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23 pages, 14443 KiB  
Article
The Formation and Modification of the Arcuate Tectonic Belt in the Northeastern Tibetan Plateau: Insight from Three-Dimensional Finite Element Numerical Simulation
by Yilin Zhao, Wei Shi, Yujun Sun and Guiting Hou
J. Mar. Sci. Eng. 2025, 13(1), 170; https://doi.org/10.3390/jmse13010170 (registering DOI) - 18 Jan 2025
Abstract
The arcuate tectonic belt in the northeast Tibetan Plateau has been a contentious topic regarding its formation and evolution, owing to its distinctive geological structure as the lateral growth boundary of the plateau. In this research, leveraging geological and geophysical data, a three-dimensional [...] Read more.
The arcuate tectonic belt in the northeast Tibetan Plateau has been a contentious topic regarding its formation and evolution, owing to its distinctive geological structure as the lateral growth boundary of the plateau. In this research, leveraging geological and geophysical data, a three-dimensional finite element numerical model is employed to explore the impact of lateral and vertical inhomogeneities in lithospheric strength on the northeast Tibetan Plateau’s growth and the arcuate tectonic belt’s formation and alteration. Additionally, the kinematic and deformation traits of the arcuate tectonic belt, such as regional motion velocity, stress, and crustal thickness during shortening and strike-slip deformation, are comparatively analyzed. The findings indicate that the arcuate tectonic belt takes shape when the weakly strengthened Tibetan Plateau is impelled into the Yinchuan Basin after being obstructed by the robust Alax and Ordos blocks during lateral expansion. Intense shear deformation occurs at the block boundaries during the arc tectonic belt’s formation. The weak middle-lower crust, serving as a detachment layer, facilitates the plateau’s lateral growth and crustal shortening and thickening without perturbing the overall deformation characteristics. It is verified that the arcuate tectonic belt was formed during the NE-SW compression phase from around 9.5 to 2.5 Ma, accompanied by significant crustal shortening and thickening. Since 2.5 Ma, within the ENE-WSW compression process, the internal faults of the arcuate tectonic belt are predominantly strike-slip, with no pronounced crustal shortening and thickening. Only local topographical modification is conspicuous. This study will enhance our comprehension of the Tibetan Plateau’s uplift and lateral growth process and furnish a foundation for investigating the formation of arcuate tectonic belts. Full article
(This article belongs to the Special Issue Advances in Ocean Plate Motion and Seismic Research)
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Figure 1

Figure 1
<p>Tectonic geomorphological map of the Tibetan Plateau and its adjacent regions. The fault data were sourced from the Data Sharing Infrastructure of the Seismic Active Fault Survey Data Center, available at <a href="https://www.activefault-datacenter.cn" target="_blank">https://www.activefault-datacenter.cn</a> (accessed on 26 April 2020). The DEM data with a resolution of 90 m was acquired from the Geospatial Data Cloud, accessible at <a href="http://www.gscloud.cn" target="_blank">http://www.gscloud.cn</a> (accessed on 26 May 2019).</p>
Full article ">Figure 2
<p>Geological schematic map of the study area. This map has been modified by CorelDRAW (Version 2018) based on [<a href="#B25-jmse-13-00170" class="html-bibr">25</a>]. The expanded portion is sourced from the online geological maps of Chinese provinces offered by the OSGeo China Center (<a href="https://www.osgeo.cn/" target="_blank">https://www.osgeo.cn/</a>, accessed on 24 October 2016), which cover Gansu, Qinghai, Shaanxi, the Ningxia Hui Autonomous Region, and the Inner Mongolia Autonomous Region. F<sub>1</sub>: Haiyuan fault zone; F<sub>2</sub>: Xiangshan–Tianjingshan fault zone; F<sub>3</sub>: Yantongshan fault zone; F<sub>4</sub>: Niushoushan–Luoshan fault zone; F<sub>5</sub>: the fault of the northern margin of the West Qinling Mountains; F<sub>6</sub>: Maxianshan fault zone; F<sub>7</sub>: Yellow River fault; F<sub>8</sub>: the fault at the east piedmont of Helan Shan; F<sub>9</sub>: Xiaoguanshan fault.</p>
Full article ">Figure 3
<p>Tectonic geomorphological features and crustal thickness distribution within the study region. The data pertaining to the crustal thickness is obtained from the publicly accessible CRUST 1.0 model, available at the website: <a href="https://igppweb.ucsd.edu/~gabi/crust1.html" target="_blank">https://igppweb.ucsd.edu/~gabi/crust1.html</a> (accessed on 27 August 2013).</p>
Full article ">Figure 4
<p>Block division, boundary conditions and mesh division within the numerical model. (<b>a</b>) Illustrated are the boundary conditions during the period from 9.5 Ma to 2.5 Ma. (<b>b</b>) Presented are the boundary conditions from 2.5 Ma up to the present. (<b>c</b>) Shown is the model mesh division with a rotation angle of 0.05 degrees. The black arrow symbolizes the velocity boundary conditions, and the black triangle represents the free-slip boundary conditions. Here, θ denotes the azimuth of the velocity. OB: Ordos block; AB: Alax block; LZB: Longzhong block; M-CB: Meso-Cenozoic basins (including the arcuate tectonic belt and Yinchuan Basin); F<sub>1</sub>: Haiyuan fault zone.</p>
Full article ">Figure 5
<p>Horizontal velocity (indicated by black arrows) and the contour map of vertical velocity (positive for upward) for Case–1 to Case–5. (<b>a</b>–<b>d</b>) Results of Case–1 to Case–4 at 2.5 Ma. (<b>e</b>) Result of Case–5 at 2.5 Ma. (<b>f</b>) Result of Case–5 at present.</p>
Full article ">Figure 6
<p>The maximum and minimum principal stresses and the maximum shear stress (shown by contour lines) in Case–5. (<b>a</b>) Results of the thrusting stage (at 2.5 Ma). (<b>b</b>) Results of the strike-slipping stage (at present). In elasticity, we define the tensile stress as positive and the compressive stress as negative. The purple lines indicate the direction and the absolute value of minimum principal stress (representing the maximum compressive stress), and the black lines represent the direction and the absolute value of maximum principal stress.</p>
Full article ">Figure 7
<p>The crustal thickness and the distribution of the maximum and minimum principal stresses in Case–5 during the (<b>a</b>) extrusion stage (2.5 Ma) and (<b>b</b>) strike-slip stage (present).</p>
Full article ">Figure 8
<p>Horizontal velocities and vertical velocity contour maps for Case–6 (<b>a</b>), Case–7 (<b>c</b>), Case–8 (<b>e</b>), and crustal thickness contour maps for Case–6 (<b>b</b>), Case–7 (<b>d</b>), Case–8 (<b>f</b>) at the thrust stage (2.5 Ma).</p>
Full article ">Figure 9
<p>Contour maps of effective viscosity superimposed faults at a depth of 20 km (<b>a</b>), 60 km (<b>b</b>). These two pictures have been modified by CorelDRAW (Version 2018) based on [<a href="#B63-jmse-13-00170" class="html-bibr">63</a>]. The lithospheric structure for effective viscosity is according to CRUST 1.0, <a href="http://igppweb.ucsd.edu/~gabi/rem.html" target="_blank">http://igppweb.ucsd.edu/~gabi/rem.html</a> (accessed on 15 July 2013).</p>
Full article ">
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>
Full article ">
60 pages, 6441 KiB  
Article
Excitation of ULF, ELF, and VLF Resonator and Waveguide Oscillations in the Earth–Atmosphere–Ionosphere System by Lightning Current Sources Connected with Hunga Tonga Volcano Eruption
by Yuriy G. Rapoport, Volodymyr V. Grimalsky, Andrzej Krankowski, Asen Grytsai, Sergei S. Petrishchevskii, Leszek Błaszkiewicz and Chieh-Hung Chen
Atmosphere 2025, 16(1), 97; https://doi.org/10.3390/atmos16010097 - 16 Jan 2025
Viewed by 257
Abstract
The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, [...] Read more.
