3 July) from the distributions on the anomaly dates of 9 July (<b>a</b>), 10 July (<b>b</b>), and 9 August (<b>c</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
Full article ">Figure A6
<p>ACP anomaly distribution maps during the period of the 2017 Maduo EQ. These maps were obtained by subtracting the spatial distribution on the reference date (28 March) from the distributions on the anomaly dates of 14 March (<b>a</b>), 21 March (<b>b</b>), 22 March (<b>c</b>), and 7 May (<b>d</b>). Labeled as shown in <a href="#remotesensing-17-00311-f005" class="html-fig">Figure 5</a>.</p>
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21 pages, 5166 KiB  
Article
Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses
by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis and Yasuhide Hobara
Atmosphere 2025, 16(1), 88; https://doi.org/10.3390/atmos16010088 - 15 Jan 2025
Viewed by 294
Abstract
The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological [...] Read more.
The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological “open” data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense “ground-based” network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC. Full article
(This article belongs to the Section Meteorology)
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Figure 1
<p>Location of the epicenter of the 1995 Kobe EQ (indicating by a red star), together with a few AMeDAS stations (by black boxes) close to the EQ epicenter. Additionally, the fault regions possibly related with the EQ are plotted.</p>
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<p>Temporal evolutions of solar-terrestrial conditions. From the top, Dst index, Kp index, and solar radiation flux at the wavelength of 10.7 cam (f10.7).</p>
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<p>(<b>a</b>) Temporal evolution of daily T/Hum values, with confidence bounds, and (<b>b</b>) detrended δ(T/Hum). A few colored curves are plotted in (<b>a</b>); the bottom blue curve refers to the mean value (m), green, m + σ (standard deviation), orange, m + 2σ, and red, m + 3σ. Here, the values of m and σ are estimated during 30 days before the current day, and in (<b>b</b>), we have plotted the mean, ±σ, ±2σ, and ±3σ curves. The day of the EQ is indicated by a vertical red line. Further, the periods of geomagnetic storms (light-red boxes) and a typhoon (blue vertical dotted line) are indicated for your reference.</p>
Full article ">Figure 4
<p>(<b>a</b>) Temporal evolution of daily ACP values, with confidence bounds, and (<b>b</b>) detrended δ(ACP). A few colored curves are plotted in (<b>a</b>); the bottom blue curve refers to the mean value (m), green, m + σ (standard deviation), orange, m + 2σ, and red, m + 3σ. Here, the values of m and σ are estimated during 30 days before the current day, and in (<b>b</b>), we have plotted the mean, ±σ, ±2σ, and ±3σ curves. The day of the EQ is indicated by a vertical red line. Further, the periods of geomagnetic storms (light-red boxes) and a typhoon (blue vertical dotted line) are indicated for reference.</p>
Full article ">Figure 5
<p>Statistics of δ(T/Hum) data (histogram of values and corresponding Gaussian fitting) over (<b>a</b>) the summer period 1/6/1994–31/08/1994, when the data present a kurtosis k = 3.7757 and were fitted by a Gaussian distribution with a fitting log likelihood of 135.999, and (<b>b</b>) the winter period 1 December 1994–28 February 1995, when the data present a kurtosis k = 4.2870 and were fitted by a Gaussian distribution with a fitting log likelihood of 150.724.</p>
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<p>Statistics of δ(ACP) data (histogram of values and corresponding Gaussian fitting) over (<b>a</b>) the summer period 1/6/1994–31/08/1994, when the data present a kurtosis k = 3.2545 and were fitted by a Gaussian distribution with a fitting log likelihood of 414.502, and (<b>b</b>) the winter period 1 December 1994–28 February 1995, when the data present a kurtosis k = 2.8359 and were fitted by a Gaussian distribution with a fitting log likelihood of 393.356.</p>
Full article ">Figure 7
<p>Spatial distributions (as contour maps) of anomaly intensity for (<b>a</b>) T/Hum and (<b>b</b>) ACP by making full use of more than 40 AMeDAS stations on 10 January 1995. The small black dots are AMeDAS stations and the EQ epicenter is indicated by a red star.</p>
Full article ">Figure 7 Cont.
<p>Spatial distributions (as contour maps) of anomaly intensity for (<b>a</b>) T/Hum and (<b>b</b>) ACP by making full use of more than 40 AMeDAS stations on 10 January 1995. The small black dots are AMeDAS stations and the EQ epicenter is indicated by a red star.</p>
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<p>Model architecture of a hybrid NARX and LSTM. LSTM is used as a core part of the NARX model. Specifically, LSTM is responsible for combining past time series data and external inputs to predict future values in the NARX model.</p>
Full article ">Figure 9
<p>Deviation of observed T/Hum values from NARX-LSTM-predicted values with Bollinger band analysis of ±2σ and ±3σ. The day of the EQ is indicated by a thick vertical red line. Additionally, possible time spans of geomagnetic disturbances are indicated by light-red boxes, whereas a typhoon day is also marked. Finally, the value exceeding the +3σ Bollinger band, indicated by the red circle on the top of the peak on 10 January 1995, is highly likely to be an EQ precursor.</p>
Full article ">Figure 10
<p>Deviation of observed ACP values from NARX-LSTM-predicted values with Bollinger band analysis of ±2σ and ±3σ. The day of the EQ is indicated by a thick vertical red line. Additionally, possible time spans of geomagnetic disturbances are indicated by light-red boxes, whereas a typhoon day is also marked. Finally, the value exceeding well above the +2σ Bollinger band, indicated by the red circle on the top of the peak on 10 January 1995, is highly likely to be an EQ precursor.</p>
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22 pages, 7255 KiB  
Article
Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan
by Karan Nayak, Rosendo Romero-Andrade, Gopal Sharma, Charbeth López-Urías, Manuel Edwiges Trejo-Soto and Ana Isela Vidal-Vega
Atmosphere 2024, 15(12), 1492; https://doi.org/10.3390/atmos15121492 - 14 Dec 2024
Viewed by 958
Abstract
This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and [...] Read more.
This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts. Full article
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Figure 1

Figure 1
<p>Seismotectonic map of the Noto Peninsula in Japan depicting the earthquake on 1 January 2024 (highlighted by the black star). Aftershocks are marked with red stars, green triangles denote CORS points used for TEC analysis within the earthquake preparation zone, and active faults are represented by brown lines (modified after Styron et al., 2020 [<a href="#B30-atmosphere-15-01492" class="html-bibr">30</a>]).</p>
Full article ">Figure 2
<p>TEC readings taken at the closest USUD station in the month leading up to the earthquake. The upper and lower boundaries, determined by Equation (2), are represented by the black and green solid lines, respectively. The daily TEC values, measured in TEC units (TECU), are depicted by the red lines. Any deviations beyond these limits are identified as anomalies, with positive anomalies shown as black columns and negative anomalies as green columns.</p>
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<p>TEC readings taken at MIZU station in the month leading up to the earthquake. The upper and lower boundaries, determined by Equation (2), are represented by the black and green solid lines, respectively. The daily TEC values, measured in TEC units (TECU), are depicted by the red lines. Any deviations beyond these limits are identified as anomalies, with positive anomalies shown as black columns and negative anomalies as green columns.</p>
Full article ">Figure 4
<p>Elaborate depiction of the day-to-day changes in the Dst, Kp, and F10.7 index observed between 17 November and 31 December, covering the 45 days preceding the earthquake. In each subplot, red horizontal lines signify the predetermined threshold levels for these indices. Anomalies in the TEC patterns are taken into account only when the indices fall below their respective thresholds.</p>
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<p>Anomalous variations in TEC depletions exceeding a threshold of at least 3 TEC units (3 × 10<sup>16</sup> electrons/m<sup>2</sup>).</p>
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<p>Observation of TEC on the anomaly day with the PNA time at 3.367 UTC, considering data from the nearby 22 stations. The X-axis illustrates the CORS distance from the epicenter, while the Y-axis represents the vTEC in relation to TEC units.</p>
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<p>Relationship between TEC Pearson correlation and distance, showing spatial decay in correlation with statistical significance across stations for the anomaly day of 7 December 2023.</p>
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<p>Spatial distribution of vTEC based on data from 22 neighboring stations, depicted by green triangles, during the Peak Negative Anomaly time at 3.367 UTC on the anomaly day of 7 December.</p>
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<p>Observation of TEC on the anomaly day with, the PNA time at 3.117 UTC, considering data from the nearby 22 stations. The X-axis illustrates the CORS distance from the epicenter, while the Y-axis represents the vTEC in relation to TEC units.</p>
Full article ">Figure 10
<p>Relationship between TEC Pearson correlation and distance, showing spatial decay in correlation with statistical significance across stations for the anomaly day of 8 December 2023.</p>
Full article ">Figure 11
<p>Spatial distribution of vTEC based on data from 22 neighboring stations, depicted by green triangles, during the Peak Negative Anomaly time at 3.117 UTC on the anomaly day of 8 December.</p>
Full article ">Figure 12
<p>Anomalous variations in TEC depletions exceeding a threshold of at least 3 TEC units, as observed from the AIRA station for the Nichinan earthquake of Japan.</p>
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<p>Observation of TEC on the anomaly day with the PNA time at 3.85 UTC, considering data from the 17 stations. The X-axis illustrates the CORS distance from the epicenter, while the Y-axis represents the vTEC in relation to TEC units.</p>
Full article ">Figure 14
<p>Relationship between TEC Pearson correlation and distance, showing spatial decay in correlation with statistical significance for the Nichinan Earthquake across stations for the anomaly day of 28 July 2024.</p>
Full article ">Figure 15
<p>Spatial distribution of vTEC based on data from 17 neighboring stations, depicted by green triangles, during the Peak Negative Anomaly time at 3.85 UTC on the anomaly day of 28 July 2024.</p>
Full article ">Figure A1
<p>Space weather conditions preceding the Nichinan earthquake of 8 August 2024.</p>
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28 pages, 5473 KiB  
Article
Sensitivity of Band-Pass Filtered In Situ Low-Earth Orbit and Ground-Based Ionosphere Observations to Lithosphere–Atmosphere–Ionosphere Coupling Over the Aegean Sea: Spectral Analysis of Two-Year Ionospheric Data Series
by Wojciech Jarmołowski, Anna Belehaki and Paweł Wielgosz
Sensors 2024, 24(23), 7795; https://doi.org/10.3390/s24237795 - 5 Dec 2024
Viewed by 579
Abstract
This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they [...] Read more.