The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, including ultra low frequency (ULF), extremely low frequency (ELF), and very low frequency (VLF) ranges. The electric current source due to lightning near the location of the Hunga Tonga volcano eruption has a wide-band frequency spectrum determined in this paper based on a data-driven approach. The spectrum is monotonous in the VLF range but has many significant details at the lower frequencies (ULF, ELF). The decreasing amplitude tendency is maintained at frequencies exceeding 0.1 Hz. The density of effective lightning current in the ULF range reaches the value of the order of 10−7 A/m2. A combined dynamic/quasi-stationary method has been developed to simulate ULF penetration through the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system. This method is suitable for the ULF range down to 10−4 Hz. The electromagnetic field is determined from the dynamics in the ionosphere and from a quasi-stationary approach in the atmosphere, considering not only the electric component but also the magnetic one. An analytical/numerical method has been developed to investigate the excitation of the global Schumann resonator and the eigenmodes of the coupled Schumann and ionospheric Alfvén resonators in the ELF range and the eigenmodes of the Earth–ionosphere waveguide in the VLF range. A complex dispersion equation for the corresponding disturbances is derived. It is shown that oscillations at the first resonance frequency in the Schumann resonator can simultaneously cause noticeable excitation of the local ionospheric Alfvén resonator, whose parameters depend on the angle between the geomagnetic field and the vertical direction. VLF propagation is possible over distances of 3000–10,000 km in the waveguide Earth–ionosphere. The results of simulations are compared with the published experimental data. Full article
(This article belongs to the Special Issue Feature Papers in Upper Atmosphere (2nd Edition))
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

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>
Full article ">Figure 2
<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>
Full article ">Figure 3
<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>
Full article ">Figure 6
<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>
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<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>
Full article ">
22 pages, 17623 KiB  
Article
An Analysis of Meteorological Anomalies in Kamchatka in Connection with the Seismic Process
by Alexey Lyubushin, Galina Kopylova, Eugeny Rodionov and Yulia Serafimova
Atmosphere 2025, 16(1), 78; https://doi.org/10.3390/atmos16010078 - 13 Jan 2025
Viewed by 316
Abstract
This study investigates the hypothesis that meteorological anomalies may precede earthquake events. Long-term time series of observations for air temperature, atmospheric pressure and precipitation at a meteorological station in Kamchatka are considered. Time series are subjected to Huang decomposition into sequences of levels [...] Read more.
This study investigates the hypothesis that meteorological anomalies may precede earthquake events. Long-term time series of observations for air temperature, atmospheric pressure and precipitation at a meteorological station in Kamchatka are considered. Time series are subjected to Huang decomposition into sequences of levels of empirical oscillation modes (intrinsic mode functions—IMFs), forming a set of orthogonal components with decreasing average frequency. For each IMF level, the instantaneous amplitudes of envelopes are calculated using the Hilbert transform. A comparison with the earthquake sequence is made using a parametric model of the intensity of two interacting point processes, which allows one to quantitatively estimate the “measure of the lead” of the time instants of the compared sequences. For each IMF level, the number of time moments of the largest local maxima of instantaneous amplitudes which is equal to the number of earthquakes is selected. As a result of the analysis, it turned out that for the sixth IMF level (periods of 8–16 days), the “lead measure” of the instantaneous amplitude maxima of meteorological parameters in comparison with earthquake time moments significantly exceeds the inverse lead, which confirms the existence of prognostic changes in meteorological parameters in the problem of “atmosphere–lithosphere” interaction. This study reveals that certain meteorological anomalies can be a precursor for seismic activity. Full article
(This article belongs to the Section Planetary Atmospheres)
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Figure 1

Figure 1
<p>Initial data—atmospheric pressure (<b>a</b>), air temperature (<b>b</b>) and precipitation (<b>c</b>) at the Pionerskaya weather station, 4 November 1996–30 September 2024, with a time step of 3 h, giving 81,544 readings.</p>
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<p>Map of epicenters of 418 earthquakes with <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>L</mi> </msub> <mo>≥</mo> <mn>5.5</mn> </mrow> </semantics></math> that occurred in the KB GS RAS responsibility area (area bounded by the red line) from 4 November 1996 to 30 September 2024, and the location of the Pionerskaya weather station (black circle). Red lines are active faults according to [<a href="#B23-atmosphere-16-00078" class="html-bibr">23</a>].</p>
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<p>Waveforms of the EEMD decomposition of the atmospheric pressure series for IMF levels with numbers from 1 to 12, the amplitudes of their envelopes and instantaneous frequencies calculated using the Hilbert transform.</p>
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<p>Waveforms of the EEMD decomposition of the air temperature series for IMF levels with numbers from 1 to 12, the amplitudes of their envelopes and instantaneous frequencies calculated using the Hilbert transform.</p>
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<p>Waveforms of the EEMD decomposition of the precipitation series for IMF levels with numbers from 1 to 12, the amplitudes of their envelopes and instantaneous frequencies calculated using the Hilbert transform.</p>
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<p>Average values of instantaneous frequencies depending on the IMF level number for time series of atmospheric pressure (blue), air temperature (red) and precipitation (green).</p>
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<p>(<b>a</b>) Time sequence of 418 earthquakes with magnitude <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>L</mi> </msub> <mo>≥</mo> <mn>5.5</mn> </mrow> </semantics></math>; (<b>b</b>–<b>e</b>) Results of calculating the amplitudes of the envelope for decompositions of the original time series: (<b>b</b>) for the 6th IMF level of decomposition of the atmospheric pressure series; (<b>c</b>) for the 6th IMF level of decomposition of the air temperature series; (<b>d</b>) for the 6th IMF level of decomposition of the atmospheric precipitation series; and (<b>e</b>) for the 6th level of wavelet decomposition of the air temperature series. The positions of the 418 largest local maxima of the envelope amplitudes are marked with red dots on each graph.</p>
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<p>Average values of the components of the influence matrices of the largest local maxima of the amplitudes of the envelopes of meteorological parameter series on seismic events (left column of the graphs) and average values of the components of the reverse influence (right column of the graphs). The graphs (<b>a1</b>,<b>a2</b>) relate to ”direct” and “reverse” influence matrices components between time moments of the biggest local maxima of atmospheric pressure instantaneous amplitudes at IMF level #6 and time moments of seismic events. The same is true for graphs (<b>b1</b>,<b>b2</b>) for air temperature; (<b>c1</b>,<b>c2</b>) for precipitation; and (<b>d1</b>,<b>d2</b>) again for temperature but at wavelet decomposition detail level #6 instead of IMF level #6. Graphs (<b>e1</b>,<b>e2</b>) present the averaging of all “direct” and “reverse” influence matrices components. The horizontal red lines represent the average values of the averaged components of the influence matrices for each meteorological parameter for the “direct prognostic” (left column of the graphs) and “reverse postseismic” (right column of the graphs) influence. The values of the average values are shown in red above each horizontal mean line. <a href="#atmosphere-16-00078-f008" class="html-fig">Figure 8</a> demonstrates a clear leading effect of meteorological anomalies before seismic events.</p>
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<p>Gray lines present the average values of the components of the matrices of the influence of the largest local maxima of the amplitudes of the envelopes of atmospheric pressure and air temperature series (the precipitation time series is excluded) on seismic events in comparison with the time moments of earthquakes with magnitudes <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>W</mi> </msub> <mo>≥</mo> <mn>6.5</mn> </mrow> </semantics></math> (<b>a</b>) and the closest earthquakes for which the ratio of the epicentral distance <math display="inline"><semantics> <mi>R</mi> </semantics></math> to the size of their source <math display="inline"><semantics> <mi>L</mi> </semantics></math> is <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>/</mo> <mi>L</mi> <mo>≤</mo> <mn>6</mn> </mrow> </semantics></math> (<b>b</b>). Vertical red lines indicate time moments and magnitudes (on the (a)) of seismic events.</p>
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16 pages, 5768 KiB  
Article
On the Ionosphere–Atmosphere–Lithosphere Coupling During the 9 November 2022 Italian Earthquake
by Mirko Piersanti, Giulia D’Angelo, Dario Recchiuti, Fabio Lepreti, Paola Cusano, Enza De Lauro, Vincenzo Carbone, Pietro Ubertini and Mariarosaria Falanga
Geosciences 2025, 15(1), 22; https://doi.org/10.3390/geosciences15010022 - 10 Jan 2025
Viewed by 391
Abstract
In the last decades, the scientific community has been focused on searching earthquake signatures in the Earth’s atmosphere, ionosphere, and magnetosphere. This work investigates an offshore Mw 5.5 earthquake that struck off the Marche region’s coast (Italy) on 9 November 2022, with a [...] Read more.
In the last decades, the scientific community has been focused on searching earthquake signatures in the Earth’s atmosphere, ionosphere, and magnetosphere. This work investigates an offshore Mw 5.5 earthquake that struck off the Marche region’s coast (Italy) on 9 November 2022, with a focus on the potential coupling between the Earth’s lithosphere, atmosphere, and magnetosphere triggered by the seismic event. Analysis of atmospheric temperature data from ERA5 reveals a significant increase in potential energy (Ep) at the earthquake’s epicenter, consistent with the generation of Atmospheric Gravity Waves (AGWs). This finding is further corroborated by the MILC analytical model, which accurately simulates the observed Ep trends (within 5%), supporting the theory of Lithosphere–Atmosphere–Ionosphere–Magnetosphere coupling. The study also examines the vertical Total Electron Content (vTEC) and finds notable fluctuations at the epicenter, exhibiting periodicities (7–12 min) characteristic of AGWs and traveling ionospheric disturbances. The correlation between ERA5 observations and MILC model predictions, particularly in temperature deviations and Ep distributions, strengthens the hypothesis that earthquake-generated AGWs impact atmospheric conditions at high altitudes, leading to observable ionospheric perturbations. This research contributes to a deeper understanding of Lithosphere–Atmosphere–Ionosphere–Magnetosphere coupling mechanisms and the potential for developing reliable earthquake prediction tools. Full article
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Figure 1
<p>(<b>a</b>) Geographic map of Italy: the red circle indicates the epicenter of EQ2022; (<b>b</b>) Accelerograms acquired at FANO station (43.8434° N and 13.0183° E) along the three directions of motion: east–west (EW), north–south (NS) and vertical (Z) [<a href="#B31-geosciences-15-00022" class="html-bibr">31</a>]; (<b>c</b>) focal mechanism indicating the nodal planes: strike = 142°, dip = 35°, rake = 110° [<a href="#B32-geosciences-15-00022" class="html-bibr">32</a>]; (<b>d</b>) static displacement field estimated for the earthquake by using Coulomb software, over a 100 km × 100 km grid at the sea level. The horizontal displacements measured at the GNSS stations (blue circles) and the modeled ones are represented by blue and black arrows, respectively. The GNSS stations are numbered according to Pezzo et al. [<a href="#B33-geosciences-15-00022" class="html-bibr">33</a>].</p>
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<p>Co-seismic ERA-5 observations. (<b>A</b>) Vertical profiles of temperature (left panel), temperature deviation (middle panel), and potential energy (right panel) on 9 November 2022 at 06:00 UT. (<b>B</b>) Energy potential maps from 8 (left) to 10 (right) November 2022. The date and altitude are indicated in each panel. The earthquake epicenter is marked by a black dot.</p>
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<p>Comparison between the MILC model previsions and the co-seismic ERA5 observations on 9 November 2022. (<b>Left panel</b>): dispersion relation of the AGW frequency and wavelength predicted by the MILC model, in which the red dashed line represents the parameter <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>h</mi> </mrow> </semantics></math>. (<b>Right panel</b>): comparison between MILC model prevision (red line) and observations (blue line) of the temperature deviation vertical profile. Here, <math display="inline"><semantics> <msub> <mi>c</mi> <mn>0</mn> </msub> </semantics></math> is the sound speed and <span class="html-italic">h</span> is the temperature scale height.</p>
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<p>Comparison between the MILC model previsions and the co-seismic ERA5 observations relative to 9 November 2022. (<b>Left panel</b>): observed energy potential map. (<b>Middle panel</b>): modeled energy potential map. (<b>Right panel</b>): difference between observed and modeled energy potential map. The date and altitude are indicated in each panel. The black dot represents the earthquake epicenter.</p>
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<p>vTEC fluctuations characterized by a period between 5 and 9 min for all the satellites in the field of view of all available GNSS receivers near the EQ epicenter (black dot) recorded every 5 min between 05:56 UT and 06:36 UT on 9 November 2022.</p>
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<p>Solar wind parameters and the Sym-H index (<a href="https://cdaweb.gsfc.nasa.gov/" target="_blank">https://cdaweb.gsfc.nasa.gov/</a> (accessed on 17 December 2024)) for 9 November 2022. Box (<b>A</b>) shows the Interplanetary magnetic field components. Box (<b>B</b>) shows the solar wind parameters, namely (from the top) the three components of the solar wind speed, solar wind plasma density, and solar wind dynamic pressure. Box (<b>C</b>) shows the Sym-H index. Red dashed lines represent the time of the EQ occurrence.</p>
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<p>Weather report maps over Italy (<a href="https://meteologix.com/it/satellite/italy/satellite-nature-15min-en/20221109-0500z.html" target="_blank">https://meteologix.com/it/satellite/italy/satellite-nature-15min-en/20221109-0500z.html</a> (accessed on 1 January 2024)) for the 9 November 2022 at 06:00 UT.</p>
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18 pages, 10602 KiB  
Article
A Study of Lithosphere–Ionosphere Seismic Precursors from Detecting Gamma-Ray and Total Electron Content Anomalies Prior to the 2018 ML6.2 Hualien Earthquake in Eastern Taiwan
by Ching-Chou Fu, Hau-Kun Jhuang, Yi-Ying Ho, Tsung-Che Tsai, Lou-Chuang Lee, Cheng-Horng Lin, Ching-Ren Lin, Vivek Walia and I-Te Lee
Remote Sens. 2025, 17(2), 188; https://doi.org/10.3390/rs17020188 - 7 Jan 2025
Viewed by 545
Abstract
This study conducts a comprehensive analysis of observations related to the ML6.2 Hualien earthquake that struck eastern Taiwan on 6 February 2018, focusing particularly on gamma-ray emissions and total electron content (TEC) as earthquake precursors. Prior research has shown that significant [...] Read more.