This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes. Full article
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Figure 1
<p>Selection of Swarm B and C tracks in the last quarter of 2020 together with the epicenter of earthquakes that occurred at that time in the Aegean Sea and neighboring regions. The tectonic plate boundaries are also presented in this map.</p>
Full article ">Figure 2
<p>Example residual Swarm Ne data (<b>upper right</b>), example spectrogram of suspected co-seismic Ne disturbance detected by Swarm B (<b>lower right</b>), and Swarm PSD sampled at 35 s wave period with tectonic plate boundaries (<b>left</b>).</p>
Full article ">Figure 3
<p>Critical frequency of (<b>a</b>) F2 layer (foF2), (<b>b</b>) sporadic E layer (foEs), and (<b>c</b>) spread frequency (ff) from Athens Digisonde (black) and their 90-day trends estimated by DFT (red) in 2020/2021. Data gaps are also ignored in further correlation analysis.</p>
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<p>The VTEC interpolated from UQRG GIM near Athens (38° N and 24° E) (black) and its 90-day trend (red) in 2020/2021.</p>
Full article ">Figure 5
<p>Maxima of PSD of Swarm B Ne disturbances in (<b>a</b>) 2020 and (<b>b</b>) 2021 (arbitrary scaling, blue narrow line) together with earthquakes in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). Max PSD have calculated the 20-day moving average (blue bold line). The earthquakes have calculated an indicator of seismicity (black bold line). The Dst index is plotted as an orange line. The sunspot number is represented by a yellow area plot. The solar radio flux is shown as a red line. The time periods indicated with red horizontal lines cover earthquake groups presented geographically in <a href="#sensors-24-07795-f007" class="html-fig">Figure 7</a>. Green horizontal lines denote periods of higher seismicity.</p>
Full article ">Figure 6
<p>Maxima of PSD of Swarm C Ne disturbances in (<b>a</b>) 2020 and (<b>b</b>) 2021 (arbitrary scaling, blue narrow line) together with earthquakes in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). Max PSD have calculated the 20-day moving average (blue bold line). The earthquakes have calculated an indicator of seismicity (black bold line). The Dst index is plotted as an orange line. The sunspot number is represented by a yellow area plot. The solar radio flux is shown as a red line. The time periods indicated with red horizontal lines cover earthquake groups presented geographically in <a href="#sensors-24-07795-f008" class="html-fig">Figure 8</a>. Green horizontal lines denote periods of higher seismicity.</p>
Full article ">Figure 7
<p>Geographical location of earthquakes occurring in selected periods in 2020 ((<b>a</b>–<b>f</b>) present earthquake groups indicated in <a href="#sensors-24-07795-f005" class="html-fig">Figure 5</a>a and <a href="#sensors-24-07795-f006" class="html-fig">Figure 6</a>a by red horizontal lines), together with Swarm B (green) and Swarm C (red) tracks at the same time.</p>
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<p>Geographical location of earthquakes occurring in selected periods in 2021 ((<b>a</b>–<b>f</b>) present earthquake groups indicated by red horizontal lines in <a href="#sensors-24-07795-f005" class="html-fig">Figure 5</a>b and <a href="#sensors-24-07795-f006" class="html-fig">Figure 6</a>b), together with Swarm B (green) and Swarm C (red) tracks at the same time.</p>
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<p>STFT analysis and band-pass filtering of foF2 parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. The foF2 is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of foEs parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. The foEs is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of ff parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (6–10 days) calculated using a 20-day window. The ff is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of VTEC interpolated near Athens from UQRG in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. VTEC is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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18 pages, 1578 KiB  
Review
The Generation of Seismogenic Anomalous Electric Fields in the Lower Atmosphere, and Its Application to Very-High-Frequency and Very-Low-Frequency/Low-Frequency Emissions: A Review
by Masashi Hayakawa, Yasuhide Hobara, Koichiro Michimoto and Alexander P. Nickolaenko
Atmosphere 2024, 15(10), 1173; https://doi.org/10.3390/atmos15101173 - 30 Sep 2024
Viewed by 705
Abstract
The purpose of this paper is, first of all, to review the previous works on the seismic (or earthquake (EQ)-related) direct current (DC) (or quasi-stationary) electric fields in the lower atmosphere, which is likely to be generated by the conductivity current flowing in [...] Read more.
The purpose of this paper is, first of all, to review the previous works on the seismic (or earthquake (EQ)-related) direct current (DC) (or quasi-stationary) electric fields in the lower atmosphere, which is likely to be generated by the conductivity current flowing in the closed atmosphere–ionosphere electric circuit during the preparation phase of an EQ. The current source is electromotive force (EMF) caused by upward convective transport and the gravitational sedimentation of radon and charged aerosols injected into the atmosphere by soil gasses during the course of the intensification of seismic processes. The theoretical calculations predict that pre-EQ DC electric field enhancement in the atmosphere can reach the breakdown value at the altitudes 2–6 km, suggesting the generation of a peculiar seismic-related thundercloud. Then, we propose to apply this theoretical inference to the observational results of seismogenic VHF (very high frequency) and VLF/LF (very low frequency/low frequency) natural radio emissions. The formation of such a peculiar layer initiates numerous chaotic electrical discharges within this region, leading to the generation of VHF electromagnetic radiation. Earlier works on VHF seismogenic radiation performed in Greece have been compared with the theoretical estimates, and showed a good agreement in the frequency range and intensity. The same idea can also be applied, for the first time, to seismogenic VLF/LF lightning discharges, which is completely the same mechanism with conventional cloud-to-ground lightning discharges. In fact, such seismogenic VLF/LF lightning discharges have been observed to appear before an EQ. So, we conclude in this review that both seismogenic VHF radiation and VLF/LF lightning discharges are regarded as indirect evidence of the generation of anomalous electric fields in the lowest atmosphere due to the emanation of radioactive radon and charged aerosols during the preparation phase of EQs. Finally, we have addressed the most fundamental issue of whether VHF and VLF/LF radiation reported in earlier works is either of atmospheric origin (as proposed in this paper) or of lithospheric origin as the result of microfracturing in the EQ fault region, which has long been hypothesized. This paper will raise a question regarding this hypothesis of lithospheric origin by proposing an alternative atmospheric origin outlined in this review. Also, the data on seismogenic electromagnetic radiation and its inference on perturbations in the lower atmosphere will be suggested to be extensively integrated in future lithosphere–atmosphere–ionosphere coupling (LAIC) studies. Full article
(This article belongs to the Section Upper Atmosphere)
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<p>Height (z) and spatial (r) dependence of the vertical component of the electric field amplitude E<sub>z</sub>(r,z) relative to its breakdown value E<sub>k</sub>(z). The following parameters are chosen in the computations: from the top to the bottom, (<b>a</b>) H<sub>c</sub> = 2 km, u<sub>0</sub> = 3.3 × 10<sup>−2</sup> m/s; (<b>b</b>) H<sub>c</sub> = 5 km, u<sub>0</sub> = 3.2 × 10<sup>−2</sup> m/s; and (<b>c</b>) H<sub>c</sub> = 6 km, u<sub>0</sub> = 3.2 × 10<sup>−2</sup> m/s. See the details in [<a href="#B74-atmosphere-15-01173" class="html-bibr">74</a>].</p>
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<p>The theoretical frequency spectrum of electromagnetic radiation at a distance of 300 km from the EQ epicenter (black curve), and the two vertical bars (at two frequencies of 41 and 53 MHz) denote the experimental data. See [<a href="#B73-atmosphere-15-01173" class="html-bibr">73</a>] for the details on the physical parameters used in the computation.</p>
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<p>(<b>a</b>) A conventional atmospheric lightning discharge (in the case of -CG lightning discharge), and (<b>b</b>) a peculiar seismogenic thundercloud due to pre-EQ activity (emanation of radon and charged aerosols), and the similar lightning discharge.</p>
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<p>The temporal evolution of seismogenic lightning discharges detected by a Taiwanese lightning network as observed for the 1999 Chi-chi EQ. Source: Tsai et al. (2006) [<a href="#B98-atmosphere-15-01173" class="html-bibr">98</a>].</p>
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12 pages, 3994 KiB  
Article
Possible Identification of Precursor ELF Signals on Recent EQs That Occurred Close to the Recording Station
by Ioannis Contopoulos, Janusz Mlynarczyk, Jerzy Kubisz and Vasilis Tritakis
Atmosphere 2024, 15(9), 1134; https://doi.org/10.3390/atmos15091134 - 19 Sep 2024
Viewed by 843
Abstract
The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such [...] Read more.