This study conducts a comprehensive analysis of observations related to the ML6.2 Hualien earthquake that struck eastern Taiwan on 6 February 2018, focusing particularly on gamma-ray emissions and total electron content (TEC) as earthquake precursors. Prior research has shown that significant gamma-ray enhancements are frequently detected at the YMSG (Yangmingshan gamma-ray) station prior to major earthquakes in eastern and northeastern Taiwan, suggesting that gamma-ray anomalies may serve as reliable indicators for identifying seismic precursors in this area. Our findings reveal a significant rise in gamma-ray emissions at the YMSG station from 19 January to 4 February 2018, which corresponds to a precursor period of approximately 18 days before the Hualien earthquake. Positive and negative TEC anomalies were observed in Taiwan on 20–21 January and 5 February, respectively, and may be considered as ionospheric precursors to the earthquake. Additionally, deep-learning techniques applied to TEC data facilitate the detection of ionospheric precursors associated with the Hualien earthquake, enabling forecasts of an approaching seismic event. Collectively, these observations indicate that all identified anomalies are regarded as short-term precursors, explicable through the theoretical framework of lithosphere–ionosphere coupling (LIC). Full article
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<p>(<b>a</b>) The simplified sketch of the regional map and principal tectonic structures around Taiwan shows the Philippine Sea Plate (PSP) is moving northwest at 8 cm/y towards the Eurasia Plate (EP). In the upper part of <a href="#remotesensing-17-00188-f001" class="html-fig">Figure 1</a>a, the colored circle symbols represent the earthquake epicenter, with the focal depths corresponding to different colors from 1 December 2017, to 31 March 2018. The open square represents the location of the YMSG gamma-ray station. The red lines indicate the active fault proposed by the Central Geological Survey of Taiwan [<a href="#B6-remotesensing-17-00188" class="html-bibr">6</a>]. The white star indicates the epicenter of the 1951/10/21 (M<sub>L</sub>7.3) and 1951/10/22 (M<sub>L</sub>7.1) Hualien earthquakes. Seismic events with magnitudes larger than five are labeled and listed in <a href="#remotesensing-17-00188-t001" class="html-table">Table 1</a>. (<b>b</b>) The circular symbols denote the distribution of aftershock epicenters occurring over a span of approximately three weeks, with variations in color utilized to signify changes in the temporal sequence. LV: Longitudinal Valley; OkT: Okinawa Trough; RkT: Ryukyu Trench.</p>
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<p>The temporal variations, from 1 December 2017, to 31 March 2018, are displayed for the meteorological data (<b>a</b>), including atmospheric pressure (green), humidity (blue), temperature (red), and hourly rainfall (black); the gamma-rays (<b>b</b>) where purple lines indicate the 24 h running averages of gamma-ray data, grey lines represent the raw data, horizontal dashed lines indicate the annual averages of the observed results, and the anomalous threshold value (±2σ) is shown with the yellow bar; and the magnitude and depth of earthquakes (<b>c</b>) are designated by circle symbols in different colors, denoting the epicenter distance to the YMSG station (i.e., red for &lt;50 km, blue for 50 to 130 km, and grey for &gt;130 km). A zoomed time period (<b>d</b>) from 1 January to 14 February 2018, exhibits more details about the gamma-rays and earthquake activity.</p>
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<p>(<b>a</b>) Dst index and (<b>b</b>) The TEC variation at 24.0°N and 121.5°E from 25 days before to 25 days after the M<sub>L</sub>6.2 Hualien earthquake. The red curve is the hourly observed TEC, the two gray curves are the associated UB and LB bounds. The red/black shadow denotes the absolute value of deviations for those observed TEC higher (red) than UB or lower (black) than LB bounds.</p>
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<p>Thirty days of TEC variations before the M<sub>L</sub>6.2 Hualien earthquake. (<b>a</b>) The GIM-TEC variations, from 7 January to 5 February 2018, where the red line denotes the observed TEC and the blue one designates the predicted TEC from the trained model. The black shadow denotes the absolute value of the normalized deviations between the observed GIM-TEC and predicted TEC. The root mean square error (RMSE) for the period from 7 January to 22 January and 22 January to 5 February is 0.1308 and 0.1076, respectively. (<b>b</b>) The CWA-TEC variations from 7 January to 5 February 2018. The red line denotes the observed TEC from CWA data. The blue line denotes the previous 15 days running median of the CWA-TEC data. The black shadow denotes the absolute value of the normalized deviations between the observed CWA-TEC and the running median TEC.</p>
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<p>The variation diagrams illustrate the relationship between the magnitude of the precursory event and (<b>a</b>) the distribution of precursory time and (<b>b</b>) epicenter distance from the YMSG station. The red circles represent the findings of this study. The fitted curves are shown as solid lines, with dashed lines representing the 95% confidence interval of the dataset, and hollow circles correspond to the data from Fu et al. [<a href="#B49-remotesensing-17-00188" class="html-bibr">49</a>].</p>
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<p>Composite plots of anomalies from multiple observations associated with the 2018 Hualien earthquake and its schematic of the Lithosphere–Atmosphere–Ionosphere (LAI) coupling. Detailed descriptions of each anomaly are provided in the text.</p>
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23 pages, 28101 KiB  
Article
Quantifying Time-Lag and Time-Accumulation Effects of Climate Change and Human Activities on Vegetation Dynamics in the Yarlung Zangbo River Basin of the Tibetan Plateau
by Ning Li and Di Wang
Remote Sens. 2025, 17(1), 160; https://doi.org/10.3390/rs17010160 - 5 Jan 2025
Viewed by 638
Abstract
Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the [...] Read more.
Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the health and stability of fragile ecosystems, such as the Yarlung Zangbo River (YZR) basin of the Tibetan Plateau, the highest-elevation river basin in the world. Vegetation responses to climate change are inherently asymmetric, characterized by distinct temporal effects. However, these temporal effects remain poorly understood, particularly in high-altitude ecosystems. Here, we examine the spatiotemporal changes in leaf area index (LAI) and four climatic factors—air temperature, precipitation, potential evapotranspiration, and solar radiation—in the YZR basin over the period 2000–2019. We further explore the time-lag and time-accumulation impacts of these climatic factors on LAI dynamics and apply an enhanced residual trend analysis to disentangle the relative contributions of climate change and human activities. Results indicated that (1) a modest increase in annual LAI at a rate of 0.02 m2 m−2 dec−1 was detected across the YZR basin. Spatially, LAI increased in 66% of vegetated areas, with significant increases (p < 0.05) in 10% of the basin. (2) Temperature, precipitation, and potential evapotranspiration exhibited minimal time-lag (<0.5 months) but pronounced notable time-accumulation effects on LAI variations, with accumulation periods ranging from 1 to 2 months. In contrast, solar radiation demonstrated significant time-lag impacts, with an average lag period of 2.4 months, while its accumulation effects were relatively weaker. (3) Climate change and human activities contributed 0.023 ± 0.092 and –0.005 ± 0.109 m2 m−2 dec−1 to LAI changes, respectively, accounting for 60% and 40% on the observed variability. Spatially, climate change accounted for 85% of the changes in LAI in the upper YZR basin, while vegetation dynamics in the lower basin was primarily driven by human activities, contributing 63%. In the middle basin, vegetation dynamics were influenced by the combined effects of climate change and human activities. Our findings deepen insights into the drivers of vegetation dynamics and provide critical guidance for formulating adaptive management strategies in alpine ecosystems. Full article
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<p>Overview of the study area and its environmental characteristics. (<b>a</b>) Location of the YZR basin on the TP. (<b>b</b>) Elevation distribution across the basin. (<b>c</b>) Land use/cover composition. (<b>d</b>) Mean annual temperature distribution and (<b>e</b>) mean annual precipitation distribution derived from WorldClim Historical Monthly Weather Data (1980–2019) at a spatial resolution of 2.5 min (<a href="https://worldclim.org/data/monthlywth.html" target="_blank">https://worldclim.org/data/monthlywth.html</a> (accessed on 26 November 2024)).</p>
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<p>Flowchart of this study. Dotted-box indicates tools/methods used in the analysis.</p>
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<p>Spatial patterns and temporal trends of climatic factors in the YZR basin during 2000–2019. (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) Mean annual values of air temperature (TEM), precipitation (PRE), potential evapotranspiration (PET), and solar radiation (SRD). (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) Spatial distributions of the trends in TEM, PRE, PET, and SRD). Insets illustrate the frequency distribution of trends across the basin.</p>
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<p>Seasonal variations and trends of climatic factors in the YZR basin and its sub-basins from 2000 to 2019. (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) Seasonal mean values of air temperature (TEM), precipitation (PRE), potential evapotranspiration (PET), and solar radiation (SRD) for spring (MAM), summer (JJA), autumn (SON), and winter (DJF). (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) Seasonal trends of the respective climatic factors. Error bars represent the standard deviations.</p>
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<p>Spatiotemporal variations in LAI across the YZR basin during 2000–2019. (<b>a</b>) Spatial patterns of the growing season (May–September) mean LAI with an inset showing the frequency distribution of LAI values across the basin. (<b>b</b>) Temporal trend of basin-wide mean LAI, with a regression line and 95% confidence interval. (<b>c</b>) Distribution of significant (<span class="html-italic">p</span> &lt; 0.05) and insignificant greening and browning trends.</p>
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<p>Spatial patterns and proportions of optimal time-lag and time-accumulation periods for climatic factors affecting LAI dynamics in the YZR basin. (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) Spatial distribution of the optimal number of months for time-lag (L) and time-accumulation (A) effects for four climatic factors. (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) Corresponding pie charts illustrating the proportions of different time-lag and time-accumulation combinations across the basin. Color codes represent specific lag-accumulation combinations (e.g., L0A0 indicates no lag and accumulation effects, and L3A0 indicates a three-month lag with no accumulation effects).</p>
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<p>Spatial distribution LAI trends attributed to different drivers in the YZR basin over the period 2000 to 2019. (<b>a</b>–<b>d</b>) Trends of LAI components attributed to precipitation (LAI<sub>PRE</sub>), air temperature (LAI<sub>TEM</sub>), potential evapotranspiration (LAI<sub>PET</sub>), and solar radiation (LAI<sub>SRD</sub>). (<b>e</b>,<b>f</b>) Trends of LAI components driven by climate change (LAI<sub>CC</sub>) and human activities (LAI<sub>HA</sub>). Insets in each panel show the frequency distribution of trend values within the basin. Positive trends are shown in green, while negative trends are in magenta, with hatched areas indicating statistically significant trends at the <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>Percentage contributions of climate change (CC) and human activities (HAs) to LAI changes across the YZR basin from 2000 to 2019. (<b>a</b>) Spatial distribution of the percentage contribution of CC (%), with pie charts summarizing the contributions across the entire basin (YZR) and its sub-basins (LZ, LS, NX, and PS). (<b>b</b>) Spatial distribution of the percentage contribution of HAs (%), with corresponding pie charts showing their relative impact in the YZR and its sub-basins.</p>
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<p>Comparison of model performance for LAI simulation with and without temporal effects in the YZR basin. (<b>a</b>) Spatial distribution of R<sup>2</sup> values for models without temporal effects, with a median R<sup>2</sup> of 0.81. (<b>b</b>) Spatial distribution of R<sup>2</sup> values for models with temporal effects, showing an improved median R<sup>2</sup> of 0.87. (<b>c</b>) Spatial distribution of the differences in R<sup>2</sup> between the two models. (<b>d</b>) Violin plots comparing R<sup>2</sup> values across the entire basin (YZR) and sub-basins (LZ, LS, NX, and PS).</p>
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<p>Land-use transitions in the YZR basin from 2001 to 2019. (<b>a</b>) Land use/cover maps for 2001, 2010, and 2019. The land-use classes shown in the figure were reclassified based on the MOD-IGBP classification system (<a href="#app1-remotesensing-17-00160" class="html-app">Table S4</a>). (<b>b</b>) Sankey diagram depicting overall land-use transitions in the YZR basin. (<b>c</b>–<b>f</b>) Regional Sankey diagrams for the sub-basins: LZ (<b>c</b>), LS (<b>d</b>), NX (<b>e</b>), and PS (<b>f</b>), visualizing land-use changes within each sub-region over time.</p>
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24 pages, 3278 KiB  
Review
Metallogenic Evolution Related to Mantle Delamination Under Northern Tunisia
by Nejib Jemmali, Fouad Souissi, Larbi Rddad, Emmanuel John Carranza and Guillermo Booth-Rea
Minerals 2025, 15(1), 31; https://doi.org/10.3390/min15010031 - 30 Dec 2024
Viewed by 669
Abstract
Mineralization processes in the Tell-Atlas of North Africa coincided with magmatism, extension, and lithospheric rejuvenation during the middle to late Miocene. This review examines the lead isotope compositions and Pb-Pb age dating of ore deposits in the region to elucidate the sources and [...] Read more.