The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such as radon, through subterranean fissures that develop in the lead-up to the quake. This discharge augments the ionization at the lower atmospheric layers, instigating disturbances that extend from the Earth’s surface to the lower ionosphere. A critical component of the LAIC sequence involves the distinctive perturbations of Extremely Low Electromagnetic Frequencies (ELF) within the Schumann Resonances (SR) spectrum of 2 to 50 Hz, detectable days ahead of the seismic event. Our study examines 10 earthquakes that transpired over a span of 3.5 months—averaging nearly three quakes monthly—which concurrently generated 45 discernible potential precursor seismic signals. Notably, each earthquake originated in Southern Greece, within a radius of 30 to 250 km from the observatory on Mount Parnon. Our research seeks to resolve two important issues. The first concerns the association between specific ELF signals and individual earthquakes—a question of significant importance in seismogenic regions like Greece, where earthquakes occur frequently. The second inquiry concerns the parameters that determine the detectability of an earthquake by a given station, including the requisite proximity and magnitude. Initial findings suggest that SR signals can be reliably linked to a particular earthquake if the observatory is situated within the earthquake’s preparatory zone. Conversely, outside this zone, the correlation becomes indeterminate. Additionally, we observe a differentiation in SR signals based on whether the earthquake took place over land or offshore. The latter category exhibits unique signal behaviors, potentially attributable to the water layers above the epicenter acting as a barrier to the ascending gases, thereby affecting the atmospheric–ionospheric ionization process. Full article
(This article belongs to the Section Upper Atmosphere)
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<p>The Mediterranean Ridge and the Hellenic Trench around Crete.</p>
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<p>Typical ELF recordings 0–50 Hz, raw data (upper panels) and associated Fourier spectra (low panels), received by both the Greek (<b>left</b>) and the Polish (<b>right</b>) systems in our observing site on Mount Parnon. Schumann’s resonances around 7, 14, 21, and 28 Hz are evident in both spectra. PSD stands for “Power Spectral Density” in the Greek system recording (<b>left</b>). ASD stands for “Amplitude Spectral Density” in the Polish system recording (<b>right</b>). B stands for the magnetic field value (in pT). N–S denotes recordings from the North–South oriented coil.</p>
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<p>Typical strong perturbations in the raw data and spectra are considered potential precursor seismic signals. Simultaneous measurements were received by the Greek (blue) and the Polish (red) systems, respectively.</p>
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<p>Greek peninsula with the recording station of Parnon (red pin in the center of the circles) and the 10 EQs of <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a> (yellow pins) shown. The color circles delimit the preparation areas around the station if the EQ epicenter lies at the position of the station itself. The outer red circle corresponds to an EQ of 6 Richter while the inner blue circle corresponds to an EQ of 4 Richter. Overall, the shown circles delimit zones of EQs = 6, 5.5, 5.0, 4.5 and 4 Richter from the outer to the inner circle, respectively.</p>
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<p>The first (<b>left</b>) and the last (<b>right</b>) recorded potential precursor signals before the EQ at Leonidion (no.1 in <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a>). E–W denotes recordings from the East–West oriented coil.</p>
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<p>The last 3 successive signals before the 5.6 R EQ in the Kyparissia Gulf (no. 7 in <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a>). The spectrum of these signals differs from the spectrum of the signals in <a href="#atmosphere-15-01134-f005" class="html-fig">Figure 5</a> (they show two characteristic excesses at the boundaries of the normal precursor frequency area of 20–25 Hz).</p>
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18 pages, 937 KiB  
Article
Integrated Analysis of Multi-Parameter Precursors to the Fukushima Offshore Earthquake (Mj = 7.3) on 13 February 2021 and Lithosphere–Atmosphere–Ionosphere Coupling Channels
by Masashi Hayakawa and Yasuhide Hobara
Atmosphere 2024, 15(8), 1015; https://doi.org/10.3390/atmos15081015 - 21 Aug 2024
Cited by 2 | Viewed by 2295
Abstract
The preparation phase of earthquakes (EQs) has been investigated by making full use of multi-parameter and multi-layer observations of EQ precursors, in order to better understand the lithosphere–atmosphere–ionosphere coupling (LAIC) process. For this purpose, we chose a specific target EQ, the huge EQ [...] Read more.
The preparation phase of earthquakes (EQs) has been investigated by making full use of multi-parameter and multi-layer observations of EQ precursors, in order to better understand the lithosphere–atmosphere–ionosphere coupling (LAIC) process. For this purpose, we chose a specific target EQ, the huge EQ of Fukushima-ken-oki EQ on 13 February 2021 (magnitude Mj = 7.3). We initially reported on EQ precursors in different physical parameters not only of the lithosphere, but also of the atmosphere and ionosphere (Hayakawa et al. followed by Akhoondzadeh et al. and Draz et al., both based on satellite observations). Our first two papers dealt with seven electromagnetic precursors in the three layers (with emphasis on our own ground-based observations in the atmosphere and lower ionosphere), while the second paper dealt with Swarm satellite observations of magnetic field, electron density, and GPS TEC in the ionosphere, and the third paper dealt only with climatological parameters on and above the Earth’s surface (together with GPS TEC). We have extensively reviewed all of these results, and have coordinated the temporal evolutions of various physical parameters relevant to the LAIC system; we have sought to understand which hypothesis is more plausible in explaining the LAIC process. Then, we came to a conclusion that two possible LAIC channels seem to exist simultaneously for this EQ: a fast channel (nearly simultaneous responses on the ground and ionosphere), and a slow channel (or diffusion-type), with a time delay of a few to several days, in which the agent effects in the lithosphere and lowest atmosphere seem to propagate up to the ionosphere with a definite time delay. Finally, we have suggested some research directions for the future elucidation of LAIC channels, and also made some comments on an early EQ warning system. Full article
(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)
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<p>Location of the EQ on 13 February 2021, together with the ULF observatory at Kakioka (KAK), and two ULF/ELF observatories at Nakatsugawa (NAK) and Shinojima (SHI) [<a href="#B66-atmosphere-15-01015" class="html-bibr">66</a>]. The wide gray dashed lines refer to the positions of trenches, and the black thin dash–dot lines on the main land indicate the location of the Japanese main faults.</p>
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<p>Integrated plots of anomalies in different layers (lithosphere, atmosphere, and ionosphere) for the EQ (vertical red line) on 13 February 2021. Top panel indicates the temporal evolution of Dst (geomagnetic activity), the abscissa is the date, and lead time means the day relative to the day of EQ (− before and + after the EQ). Different sized triangles reflect the anomaly intensity. See the details of each anomaly in the text.</p>
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31 pages, 11063 KiB  
Article
The Preparation Phase of the 2023 Kahramanmaraş (Turkey) Major Earthquakes from a Multidisciplinary and Comparative Perspective
by Gianfranco Cianchini, Massimo Calcara, Angelo De Santis, Alessandro Piscini, Serena D’Arcangelo, Cristiano Fidani, Dario Sabbagh, Martina Orlando, Loredana Perrone, Saioa A. Campuzano, Mariagrazia De Caro, Adriano Nardi and Maurizio Soldani
Remote Sens. 2024, 16(15), 2766; https://doi.org/10.3390/rs16152766 - 29 Jul 2024
Cited by 4 | Viewed by 1012
Abstract
On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, [...] Read more.