Mineralization processes in the Tell-Atlas of North Africa coincided with magmatism, extension, and lithospheric rejuvenation during the middle to late Miocene. This review examines the lead isotope compositions and Pb-Pb age dating of ore deposits in the region to elucidate the sources and timing of mineralization events. The data reveal a predominantly radiogenic signature in the ores, indicating that the primary component is from a crustal source, with a contribution from the mantle. Pb-Pb age dating suggests the ranges of mineralization ages, with late Miocene events being particularly significant, coinciding with proposed sub-continental mantle delamination following subduction of the African lithosphere. In this context, polymetallic mineralizations formed related to felsic magmatism, hydrothermalism driven by extensional faults, resulting in the formation of Mississippi Valley-Type, and Sedimentary exhalative deposits within associated semi-grabens and diapirism. The correlation between orogenic extensional collapse, magmatism, and mineralization underscores the importance of understanding the specific geological context of ore formation. The detachment of subducted slabs and subsequent influx of hot asthenosphere play pivotal roles in creating conducive conditions for mineralization. This study sheds light on the intricate interplay between tectonic mechanisms, mantle-crust interactions, and mineralization events in the Tell-Atlas, offering insights for further exploration in the region. Full article
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<p>Tectonic sketch of the western Mediterranean basins and orogens. Modified from [<a href="#B31-minerals-15-00031" class="html-bibr">31</a>]. The box shows the Nappes zone location.</p>
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<p>Simplified geologic map of northern Tunisia (modified from [<a href="#B71-minerals-15-00031" class="html-bibr">71</a>,<a href="#B72-minerals-15-00031" class="html-bibr">72</a>,<a href="#B73-minerals-15-00031" class="html-bibr">73</a>]) with the distribution of ore deposits, magmatic rocks, and deep-seated faults. GHCS: Ghardimaou-Cap Serrat Fault. RKTF: Ras el Korane-Thibar Fault. ETF: El Alia-Teboursouk Fault. TEF: Tunis-Elles Fault. SD-DH-OB: Sidi Driss-Douahria-Oued Belif, JA-AA: Jebel Arja-Ain Allega, RR: Ras Rajel, OM: Oued Maden, FH: Fedj Hassene, AB-CH: Ain el Bey-Chouichia, SB-JH: Sidi Bouaouane-Jebel Hallouf, BZ: Bazina, SM; Semmene, JL-BA: Jalta-Bir Afou, JG: Jebel Ghozlane.</p>
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<p>Lead isotope compositions of galena from selected Tunisian Nappes zone ore deposits plotted on <sup>207</sup>Pb/<sup>204</sup>Pb vs. <sup>206</sup>Pb/<sup>204</sup>Pb and <sup>208</sup>Pb/<sup>204</sup>Pb vs. <sup>206</sup>Pb/<sup>204</sup>Pb diagrams, together with Nefza-La Galite-Algeria Neogene magmatic rocks [<a href="#B28-minerals-15-00031" class="html-bibr">28</a>,<a href="#B125-minerals-15-00031" class="html-bibr">125</a>], and Alboran sea volcanic rocks [<a href="#B126-minerals-15-00031" class="html-bibr">126</a>]. Sources of data for Tunisian ore deposits (see <a href="#app1-minerals-15-00031" class="html-app">Table S2</a>).</p>
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<p>Δγ–Δβ genetic classification diagram after [<a href="#B124-minerals-15-00031" class="html-bibr">124</a>] showing lead isotope composition of Nappes zone ores (1: mantle lead, 2: upper crustal lead, 3: mixed upper crustal and mantle lead-3a: magmatism, 3b: sedimentation, 4: chemical deposit lead, 5: submarine hydrothermal lead, 6: medium-high grade metamorphic lead, 7: hypometamorphic lower crustal lead, 8: orogenic belt lead, 9: ancient shale upper crustal lead, and 10: retrograde metamorphic lead) (Δβ = [β/βM(t) − 1] × 1000; Δγ = [γ/γM(t) − 1] × 1000; β = <sup>207</sup>Pb/<sup>204</sup>Pb; γ = <sup>208</sup>Pb/<sup>204</sup>Pb; βM(t) = 15.33; γM(t) = 37.47).</p>
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<p>Lead isotope compositions of galena from selected Tunisian Nappes zone ore deposits plotted on a <sup>207</sup>Pb/<sup>204</sup>Pb vs. <sup>206</sup>Pb/<sup>204</sup>Pb diagram for comparison together with the ores of Algerian Tell [<a href="#B131-minerals-15-00031" class="html-bibr">131</a>], Morocco Rif [<a href="#B132-minerals-15-00031" class="html-bibr">132</a>], and southeastern Spain [<a href="#B133-minerals-15-00031" class="html-bibr">133</a>].</p>
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<p>Scattergrams for the Nappes zone ore deposits, showing relationships between (<b>A</b>) <sup>207</sup>Pb/<sup>204</sup>Pb and μ, and (<b>B</b>) model age and μ. Model ages and μ were calculated using the equation of [<a href="#B133-minerals-15-00031" class="html-bibr">133</a>].</p>
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<p>Model age distribution of galena ores [<a href="#B24-minerals-15-00031" class="html-bibr">24</a>,<a href="#B26-minerals-15-00031" class="html-bibr">26</a>,<a href="#B27-minerals-15-00031" class="html-bibr">27</a>,<a href="#B28-minerals-15-00031" class="html-bibr">28</a>] and magmatic rocks [<a href="#B28-minerals-15-00031" class="html-bibr">28</a>,<a href="#B87-minerals-15-00031" class="html-bibr">87</a>,<a href="#B91-minerals-15-00031" class="html-bibr">91</a>,<a href="#B104-minerals-15-00031" class="html-bibr">104</a>] of the Nappes zone. Dotted line corresponds to the mean value. The solid line corresponds to the outlier.</p>
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<p>(<b>a</b>) Cartoon of the tectonic mechanisms driving lithospheric rejuvenation, crustal extension, magmatism, and associated metal endowment in Northern Tunisia and Algeria during the middle to late Miocene (<b>b</b>) Schematic cross-s ection 1-1’across Northern Algeria-Tunisia showing the driving mechanisms and tectonic setting of different ore deposits in the region at the time of deposition. Notice the North to South crustal thinning gradient, taken from the EGT’85 refraction seismic experiment (99), and the occurrence of magmatic outcrops and a high-velocity lower crustal domain that are present towards the North. NSA: North-South Axis. TA-STEP: Tunisian Atlas Step Fault. T-STEP: Tyrrhenian Step Fault. PR: Pantelleria rift. LAB: Lithosphere Asthenosphere Boundary.</p>
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18 pages, 11461 KiB  
Article
Identification and Geological Significance of Late Cambrian OIB-Type Volcanic Rocks in the Nailenggeledaban Area, Northern Yili Block
by Da Xu, Ming Cao, Meng Wang, Youxin Chen, Shaowei Zhao, Shengqiang Zhu, Tai Wen and Zhi’an Bao
Minerals 2025, 15(1), 7; https://doi.org/10.3390/min15010007 - 25 Dec 2024
Viewed by 365
Abstract
Paleozoic igneous rocks exposed in the northern Yili Block are thought to have resulted from the subduction of the North Tianshan oceanic crust. However, the exact timing of the transition of the northern margin of the Yili Block from a passive to an [...] Read more.