On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, such a significant seismic activity is expected to cause anomalies across various observables, from the Earth’s surface to the ionosphere. This multidisciplinary study investigates the preparatory phase of these two major earthquakes by identifying potential precursors across the lithosphere, atmosphere, and ionosphere. Our comprehensive analysis successfully identified and collected various anomalies, revealing that their cumulative occurrence follows an accelerating trend, either exponential or power-law. Most anomalies appeared to progress from the lithosphere upward through the atmosphere to the ionosphere, suggesting a sequential chain of processes across these geospheres. Notably, some anomalies deviated from this overall trend, manifesting as oscillating variations. We propose that these anomalies support a two-way coupling model preceding major earthquakes, highlighting the potential role of fluid chemistry in facilitating these processes. Full article
(This article belongs to the Section Earth Observation for Emergency Management)
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Graphical abstract
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<p>Tectonic setting of the Aegean–Anatolian region: the black and red lines represent the major thrust and faults in region, respectively; the black arrows represent the motion of the Aegean and Anatolian blocks; the red and yellow triangles are the volcanoes (Reprinted with permission from Ref. [<a href="#B30-remotesensing-16-02766" class="html-bibr">30</a>]).</p>
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<p>Spatial EQ distribution of all available events from 1 January 2018 to 31 July 2023, constituting the catalogue that was obtained without imposing any selection criteria. The clustering of events in green (mostly aftershocks) along the two branches in the EAFZ, where the large EQ doublet occurred (red stars), put in evidence the fault segments which activated on February 6, accompanied by the three larger aftershocks of the sequence (magenta circle).</p>
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<p>Geomagnetic indices (ap and Dst) from 25 January to 8 February 2023. Red horizontal lines highlight the veto thresholds applied for the ionospheric data analyses. Magenta dashed vertical line corresponds to the 6 February mainshock origin time.</p>
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<p>The two kinds of electron loss phenomena, the electron burst inside the red square and several extended electron precipitations around the SAA as viewed by NOAA-19 on 31 January 2023.</p>
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<p>Seismicity map for the analysed time period, together with the main fault systems of the investigated area. The yellow star represents an earthquake (M5.5) that occurred on 4 August 2020.</p>
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<p><span class="html-italic">Mc</span> calculated using the maximum likelihood method with a 95% confidence.</p>
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<p>Temporal variations of the <span class="html-italic">b</span>-value parameter of the G–R law based on the likelihood method.</p>
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<p>(<b>a</b>) Map of the spatial distribution of the events, which contributed to the acceleration characterising the sequence of foreshocks; (<b>b</b>) the R-AMR analysis: red symbols represents those events occurring within the inner circle with R0 = 50 km radius (representing the fault area); the blue and the green dots, in the upper and lower insets, respectively, are the events outside the inner circle, i.e., those occurring in the external damped region. The analysis evidenced the appearance of acceleration (C-value = 0.615). Noteworthy is the resulting time to failure <span class="html-italic">t<sub>f</sub></span> (1.7 days), i.e., almost 2 days after the real mainshock occurrence.</p>
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<p>Analysis of the SKT parameter for Turkey EQ with comparison between the 2023 time series (dashed red line) and the historical time series (1980–2022, blue line). No anomalies were evidenced. In each legend, std stands for standard deviation; 1·std, 1.5·std, 2·std mean one, one and half and two standard deviations, respectively.</p>
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<p>Analysis of the OLR parameter for Turkey EQ with comparison between the 2023 time series (dashed red line) and the historical time series (1980–2022, blue line). Evidenced by the red circle, there is one anomalous value with a persistence of 2 days referred to 21 January 2023 (i.e., 15 and 16 days before the mainshock). In each legend, std stands for standard deviation; 1·std, 1.5·std, 2·std mean one, one and half and two standard deviations, respectively.</p>
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<p>Map of the OLR anomalous day (21 January) in terms of difference with respect to the historical mean. The epicentre is indicated by the central star. The darker red part of the region could be related to a pre-earthquake OLR anomaly.</p>
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<p>Sulphur dioxide (SO<sub>2</sub>) concentration in the four months before the mainshock (dashed red line) compared with the historical time series of the previous almost 19 years. The blue line is the historical mean, while the coloured bands present the 1 (light blue), 1.5 (green), and 2 (yellow) standard deviations. The red circle indicates an anomaly that emerges clearly from the 2σ background. Bottom: main anomaly maps form 2-day persistent signals.</p>
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<p>Carbon dioxide (CO<sub>2</sub>) concentration in the four months before the mainshock (dashed red line) compared with the historical time series of the previous almost 19 years. The blue line is the historical mean, while the coloured bands present the 1 (light blue), 1.5 (green), and 2 (yellow) standard deviations. The red circle indicates an anomaly that emerges clearly from the 2σ background. Bottom: main anomaly maps form 3-day and 3-day persistent signals, respectively.</p>
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<p>Carbon monoxide (CO) concentration in the four months before the mainshock compared (dashed red line) with the historical time series of the previous almost 19 years. The blue line is the historical mean, while the coloured bands present the 1 (light blue), 1.5 (green), and 2 (yellow) standard deviations. The red circle indicates an anomaly that emerges clearly from the 2σ background. Bottom: main anomaly maps form 11-day and 9-day persistent signals, respectively.</p>
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<p>Swarm-A (from left to right panels) first differences (MASS method for magnetic data) of: X, Y, Z, and F; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle; [<a href="#B63-remotesensing-16-02766" class="html-bibr">63</a>]), and analysed track (red line) for 26 January 2023 (i.e., 11 days before the earthquake). Coloured squares indicate the anomalies automatically detected by MASS.</p>
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<p>Swarm-C (from left to right panels): electron density data with respect to geomagnetic latitude; first differences (NeAD method for Ne data); electron temperature data with respect to geomagnetic latitude; plasma potential data with respect to geomagnetic latitude; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 27 January 2023 (10 days before the earthquake). Coloured square indicates the anomaly automatically detected by NeAD.</p>
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<p>Swarm-A (from left to right panels) first differences (MASS method for magnetic data) of: X, Y, Z, and F; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 29 August 2017 (i.e., 1987 days before the earthquake). Coloured squares indicate the anomalies automatically detected by MASS.</p>
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<p>CSES-01 (from left to right panels): electron density data with respect to geomagnetic latitude; first differences (NeAD method for Ne data); electron density data with respect to geographic latitude; electron density data with respect to time in hours; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 15 December 2022 (i.e., 53 days before the earthquake). Coloured squares indicate the anomalies automatically detected by NeAD. In this case this anomaly is an artefact due to the jumps seen around 20°–10° geomagnetic and geographic latitudes, and it is outside of the Dobrovolsky area.</p>
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<p>Swarm Alpha (<b>a</b>) and Bravo (<b>b</b>) (from left to right panels): electron density data with respect to geomagnetic latitude; first differences (NeAD method for Ne data); electron temperature data with respect to geomagnetic latitude; plasma potential data with respect to geomagnetic latitude; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 15 December 2022 (i.e., 53 days before the earthquake). Coloured square indicates the anomaly automatically detected by NeAD.</p>
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<p>The anomaly observed on 20 November 2022 by using Δh’Es, δfbEs, and δfoF2 variations.</p>
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<p>Ionosonde anomaly on 20 November 2022 likely associated to the 6 February 2023 Mw7.8 Turkey EQ (indicated by the black arrow), compared to the relationship between ∆<span class="html-italic">T</span>·<span class="html-italic">R</span> and <span class="html-italic">M</span> previously found by the analysis of 33 M6+ EQs in the vicinity of Japan during the years 1985–2000 [<a href="#B78-remotesensing-16-02766" class="html-bibr">78</a>] (solid line).</p>
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<p>Electron bursts were detected by (<b>a</b>) NOAA-18, (<b>b</b>) NOAA-19, and (<b>c</b>) MetOp-01 on 28 January 2023.</p>
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<p>A significant and extensive electron loss phenomenon was observed by MetOp-01 on 7 February 2023.</p>
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<p>Comprehensive cumulative number of all anomalies. The red curve represents the best exponential fit, while the black curve is the best power-law fit. The type of anomaly is also indicated in correspondence with each point.</p>
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<p>The time markers of the appearance of the anomalies: ordered in abscissa with time to the EQ origin time (<span class="html-italic">t<sub>0</sub></span>) (magenta dashed line); and with the altitude in ordinate.</p>
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16 pages, 6069 KiB  
Article
Analysis of Ionospheric Anomalies before Earthquakes of Mw6.5 and above in Japan from 2011 to 2022
by Zhen Li, Zhen Tao and Lianhai Cao
Atmosphere 2024, 15(8), 887; https://doi.org/10.3390/atmos15080887 - 25 Jul 2024
Cited by 1 | Viewed by 1178
Abstract
In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. [...] Read more.
In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes. Full article
(This article belongs to the Section Planetary Atmospheres)
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<p>Geographical distribution of 20 earthquakes in Japan from 2011 to 2022. The red dots represent the epicenter of Mw7.0–7.3 earthquakes, and the green dots represent the epicenter of Mw6.5–6.9 earthquakes.</p>
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<p>TEC periodic spectrum analysis in 2005. (<b>a</b>) TEC time series; (<b>b</b>) TEC Morlet wavelet analysis power spectrum; (<b>c</b>) TEC overall periodic spectrum (The dashed blue line represents the 95% significance test curve).</p>
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<p>Sunspot (SSN) changes for Mw6.5+ earthquakes in Japan from 2011 to 2022.</p>
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<p>Changes in 10.7 cm radio flux (F10.7) for Mw6.5+ earthquakes in Japan from 2011 to 2022.</p>
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<p>Changes in total solar radiation (TSI) for Mw6.5+ earthquakes in Japan from 2011 to 2022.</p>
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<p>Variation in solar wind velocity (Vsw) for Mw6.5+ earthquakes in Japan from 2011 to 2022.</p>
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<p>Changes in the global geomagnetic activity index (KP) of Mw6.5+ earthquakes in Japan from 2011 to 2022.</p>
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<p>Changes in the geomagnetic activity index of the equatorial region (DST) of Mw6.5 + earthquakes in Japan from 2011 to 2022 (The red and blue areas respectively indicate positive and negative anomalies).</p>
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<p>TEC changes in Mw6.5+ earthquakes in Japan from 2011 to 2022 (The red and blue areas respectively indicate positive and negative anomalies. The green areas represent anomalies after excluding geomagnetic and solar activity).</p>
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<p>TEC anomaly amplitude at the epicenter before the earthquake. The colors represent earthquake magnitudes, with blue (earthquakes 2, 4, 5, 6, and 7) representing magnitudes 6.5 to 6.7, green (earthquakes 9, 11, 12, and 13) representing magnitudes 6.8–6.9, and red (earthquakes 14, 15, 16, and 20) representing magnitudes 7.0–7.3.</p>
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<p>TEC anomaly occurrence time before each earthquake (UTC).</p>
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<p>Solar and geomagnetic activity from 14 February to 18 March 2022 (UTC). (<b>a</b>) SSN time series; (<b>b</b>) F10.7 time series; (<b>c</b>) TSI time series; (<b>d</b>) Vsw time series; (<b>e</b>) DST time series; (<b>f</b>) KP time series. The thick green line represents the time when the earthquake occurred, and the thin green line represents the time when the ionospheric anomaly occurred.</p>
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<p>Global ionospheric changes from 00:00 to 7:00 on 15 February 2022; (<b>a</b>) UTC 00:00; (<b>b</b>) UTC 01:00; (<b>c</b>) UTC 02:00; (<b>d</b>) 03:00 UTC; (<b>e</b>) 04:00 UTC; (<b>f</b>) 05:00 UTC; (<b>g</b>) 06:00 UTC; (<b>h</b>) 07:00 UTC. The red five-pointed star represents the sky above the epicenter, and the black curve represents the magnetic equatorial line.</p>
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<p>Action flowchart of the lithosphere–atmosphere–ionospheric coupling (LAIC) model.</p>
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20 pages, 9973 KiB  
Article
The Preparation Phase of the 2022 ML 5.7 Offshore Fano (Italy) Earthquake: A Multiparametric–Multilayer Approach
by Martina Orlando, Angelo De Santis, Mariagrazia De Caro, Loredana Perrone, Saioa A. Campuzano, Gianfranco Cianchini, Alessandro Piscini, Serena D’Arcangelo, Massimo Calcara, Cristiano Fidani, Adriano Nardi, Dario Sabbagh and Maurizio Soldani
Geosciences 2024, 14(7), 191; https://doi.org/10.3390/geosciences14070191 - 16 Jul 2024
Viewed by 1016
Abstract
This paper presents an analysis of anomalies detected during the preparatory phase of the 9 November 2022 ML = 5.7 earthquake, occurring approximately 30 km off the coast of the Marche region in the Adriatic Sea (Italy). It was the largest earthquake [...] Read more.