Paleozoic igneous rocks exposed in the northern Yili Block are thought to have resulted from the subduction of the North Tianshan oceanic crust. However, the exact timing of the transition of the northern margin of the Yili Block from a passive to an active continental margin remains unknown. In this paper, the petrological and geochemical features, zircon U-Pb chronology, Lu-Hf isotopes, and Sr-Nd isotopes of volcanic rocks in the Nailenggeledaban area on the northern margin of the Yili Block were studied. Zircon U-Pb dating results show that the crystallization ages of the volcanic rocks in the Nailenggeledaban area on the northern margin of the Yili Block are 491 ± 2 Ma and 500 ± 2 Ma, suggesting they were formed during the Late Cambrian. Geochemical features show that the volcanic rocks are alkaline basalts with rare earth and trace element distribution patterns similar to OIB, although they exhibit some degree of Zr and Hf depletion. The εHf(t) values of alkaline basalts in the Nailenggeledaban area at the northern Yili Block range from −3.48 to −1.00, with a TDM1 age of 1152 to 1263 Ma. The εNd(t) values range from −3.53 to −0.96, with a TDM1 age of 1471 to 2162 Ma. Combined with geochemical data, the alkaline basalt magma in the Nailenggeledaban area on the northern margin of the Yili Block may be derived from the Mesoproterozoic enriched lithospheric mantle. The composition of the mantle source area is potentially garnet lherzolite, and the magma appears to have been either unaffected or only minimally contaminated by crustal materials during the ascending process. On the basis of the research results of the Early Paleozoic tectonic evolution in the northern margin of the Yili Block, this paper proposes that the volcanic rocks in the Nailenggeledaban area, located on the northern margin of the Yili Block, were formed in a back-arc extensional environment resulting from the subduction of the North Tianshan Ocean (or Junggar Ocean) beneath the northern margin of the Yili Block during the Late Cambrian. Full article
(This article belongs to the Special Issue Geochronology and Geochemistry of Alkaline Rocks)
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<p>(<b>a</b>) Structural architecture of the Central Asian Orogenic Belt and (<b>b</b>) concise geological map outlining the Western Tianshan Orogen Belt in China (according to Gao et al., 2009a [<a href="#B21-minerals-15-00007" class="html-bibr">21</a>]).</p>
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<p>Location map of Late Cambrian basalt samples in the Nailenggeledaban area on the northern YB (modified after Dong et al., 2009 [<a href="#B36-minerals-15-00007" class="html-bibr">36</a>]).</p>
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<p>(<b>a</b>) TS21-02, (<b>b</b>) TS2207 field photos, and (<b>c</b>) TS21-02, (<b>d</b>) TS2207 microscope photos of basalts in the Nailenggeledaban area. Pl: plagioclase, Px: pyroxene.</p>
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<p>(<b>a</b>) Chondrite-normalized REE patterns and (<b>b</b>) space primitive mantle-normalized trace element (normalization values are from Sun et al., 1989 [<a href="#B48-minerals-15-00007" class="html-bibr">48</a>]).</p>
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<p>Zircon CL images of basalts (in the figure, the red solid circle represents the laser ablation position of zircon age, and the yellow dotted circle represents the analysis position of zircon Lu-Hf isotope).</p>
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<p>Zircon U-Pb age concordia diagrams of basalts in the Nailenggeledaban area.</p>
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<p>ε<sub>Hf</sub>(t)-t-plot for the zircon crystals of basalts in the Nailenggeledaban area.</p>
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<p>(<sup>87</sup>Sr/<sup>86</sup>Sr)<span class="html-italic"><sub>i</sub></span>-ε<sub>Nd</sub>(t) plot for the basalts in the Nailenggeledaban area (modified after Zimmer et al., 1995 [<a href="#B50-minerals-15-00007" class="html-bibr">50</a>]). DM: depleted mantle, MORB: Mid-Ocean Ridge Basalt, OIB: Ocean Island Basalt.</p>
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<p>(<b>a</b>) Nb/La-(Th/Nb)<sub>N</sub> diagram and (<b>b</b>) La/Ba-La/Nb diagram of basalt in the Nailenggeledaban area (base map according to Fitton et al., 1991, 1995 [<a href="#B57-minerals-15-00007" class="html-bibr">57</a>,<a href="#B58-minerals-15-00007" class="html-bibr">58</a>]). OIB: Ocean Island Basalt.</p>
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<p>Harker variation diagram of the basalts in Nailenggeledaban area.</p>
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<p>Sm/Yb-Sm diagram of basalt in the Nailenggeledaban area (the trends of the partial melting models are derived from Aldanmaz et al., 2000 [<a href="#B61-minerals-15-00007" class="html-bibr">61</a>]). DM: depleted mantle, PM: enriched mantle, N-MORB: Normal Mid-Ocean Ridge Basalt, E-MORB: Enriched Mid-Ocean Ridge Basalt.</p>
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<p>(<b>a</b>) Zr/Nb-La/Y diagram and (<b>b</b>) La/Ba-La/Nb diagram of basalts in the Nailenggeledaban area (modified after Xia et al., 2019 [<a href="#B62-minerals-15-00007" class="html-bibr">62</a>]). OIB: Ocean Island Basalt, MORB: Mid-Ocean Ridge Basalt, N-MORB: Normal Mid-Ocean Ridge Basalt.</p>
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<p>(<b>a</b>) Zr/Y-Zr/Nb diagram and (<b>b</b>) Zr/Y-Y/Nb-Zr/Nb diagram of basalts in the Naile-nggeledaban area (modified after Fodor et al., 1984 [<a href="#B63-minerals-15-00007" class="html-bibr">63</a>]).</p>
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<p>Construction environment discrimination diagram in basalts. (<b>a</b>) [<a href="#B75-minerals-15-00007" class="html-bibr">75</a>]: WPB: within-plate basalt; MORB: mid-ocean ridge basalt; VAB: volcanic arc basalt. (<b>b</b>) [<a href="#B76-minerals-15-00007" class="html-bibr">76</a>]: I: N-MORB area at the edge of plate divergence; II: basalt area at the edge of the convergent plate (II1: ocean island arc basalt; II2: continental margin island arc and continental margin volcanic arc basalt area); III: ocean island, seamount basalt area, and TMORB, E-MORB area; IV: continental intraplate basalt area (IV1: intracontinental rift valley and continental rift valley basalt area; IV2: intracontinental rift alkaline basalt area; IV3: continental extension zone (or initial rift) basalt area); V: mantle plume basalt area. (<b>c</b>) [<a href="#B77-minerals-15-00007" class="html-bibr">77</a>] (Cabanis and Lecolle, 1989). (<b>d</b>) [<a href="#B78-minerals-15-00007" class="html-bibr">78</a>]: AI, AII: in-plate alkaline basalt; AII, C: intraplate tholeiitic basalt, B: enriched mid-ocean ridge basalt, D: depleted mid-ocean ridge basalt; C, D: volcanic arc basalt.</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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<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>
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<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>
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<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>
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<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>
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<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>
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<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, 53722 KiB  
Article
Analysis of Characteristics and Main Controlling Factors of Shallow Geological Hazards in the Zhongsha Islands Region of the South China Sea
by Rui Wang, Yang Wang, Qunfang Ye and Yunzhong Zhang
J. Mar. Sci. Eng. 2024, 12(12), 2236; https://doi.org/10.3390/jmse12122236 - 5 Dec 2024
Viewed by 582
Abstract
This study utilized single-channel seismic, multi-channel seismic, and multibeam bathymetric data to examine the distribution and geomorphological background of geological hazards in the Zhongsha Islands region of the South China Sea. We elucidate the regional geological structure and its evolution while focusing on [...] Read more.