This paper presents an analysis of anomalies detected during the preparatory phase of the 9 November 2022 ML = 5.7 earthquake, occurring approximately 30 km off the coast of the Marche region in the Adriatic Sea (Italy). It was the largest earthquake in Italy in the last 5 years. According to lithosphere–atmosphere–ionosphere coupling (LAIC) models, such earthquake could induce anomalies in various observable variables, from the Earth’s surface to the ionosphere. Therefore, a multiparametric and multilayer approach based on ground and satellite data collected in each geolayer was adopted. This included the revised accelerated moment release method, the identification of anomalies in atmospheric parameters, such as Skin Temperature and Outgoing Longwave Radiation, and ionospheric signals, such as Es and F2 layer parameters from ionosonde measurements, magnetic field from Swarm satellites, and energetic electron precipitations from NOAA satellites. Several anomalies were detected in the days preceding the earthquake, revealing that their cumulative occurrence follows an exponential trend from the ground, progressing towards the upper atmosphere and the ionosphere. This progression of anomalies through different geolayers cannot simply be attributed to chance and is likely associated with the preparation phase of this earthquake, supporting the LAIC approach. Full article
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<p>Summary map of the study conducted: the different layers analyzed are observed, from bottom to top. For each layer, the types of parameters considered are indicated.</p>
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<p>Simplified structural map of Italy, with the epicenter of the 9 November 2022 Fano EQ indicated by a small yellow star (modified from [<a href="#B40-geosciences-14-00191" class="html-bibr">40</a>]).</p>
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<p>Seismotectonic framework of the coastal area of the Marche region. The light blue squares represent the seismic sequence from 9 November 2022 to 14 February 2023; the first event is marked with a yellow star and the second event is shown with a green star. Historical and instrumental earthquakes from CPTI15 [<a href="#B45-geosciences-14-00191" class="html-bibr">45</a>] are indicated with colored squares, with earthquakes of Mw ≥ 5.5 highlighted in red. The surface projections of seismogenic zones are depicted with orange ribbons [<a href="#B41-geosciences-14-00191" class="html-bibr">41</a>]. The focal mechanisms of the 9 November 2022 earthquake and the event of 30 October 1930, represented by the grey and white balls, come from TDMT (Time Domain Moment Tensor) and Vannoli et al. [<a href="#B46-geosciences-14-00191" class="html-bibr">46</a>], respectively (modified from [<a href="#B30-geosciences-14-00191" class="html-bibr">30</a>]).</p>
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<p>Spatial distribution of the 174.723 events extracted from the INGV Catalog during the period 2012–2022 within a circular radius of 150 km from the epicenter of the main EQ, highlighted by the yellow star. The grey and white sphere represents the focal mechanism of the earthquake on 9 November 2022. The chosen radius includes the Central Italy sequence (2016), identifiable by the cluster of events to the south near the edge of the area.</p>
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<p>Spatial distribution of ten years of seismicity around the mainshock, in a radius of 150 km from the epicenter (largest green circle). Blue and red dots (confined within blue and red circles, respectively) represent the events contributing to the acceleration found by the R-AMR analysis. The red dots are the events closer to the seismogenic fault.</p>
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<p>Scenario of hypothesized pre-EQ coupling processes between the lithosphere of Central Italy and areas where possible EBs could be detected by LEO satellites.</p>
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<p>Outcome of the R-AMR algorithm applied to the extracted seismic dataset. The red points represent EQs that are closer to the fault (within 37 km) than those represented by the blue points. At the bottom of the main figure, the magnitudes of the involved events are represented: red is used for EQs falling within 37 km from the fault and green those outside that limit.</p>
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<p>Analysis of the SKT parameter for the Fano EQ with comparison between the 2022 time series (dashed red line) and the historical time series (1980–2021, blue line). Evidenced by red circles there are two quite anomalous values near the second standard deviations from the mean: the first one refers to 18 August, and the second one to 15 September.</p>
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<p>Maps of the SKT anomalous days in terms of difference with respect to the historical mean: (<b>a</b>) 18 August; (<b>b</b>) 15 September. The EQ epicenter is indicated by the central star. SKT is defined only on land.</p>
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<p>Analysis of the OLR for the Fano EQ with the identification of two anomalous days that exceed the historical average calculated from 1980 to 2021 by two standard deviations.</p>
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<p>Maps of OLR anomalous days maps in terms of difference with respect to the historical mean: (<b>a</b>) 5 September; (<b>b</b>) 12 September. The epicenter is indicated by the central star.</p>
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<p>The anomaly observed 9 days before the 9 November 2022 Fano EQ using Δh’Es, δfbEs, and δfoF2 variations, along with 3 h Kp index values given as a reference of geomagnetic activity.</p>
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<p>Ionosonde anomaly for the 9 November 2022 M5.7 Fano EQ (red square), compared to the relationship between ∆T·R and M previously found by the analysis of the most powerful Central Italian EQs since 1984 (red line and black squares).</p>
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<p>Anomalies found 4 days after (<b>a</b>) and 75 days before the Fano EQ (<b>b</b>) by means of an automatic search for magnetic anomalies 90 days before and 10 days after the EQ; MASS algorithm (kt = 2.5) applied to Swarm A satellite. The anomalies are evidenced by coloured rectangles. The vertical red line on the geographical map represents the satellite track.</p>
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<p>Three-dimensional representation of the NOAA-15 semi-orbits on 8 November 2022; EB evidenced by a red circle while the star identified the EQ epicenter.</p>
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<p>In a comprehensive approach of the anomalies, the cumulative number of anomalies for Fano EQ is shown here. It is possible to notice that the anomalies appear in time mostly from below (seismic data in the lithosphere) to above (atmosphere and ionosphere). The red curve is an exponential fit of the data.</p>
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32 pages, 23008 KiB  
Article
Pre-Earthquake Oscillating and Accelerating Patterns in the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) before the 2022 Luding (China) Ms6.8 Earthquake
by Xuemin Zhang, Angelo De Santis, Jing Liu, Saioa A. Campuzano, Na Yang, Gianfranco Cianchini, Xinyan Ouyang, Serena D’Arcangelo, Muping Yang, Mariagrazia De Caro, Xinyan Li, Cristiano Fidani, Hong Liu, Martina Orlando, Lei Nie, Loredana Perrone, Alessandro Piscini, Lei Dong, Dario Sabbagh, Maurizio Soldani and Pan Xiongadd Show full author list remove Hide full author list
Remote Sens. 2024, 16(13), 2381; https://doi.org/10.3390/rs16132381 - 28 Jun 2024
Cited by 5 | Viewed by 955
Abstract
The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the [...] Read more.
The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the b-value, revised accelerated moment release, Earth resistivity, ELF magnetic field emissions, atmospheric electric field, surface temperature, foF2 from ionosonde, GNSS TEC, Ne and magnetic field from CSES and Swarm satellites, and energetic electrons from CSES and NOAA satellites. It is found that the anomalies start from the lithospheric parameters as Earth resistivity and b-values 1–2 years before to reflect the local stress loading in the seismic region, then the ionospheric and atmospheric disturbances occur and accelerate −50 days before and −15 days before, and finally the electrons precipitate a few days before. The simultaneous perturbations in LAI illustrate the thermodynamic coupling channel, such as on 24 August, −12 days before. Meanwhile, the abundant developed ionospheric anomalies without atmospheric disturbances demonstrate the electromagnetic coupling way from the lithosphere to the ionosphere directly. Finally, the results demonstrate a two-way model of LAIC: one way is characterized by a slow chain of processes, of thermodynamic nature, starting from the ground and proceeding to the above atmosphere and ionosphere, showing an exponential trend in the cumulative number of anomalies; the second way is characterized by oscillating electromagnetic coupling between the lithosphere and ionosphere, showing intermittent fluctuations in the corresponding cumulative number of anomalies. Full article
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<p>Temporal variation in the b-value from 1 January 2012 to 28 October 2023. The continuous vertical red bar identifies the day on which the Luding earthquake occurred; the dashed red bar marks the beginning of the b-value decrease.</p>
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<p>R-AMR algorithm applied to the seismic catalogue. The red curve and the black line are, respectively, the power-law and linear fits of s(t). The vertical dashed green line is the origin time of the mainshock. The lower inset is the time distribution of the catalogue contributing to acceleration: the red dots are the events occurring within the inner circle with R<sub>0</sub> = 33 km radius, representing the fault area; the green dots are the events outside the inner circle, i.e., those occurring in the external damped region (circular annulus with R<sub>1</sub> = 140 km radius).</p>
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<p>The spatial distribution of the seismic events which contributed to the acceleration found by the R-AMR method. The fault is modeled as a circular area with a radius equal to the theoretical length of the rupture, which scales with the magnitude—33 km in this case according to Wells and Coppersmith (1994) [<a href="#B55-remotesensing-16-02381" class="html-bibr">55</a>]. Differently from the events represented by the red dots, all the events (blue) are those events with a damping function of the distance from the fault.</p>
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<p>The Earth resistivity observations at four stations around Luding earthquake during 2018–2022 ((<b>a</b>,<b>b</b>) for Ganzi station; (<b>c</b>,<b>d</b>) for Jiangyou; (<b>e</b>,<b>f</b>) for Mianning; (<b>g</b>,<b>h</b>) for Hongge station; the epicentral distances were marked in brackets below the earthquake’s name and magnitude with red vertical arrows; the red lines indicate the long-term trend and double directional red arrows present the duration for annual variation).</p>
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<p>The comparison of resistivity (red line) and precipitation (blue histogram) in 2022 at 3 stations (yellow rectangle marks the quick decrease during March and April).</p>
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<p>The series of ELF magnetic field abnormal signals at 8 stations around Luding earthquake from 1 May to 13 September 2022 (three earthquakes are marked as Lushan on 1 June, Maerkang on 10 June, and Luding on 5 September with vertical red dotted lines; black lines indicate the increase trend at different time period).</p>
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<p>The AEF observations at multiple stations ((<b>a</b>–<b>e</b>) AEF curves at Yanzigou, Caoke, Xinglong, Guzan, Ya’an) and related meteorological factors ((<b>f</b>) precipitation; (<b>g</b>) visibility, the dotted blue line indicates its threshold, which means that when the values are lower than it, the AEF observations will be affected by this factor, so it cannot be defined as an anomaly related to earthquakes; (<b>h</b>) the maximum wind speed with its threshold by red dotted line where this factor cannot exceed it; (<b>i</b>) humidity with threshold by red dotted line where this dactor cannot exceed it) in Luding on 15 August 2022 (the yellow colors point out the AEF anomalies and the time period with the green color may be related to strong wind with wind speed greater than 8 m/s).</p>
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<p>The statistical analysis on the stations with AEF anomalies from 1 August to 5 September 2022. (<b>a</b>) The occurrence date of anomalies at each station with the distance to the epicenter (the blue square represent the anomalies occurred at each station); (<b>b</b>) the total number of abnormal stations with blue columns on each day; (<b>c</b>) the percentage of stations with AEF anomalies to the total studied ones. In (<b>b</b>,<b>c</b>) the horizontal dashed lines indicate the levels of 1 or 2 standard deviations.</p>
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<p>Analysis of the SKT and OLR parameters for Luding Ms6.8 earthquake with comparison between the 2022 time series (dashed red line) and the historical time series (1980–2021, blue line). Each red oval puts in evidence the identified anomalies that are characterized by a 2-day nighttime persistence with values surpassing two standard deviations with respect to the historical mean. Colored stripes indicate 1.0 σ (light green), 1.5 σ (green), and 2.0 σ (yellow) from the mean of the historical time series, respectively (please note that in the legend of the figures on the left, “std” means standard deviation σ, therefore “1*std”, “1.5*std” and “2*std” stand for “1.0 σ”, “1.5 σ” and “2.0 σ”, respectively). The earthquake occurred at the end of the analyzed period (red vertical line). On the right, the corresponding anomalous day spatial maps are depicted (the one on 24 August for SKT and on 7 July 2022 for OLR). The epicenter is indicated by a star in the center of the map; as background, the system of faults is shown with grey lines.</p>
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<p>The GPS TEC time series analysis during 22–28 August 2022 ((<b>a</b>) the red rectangle indicates the anomaly in TEC on 26 August) and the spatial distribution of <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">E</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math> in China on 26 August 2022 (<b>b</b>).</p>
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<p>The GIM TEC at UT06-10 during 24 and 26 August 2022 ((<b>a</b>) the epicenter has a red star, and the conjugate has a blue star, where one is Luding earthquake at northern hemisphere, and one is Indonesian earthquake at southern hemisphere) and the <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>T</mi> <mi>E</mi> <mi>C</mi> </mrow> </semantics></math> from 1 July to 11 September 2022 at longitude scale of 100–105°E ((<b>b</b>) the earthquakes occurring at the conjugate regions were all marked with related information; gray color illustrates the days affected by solar or geomagnetic activities).</p>
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<p>The distribution of Ne observed by CSES in the daytime on 23 August 2022. The panels from top to bottom are as follows: the Ne observation, the 15-day background, and the relative change, respectively. The red and blue stars represent the epicenter and its conjugate point, respectively.</p>
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<p>The anomalies in the three components of geomagnetic field at 250 Hz (±50 Hz) from SCM onboard CSES during nighttime in 1−30 July (<b>top</b>) and 10 August to 9 September (<b>bottom</b>) 2022. At the top of each series, the magnetic indices Kp and Dst are shown. Vertical red line is the time of earthquake occurrence.</p>
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<p>The ionospheric anomaly on 3 July 2022 (<b>a</b>) and 13 July 2022 (<b>b</b>) using observed Δh’Es, δfoEs, and δfoF2 variations (arrows) (please note that the time is in LT).</p>
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<p>Swarm C (from left to right panels) first differences (MASS method for magnetic data) of X, Y, Z, F; and map with the location of the epicenter of the earthquake (green star), Dobrovolsky area (yellow circle), and analyzed track (red line) for 3 July 2022 (i.e., 64 days before the earthquake). Colored squares indicate the anomalous windows automatically detected by MASS.</p>
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<p>Electron CRs detected by NOAA-18 on 2 September 2022. Three electron bursts (indicated by red circles) are evidenced: two are east and one south of the earthquake’s epicentre, as indicated by a red star.</p>
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<p>Electron losses detected from 18 August to 11 September 2022, reported on the magnetic latitude, are indicated by green circles. Electron losses detected by CSES are indicated in yellow. Earthquake day and corresponding magnetic latitude are indicated by a red star.</p>
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<p>Extended electron losses detected on 2 September 2022 by CSES are reported on the geographic latitudes and longitudes. Electron fluxes are represented in different colors depending on the direction of the telescopes. The electron fluxes detected east of the Luding epicenter (red star) from telescope 9 are in brown. The earthquake epicenter is indicated by a red star.</p>
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<p>(<b>a</b>) Cumulative number of anomalies and their progression in time from about 300 days to the occurrence of the earthquake. An exponential fit (red line) is also shown. In the small insert, the curve and the fit are shown for the largest interval of time. The curve is characterized by the exponential trend together with some fluctuations. (<b>b</b>) As in (<b>a</b>) but removing those ionospheric anomalies appearing before the atmospheric ones. Almost all fluctuations, appearing in (<b>a</b>), disappear.</p>
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<p>The temporal development of different parameters from atmosphere (bottom of figure) to ionosphere (top of figure) within 90 days before Luding earthquake.</p>
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<p>The coupling process among lithosphere–atmosphere–ionosphere on 24 August through different parameters (In (<b>c</b>): red circle is the Luding epicenter; red stars are AEF stations with anomalies; green triangle is Xichang station with ULF geomagnetic observation; and blue triangle is Yingjing station with ELF magnetic observation).</p>
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<p>Two-way model of coupling. On the left, a sketch of the different couplings; on the right, the plot in time showing two different couplings, one direct (red) and another delayed (blue). For convenience, we indicated the former as occurring at the same time of the beginning of the delayed one, but it can appear any time after the appearance of the first lithospheric precursor.</p>
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22 pages, 18622 KiB  
Article
Spatio–Temporal Evolution of Electric Field, Magnetic Field and Thermal Infrared Remote Sensing Associated with the 2021 Mw7.3 Maduo Earthquake in China
by Muping Yang, Xuemin Zhang, Meijiao Zhong, Yufan Guo, Geng Qian, Jiang Liu, Chao Yuan, Zihao Li, Shuting Wang, Lina Zhai, Tongxia Li and Xuhui Shen
Atmosphere 2024, 15(7), 770; https://doi.org/10.3390/atmos15070770 - 27 Jun 2024
Cited by 1 | Viewed by 815
Abstract
This study presents the spatio–temporal evolution of the electric and magnetic fields recorded by the China Seismo–Electromagnetic Satellite (CSES) and the thermal infrared remote sensing data observed by the Chinese stationary meteorological satellites Feng Yun–2G (FY–2G) associated with the 2021 Mw7.3 Maduo earthquake. [...] Read more.
This study presents the spatio–temporal evolution of the electric and magnetic fields recorded by the China Seismo–Electromagnetic Satellite (CSES) and the thermal infrared remote sensing data observed by the Chinese stationary meteorological satellites Feng Yun–2G (FY–2G) associated with the 2021 Mw7.3 Maduo earthquake. Specifically, we analyzed the power spectrum density (PSD) data of the electric field in the extremely low frequency (ELF) band, the geomagnetic east–west vector data, and the temperature of brightness blackbody (TBB) data to investigate the spatio–temporal evolution characteristics under quiet space weather conditions (Dst > −30 nT and Kp < 3). Results showed that (1) the TBB radiation began to increase notably along the northern fault of the epicenter ~1.5 months prior to the occurrence of the earthquake. It achieved its maximum intensity on 17 May, and the earthquake occurred as the anomalies decreased. (2) The PSD in the 371 Hz–500 Hz and 700 Hz–871 Hz bands exhibited anomaly perturbations near the epicenter and its magnetic conjugate area on May 17, with particularly notable perturbations observed in the latter. The anomaly perturbations began to occur ~1 month before the earthquake, and the earthquake occurred as the anomalies decreased. (3) Both the magnetic –east–west component vector data and the ion velocity Vx data exhibited anomaly perturbations near the epicenter and the magnetic conjugate area on 11 May and 16 May. (4) The anomaly perturbations in the thermal infrared TBB data, CSES electric field, and magnetic field data all occurred within a consistent perturbation time period and spatial proximity. We also conducted an investigation into the timing, location, and potential causes of the anomaly perturbations using the Vx ion velocity data with magnetic field –east–west component vector data, as well as the horizontal –north–south and vertical component PSD data of the electric field with the magnetic field –east–west component vector data. There may be both chemical and electromagnetic wave propagation models for the “lithosphere—atmosphere—ionosphere” coupling (LAIC) mechanism of the Maduo earthquake. Full article
(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)
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<p>The electric field three–component PSD in the ELF band of Orbit No. 179291 passing near the Mw7.3 Maduo epicenter on 26 April 2021 (the red vertical line indicates the epicenter).</p>
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<p>The major active faults and the Maduo earthquake in the region (80° E–120° E; 12° N–52° N) (<span class="html-fig-inline" id="atmosphere-15-00770-i001"><img alt="Atmosphere 15 00770 i001" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i001.png"/></span>: epicenter).</p>
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<p>The temperature of brightness blackbody (TBB) anomalies during the period from 1 April 2021 to 6 June 2021. (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter, the black lines are faults).</p>
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<p>The horizontal –north–south component PSD in the 371 Hz–500 Hz band detected by the EFD payload (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The horizontal –east–west component PSD in the 371 Hz–500 Hz band detected by the EFD payload (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The vertical component PSD in the 371 Hz–500 Hz band detected by the EFD payload (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spatio–temporal evolution of the perturbation amplitude θ in the horizontal –north–south component of the electric field within the 371 Hz–500 Hz band from 30 March 2021 to 31 May 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spatio–temporal evolution of the perturbation amplitude θ in the vertical component of the electric field within the 371 Hz–500 Hz band from 30 March 2021 to 31 May 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spatio–temporal evolution of the perturbation amplitude θ in the horizontal –north–south component of the electric field within the 700 Hz–871 Hz band from 30 March 2021 to 31 May 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spatio–temporal evolution of the perturbation amplitude θ in the vertical component of the electric field within the 700 Hz–871 Hz band from 30 March 2021 to 31 May 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spatio–temporal evolution of the perturbation amplitude θ in the vertical component of the electric field within the 700 Hz–871 Hz band from 2 April 2021 to 3 June 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>Two groups of CSES up–orbit trajectories near the 2021 Mw 7.3 Maduo earthquake from 1 April to 15 June (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter).</p>
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<p>The spectral time series of the horizontal –east–west component associated with the 2021 Mw 7.3 Maduo earthquake from 1 April to 11 June (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter, the sets of red dashed lines are the latitudes of the epicenters and the magnetic conjugate region for the 2021 Yangbi Ms6.4 earthquake and the 2021 Maduo Mw7.3 earthquake, the two pentagrams are the epicenters of the two earthquakes).</p>
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<p>The time series variation of the ion velocity Vx along the CSES flight direction from 1 April 2021 to 15 June 2021 (<span class="html-fig-inline" id="atmosphere-15-00770-i002"><img alt="Atmosphere 15 00770 i002" src="/atmosphere/atmosphere-15-00770/article_deploy/html/images/atmosphere-15-00770-i002.png"/></span>: epicenter, the sets of red dashed lines are the latitude of the epicenters and the magnetic conjugate region for the 2021 Yangbi Ms6.4 earthquake and the 2021 Maduo Mw7.3 earthquake. The two pentagrams are the epicenters of the two earthquakes).</p>
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29 pages, 13747 KiB  
Article
Observation of the Preparation Phase Associated with Mw = 7.2 Haiti Earthquake on 14 August 2021 from a Geophysical Data Point of View
by Dedalo Marchetti
Geosciences 2024, 14(4), 96; https://doi.org/10.3390/geosciences14040096 - 30 Mar 2024
Cited by 2 | Viewed by 2014
Abstract
On 14 August 2021, an earthquake of moment magnitude Mw = 7.2 hit Haiti Island. Unfortunately, it caused several victims and economic damage to the island. While predicting earthquakes is still challenging and has not yet been achieved, studying the preparation phase of [...] Read more.
On 14 August 2021, an earthquake of moment magnitude Mw = 7.2 hit Haiti Island. Unfortunately, it caused several victims and economic damage to the island. While predicting earthquakes is still challenging and has not yet been achieved, studying the preparation phase of such catastrophic events may improve our knowledge and pose the basis for future predictions of earthquakes. In this paper, the six months that preceded the Haiti earthquake are analysed, investigating the lithosphere (by seismic catalogue), atmosphere (by climatological archive) and ionosphere by China Seismo-Electromagnetic Satellite (CSES-01) and Swarm satellites, as well as Total Electron Content (TEC) data. Several anomalies have been extracted from the analysed parameters using different techniques. A comparison, especially between the different layers, could increase or decrease the probability that a specific group of anomalies may be (or not) related to the preparation phase of the Haiti 2021 earthquake. In particular, two possible coupling processes have been revealed as part of the earthquake preparation phase. The first one was only between the lithosphere and the atmosphere about 130 days before the mainshock. The second one was about two months before the seismic event. It is exciting to underline that all the geo-layers show anomalies at that time: seismic accumulation of stress showed an increase of its slope, several atmospheric quantities underline abnormal atmospheric conditions, and CSES-01 Ne depicted two consecutive days of ionospheric electron density. This suggested a possible coupling of lithosphere–atmosphere and ionosphere as a sign of the increased stress, i.e., the impending earthquake. Full article
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Graphical abstract

Graphical abstract
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<p>Geographical and tectonic context of the Mw = 7.2 Haiti 14 August 2021 earthquake. The epicentre is shown as a green star. The yellow circle depicts Dobrovoslky’s area. Blue lines represent the coasts, brown lines represent the active faults (shown only for the Central American and Caribbean regions) and red lines represent the plate borders. Earthquakes of magnitude equal to or greater than 4.2 with a maximum depth of 50 km that occurred in the six months before the Haiti in the Dobrovolsky have been visualised as a filled dot with size proportional to their magnitude and colour to their origin time.</p>
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<p>Yearly average of the selected atmospheric parameters in the investigated six-month period (blue line). The linear fit is shown as a green line and the residual after removing the fit as a red line.</p>
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<p>Time series (<b>a</b>) and histogram (<b>b</b>) of the CH4 values estimated by the instrument AIRS onboard the AQUA satellite provided in version 7.0.</p>
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<p>Selected orbits of CSES-01 as the closest to the Mw = 7.2 Haiti 2021 epicentre.</p>
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<p>Cumulative seismic stress calculated with earthquakes in Dobrovolsky’s area and with a magnitude equal to or greater than completeness magnitude 4.3. The fit is shown as a green line, and its coefficients are presented in the box inside the figure (days in the box are counted from 0 to 180 days, in particular, x<sub>0</sub> = −65.03 in days with respect to the earthquake day).</p>
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<p>Daily seismicity analysis made by the daily E<sub>S</sub> parameter (shown as blue bars). According to the previous literature, the red line represents the threshold to consider active the region on a specific day. The dashed black vertical line indicates the day of Mw = 7.2 Haiti 2021 earthquake.</p>
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<p>Aerosol investigation in the six months before the Haiti 2021 earthquake (vertical dashed black line) using (<b>a</b>) a symmetric square area centred on the epicentre or (<b>b</b>) a rectangular area around the seismic displacement. The years 1980 (only <b>a</b>), 1991, 2015 and 2020 have been excluded for outlier values. Red circles underline anomalies in the lower panel and the one in the upper panel, confirmed by investigations in both areas, while orange circles underline anomalies in the upper panel only.</p>
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<p>Carbon monoxide (CO) investigation in the six months before the Haiti 2021 earthquake (vertical dashed black line) using (<b>a</b>) a symmetric square area centred on the epicentre or (<b>b</b>) a rectangular area around the seismic displacement. The years 1981, 1982, 1983, 1989, 1992 (only <b>b</b>) and 1998 have been excluded for outlier values. Red circles underline anomalies in the lower panel and the one in the upper panel, confirmed by investigations in both areas, while orange circles underline anomalies in the upper panel only.</p>
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<p>Dimethyl sulphide (DMS) investigation in the six months before the Haiti 2021 earthquake (vertical dashed black line) using (<b>a</b>) a symmetric square area centred on the epicentre or (<b>b</b>) a rectangular area around the seismic displacement. The years 1980 and 2005 have been excluded for outlier values. Red circles underline positive anomalies, while blue circle underlines a negative anomaly.</p>
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<p>Sulphur dioxide (SO<sub>2</sub>) investigation in the six months before the Haiti 2021 earthquake (vertical dashed black line) using (<b>a</b>) a symmetric square area centred on the epicentre or (<b>b</b>) a rectangular area around the seismic displacement. The years 1982, 1991, 1992, 2003 (only <b>a</b>) and 2006 (only <b>b</b>) have been excluded for outlier values. Red circles underline anomalies.</p>
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<p>Methane (CH<sub>4</sub>) investigation in the six months before the Haiti 2021 earthquake (vertical dashed black line). The data source is the AISR instrument, so the background has been calculated over a shorter period (from 2002 to 2023, excluding the earthquake year 2021). Red circles underline anomalies.</p>
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<p>Maps of Aerosol (AOT) on 13 June and on 14 July 2021. A red asterisk marks the epicentre. Dashed grey lines represent the main plate boundaries. The blue lines represent coastlines.</p>
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<p>Time series of CSES-01 Ne night-time values (red dot/line) acquired in the Dobrovolsky’s area in the six months before the Mw = 7.2 Haiti 2021 earthquake (vertical dashed black line). Green lines indicate thresholds to define anomalies based on median (m) and interquartile range (Iqr).</p>
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<p>CSES-01 Ne night-time latitudinal profiles acquired in geomagnetic quiet time. The blue boxes and dashed black lines represent the standard ranges of the values for each degree of latitude. The red crosses indicate the outlier values of Ne. The latitude of the Haiti earthquake (EQ) is represented as a vertical black line.</p>
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<p>Cumulate of the Swarm Y-East component of magnetic field anomalies in the six months before Mw = 7.2 Haiti 2021 earthquake in (<b>a</b>) epicentral area shown in (<b>c</b>); (<b>b</b>) in a comparison area shown in (<b>d</b>). A linear fit of the cumulative anomaly trend has been performed and shown as red line in (<b>a</b>,<b>b</b>). The maps in (<b>c</b>,<b>d</b>) of the Dobrovolsky’s and comparison areas underline the sea part with blue patches and land by green patches. The epicentre and centre of comparison area is represented by a red and the investigation areas with yellow circles. (<b>e</b>) Differences in the trends to extract the anomalies are more likely related to the preparation phase of the earthquake.</p>
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<p>(<b>A</b>)Time series of the Total Electron Content (TEC) residuals estimated from the Global Ionospheric Maps of Total Electron Content (GIM-TEC) maps and interpolated above the earthquake epicentre and at 2 LT. The green horizontal lines indicate thresholds for anomalous values in the year of the earthquake (2021). (<b>B</b>) Geomagnetic activity represented by Dst (blue line) and ap (orange line) indexes.</p>
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<p>Map of positions of Elsa and Grace hurricanes and Tropical Storm Fred that crossed the investigated area during the six months before the Haiti 2021 earthquake occurrence. The yellow circle represents the Dobrovolsky’s area and the green boxes shows the atmospheric research areas implemented in MEANS algorithm for this earthquake.</p>
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<p>Cumulative trends in the lithosphere (earthquakes), atmosphere and ionosphere (anomalies). The number inside grey circles indicates the most important stages depicted by this summary graph: (1) particular high seismicity, (2) high seismicity immediately followed by aerosol release, (3) increase of ionospheric anomalies (especially Swarm magnetic field) and (4) variation of seismic trend synchronous to increase of atmospheric anomalies followed by two consecutive days of CSES-01 electron density anomalies. These important group of anomalies possibly related to the incoming earthquake has been underlined by bold red circles. Anomalies possibly related to weather hurricanes and storms have been underlined by dashed red circles.</p>
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<p>Methane (CH<sub>4</sub>) version 6 data in the form of time series (<b>a</b>) and histogram (<b>b</b>) before and after the global warming detrending.</p>
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<p>Methane (CH<sub>4</sub>) version 6 investigation in the six months before the Haiti 2021 earthquake. The data source is AISR instrument, and so the background has been calculated on a shorter time period (from 2002 to 2023, excluding the year of the earthquake–2021). Red circles underline anomalies.</p>
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<p>CSES-01 Ne daytime latitudinal profiles acquired in geomagnetic quiet time. The blue boxes and dashed black lines represent the standard ranges of the values for each degree of latitude. The red crosses indicate the outlier values of Ne. The latitude of the Haiti earthquake (EQ) is represented as a vertical black line.</p>
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<p>Map of the two selected areas (red circles) marked with numbers “1” and “2”. The earthquakes are represented as black boxes and the Mw = 7.2 Haiti 2021 earthquake epicentre as yellow star. Main plate boundaries are depicted with bold dark blue lines.</p>
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<p>Gutenberg–Richter distributions in (<b>a</b>) selected Area 1 of <a href="#geosciences-14-00096-f0A4" class="html-fig">Figure A4</a>; (<b>b</b>) selected Area 2 and (<b>c</b>) the Dobrovolsky’s area. The last case considers two years of data before the Haiti earthquake.</p>
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26 pages, 1287 KiB  
Systematic Review
A State-of-the-Art of Exoskeletons in Line with the WHO’s Vision on Healthy Aging: From Rehabilitation of Intrinsic Capacities to Augmentation of Functional Abilities
by Rebeca Alejandra Gavrila Laic, Mahyar Firouzi, Reinhard Claeys, Ivan Bautmans, Eva Swinnen and David Beckwée
Sensors 2024, 24(7), 2230; https://doi.org/10.3390/s24072230 - 30 Mar 2024
Cited by 2 | Viewed by 3624
Abstract
The global aging population faces significant health challenges, including an increasing vulnerability to disability due to natural aging processes. Wearable lower limb exoskeletons (LLEs) have emerged as a promising solution to enhance physical function in older individuals. This systematic review synthesizes the use [...] Read more.
The global aging population faces significant health challenges, including an increasing vulnerability to disability due to natural aging processes. Wearable lower limb exoskeletons (LLEs) have emerged as a promising solution to enhance physical function in older individuals. This systematic review synthesizes the use of LLEs in alignment with the WHO’s healthy aging vision, examining their impact on intrinsic capacities and functional abilities. We conducted a comprehensive literature search in six databases, yielding 36 relevant articles covering older adults (65+) with various health conditions, including sarcopenia, stroke, Parkinson’s Disease, osteoarthritis, and more. The interventions, spanning one to forty sessions, utilized a range of LLE technologies such as Ekso®, HAL®, Stride Management Assist®, Honda Walking Assist®, Lokomat®, Walkbot®, Healbot®, Keeogo Rehab®, EX1®, overground wearable exoskeletons, Eksoband®, powered ankle–foot orthoses, HAL® lumbar type, Human Body Posturizer®, Gait Enhancing and Motivation System®, soft robotic suits, and active pelvis orthoses. The findings revealed substantial positive outcomes across diverse health conditions. LLE training led to improvements in key performance indicators, such as the 10 Meter Walk Test, Five Times Sit-to-Stand test, Timed Up and Go test, and more. Additionally, enhancements were observed in gait quality, joint mobility, muscle strength, and balance. These improvements were accompanied by reductions in sedentary behavior, pain perception, muscle exertion, and metabolic cost while walking. While longer intervention durations can aid in the rehabilitation of intrinsic capacities, even the instantaneous augmentation of functional abilities can be observed in a single session. In summary, this review demonstrates consistent and significant enhancements in critical parameters across a broad spectrum of health conditions following LLE interventions in older adults. These findings underscore the potential of LLE in promoting healthy aging and enhancing the well-being of older adults. Full article
(This article belongs to the Special Issue Intelligent Sensors and Robots for Ambient Assisted Living)
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<p>Prisma flow diagram.</p>
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<p>Studies’ classification.</p>
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15 pages, 2454 KiB  
Article
Thermal Anomalies Observed during the Crete Earthquake on 27 September 2021
by Soujan Ghosh, Sudipta Sasmal, Sovan K. Maity, Stelios M. Potirakis and Masashi Hayakawa
Geosciences 2024, 14(3), 73; https://doi.org/10.3390/geosciences14030073 - 9 Mar 2024
Cited by 4 | Viewed by 1819
Abstract
This study examines the response of the thermal channel within the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism during the notable earthquake in Crete, Greece, on 27 September 2021. We analyze spatio-temporal profiles of Surface Latent Heat Flux (SLHF), Outgoing Longwave Radiation (OLR), and Atmospheric Chemical [...] Read more.
This study examines the response of the thermal channel within the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism during the notable earthquake in Crete, Greece, on 27 September 2021. We analyze spatio-temporal profiles of Surface Latent Heat Flux (SLHF), Outgoing Longwave Radiation (OLR), and Atmospheric Chemical Potential (ACP) using reanalysis data from the National Oceanic and Atmospheric Administration (NOAA) satellite. Anomalies in these parameters are computed by removing the background profile for a non-seismic condition. Our findings reveal a substantial anomalous increase in these parameters near the earthquake’s epicenter 3 to 7 days before the main shock. The implications of these observations contribute to a deeper understanding of the LAIC mechanism’s thermal channel in seismic events. Full article
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<p>The location of the earthquake epicenter (red circle), the earthquake preparation zone (black circle), and the local earthquake fault lines for the Crete, Greece, earthquake.</p>
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<p>Variation in the SLHF anomaly from 13 to 27 September 2021 for the Crete earthquake. Along the X and Y axes, we present the geographic longitude and latitude, respectively. The black outlines are the country border, and the red dot marks the epicenter of the Crete earthquake, also denoted by the letter “C”.</p>
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<p>SLHF anomaly variation from 28 September to 12 October 2021. The figure format follows the format of <a href="#geosciences-14-00073-f002" class="html-fig">Figure 2</a>. The black outlines are the country border, and the red dot marks the epicenter of the Crete earthquake, also denoted by the letter “C”.</p>
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<p>Eddy Field OLR variations around the Crete earthquake epicenter during 13–27 September 2021 with a spatial span of latitudes 30° N to 39° N and longitudes 20° E to 30° E. The red dot and the letter “C” indicate the epicenter of the Crete earthquake. The country boundaries are indicated by black lines. The color bar represents the intensity of the mean Eddy Field in W/m<sup>2</sup>.</p>
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<p>Same as <a href="#geosciences-14-00073-f004" class="html-fig">Figure 4</a> for 28 September to 12 October 2021. The black outlines are the country border, and the red dot marks the epicenter of the Crete earthquake, also denoted by the letter “C”.</p>
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<p>ACP distribution around the epicenter of the Crete earthquake from 28 September to 12 October 2021. The black outlines are the country border, and the red dot marks the epicenter of the Crete earthquake, also denoted by the letter “C”.</p>
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<p>Same as <a href="#geosciences-14-00073-f006" class="html-fig">Figure 6</a> for 28 September to 12 October 2021. The black outlines are the country border, and the red dot marks the epicenter of the Crete earthquake, also denoted by the letter “C”.</p>
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