This study utilized single-channel seismic, multi-channel seismic, and multibeam bathymetric data to examine the distribution and geomorphological background of geological hazards in the Zhongsha Islands region of the South China Sea. We elucidate the regional geological structure and its evolution while focusing on the types and characteristics of submarine hazards since the Quaternary Period. By integrating geomorphological, tectonic, and sedimentary factors, we analyzed the primary drivers of shallow geological hazards in the region. Our findings reveal that seabed topography, tectonic activity, and sedimentary processes critically influence hazard formation, particularly in geomorphic units prone to disasters, such as submarine slopes and canyons. Igneous rocks in the region display medium-acid to medium-basic compositions, with notable developmental stages during the Himalayan and Yanshan periods. From the Paleogene to the Middle Miocene, tectonic activity intensified, significantly thinning the lithosphere. By the Middle Miocene, the crust stabilized into its present configuration, marking the formation of key tectonic units in the region. Multiple phases of sedimentary evolution, influenced by the Cenozoic tectonic movements, further contribute to the region’s susceptibility to geological hazards. Full article
(This article belongs to the Section Geological Oceanography)
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<p>Location and bathymetry of Zhongsha Bank and the location of multi-channel seismic lines (<b>a</b>). Tectonic background of the South China Sea and adjacent regions (modified from [<a href="#B37-jmse-12-02236" class="html-bibr">37</a>]). (<b>b</b>). Bathymetry of the South China Sea (modified from [<a href="#B38-jmse-12-02236" class="html-bibr">38</a>]). (<b>c</b>). Overview of the distribution of multi-channel seismic lines.</p>
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<p>Geomorphologic and geologic map of the Zhongsha Bank.</p>
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<p>Seismic stratigraphy of the Zhongsha Trough in the western of the Zhongsha Bank.</p>
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<p>Structural elements of the Zhongsha Bank based on seismic lines.</p>
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<p>Seismic profile across the southeastern slope of Langhua Bank. The rugged topography of T0 compared with the smooth surface of T1 is caused by creeping of the Quaternary sediments.</p>
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<p>Seismic profile across the western slope of the Zhongsha Bank. The incision at the outcrop of F2 may indicate subrecent tectonic movements, but most of F2 is onlap. Igneous rock could also be the continental basement of the Zhongsha Bank.</p>
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<p>Seismic profile of the Zhongsha Trough west of Zhongsha Bank. The map shows the outcrops of the volcanic and continental basement.</p>
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<p>Drowned atoll on a basement high in the area of the Yinli seamounts with onlapping sequences.</p>
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<p>Seismic line Zhongsha Trough west of Zhongsha Bank with partly buried volcanic edifices; note the pronounced reflectors of T3, probably indicating an intense volcanic productivity in the middle Miocene.</p>
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<p>Seismic line in Zhongshabei Ridge with volcanic intrusions consisting of cones, stocks, dykes, and sills. Volcanic activity at the end of the Upper Miocene may be documented by the sill at T2.</p>
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<p>Submarine slope (<b>a</b>) and direction (<b>b</b>) in the study area. The black outline in panel a shows the MTD distribution region in the study area.</p>
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<p>Slide and collapse seismic profile in the Zhongsha Trough area (the left side shows the location map of the seismic profile).</p>
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<p>Original and interpreted seismic line in the Zhongsha Scarp on the southeast side of the Zhongsha Bank; the buried volcanic edifices with sills are partly buried by a thin cover of sediment.</p>
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<p>Seismic line from the southeastern flank of Xisha and corresponding bathymetry. Massive slumps dominate both sides of the canyon shoulders.</p>
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<p>Seismic line from the Zhongsha Trough area with numerous faults observed in the sedimentary cover. The deformation is comparable to the creep structures in <a href="#jmse-12-02236-f016" class="html-fig">Figure 16</a>a.</p>
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<p>Two seismic profiles and bathymetric maps from the Zhongsha Scarp on the west side of the Zhongsha Bank.</p>
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<p>Seismic reflection characteristics in suspected hydrate zones in the southern part of the Zhongshabei Ridge.</p>
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18 pages, 14732 KiB  
Article
Atypical Linear Tectonic Block of the Intraplate Deformation Zone in the Central Indian Ocean Basin
by Vsevolod V. Yutsis, Oleg V. Levchenko, Alexander V. Tevelev, Yulia G. Marinova, Ilia A. Veklich and Abraham Del Razo Gonzalez
J. Mar. Sci. Eng. 2024, 12(12), 2231; https://doi.org/10.3390/jmse12122231 - 5 Dec 2024
Viewed by 570
Abstract
The Central Indian Ocean Basin (CIOB) is distinguished by unusually high tectonic activity, setting it apart from all other passive oceanic basins. Within the interior of the Indo-Australian lithospheric plate lies a unique area of intraplate deformation. This region is characterized by the [...] Read more.
The Central Indian Ocean Basin (CIOB) is distinguished by unusually high tectonic activity, setting it apart from all other passive oceanic basins. Within the interior of the Indo-Australian lithospheric plate lies a unique area of intraplate deformation. This region is characterized by the highest recorded intraplate oceanic seismicity, with earthquake magnitudes reaching up to M = 8, abnormally high heat flow—measured to be two to four times higher than background levels for the ancient oceanic lithosphere of the Cretaceous age—and, most notably, intense folding and faulting of sediments and the basement, which are typically associated only with boundary zones of lithospheric plates. This anomalously tectonically active intraplate area was studied during regular research cruises in the 1970s–1980s, after which new conclusions were mainly drawn from satellite data modeling. Substantially new geophysical data were obtained in 2017 after a long gap. Bathymetric surveys using multibeam echosounders during the 42nd cruise of the R/V (Research Vessel) Akademik Boris Petrov and the SO258/2 cruise of the R/V Sonne provided full coverage of a large portion of the intraplate deformation area in the CIOB. This confirmed the mosaic-block structure of the intraplate deformation zone in the Central Indian Ocean Basin, consisting of numerous isometrically deformed tectonic blocks. A linear block at 0.2–0.6° S, which has a branch-like shape in plain view, is morphologically distinct from these blocks. It represents a system of structural elements of different scales (folds, flexures, ruptures), which constitute a structural paragenesis formed in the mechanical environment of a dextral transpressive tectonic setting. Full article
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<p>General tectonic setting of the Central Indian Ocean Basin underlain by satellite-derived bathymetry (<b>a</b>). Bottom relief map of the CIOB according to [<a href="#B26-jmse-12-02231" class="html-bibr">26</a>], with the addition of new multibeam echo-sounder data from the 42nd cruise of the R/V “Akademik Boris Petrov [<a href="#B27-jmse-12-02231" class="html-bibr">27</a>] and from R/V Sonne SO258/2 [<a href="#B28-jmse-12-02231" class="html-bibr">28</a>]. The map shows the focal mechanisms of recent earthquakes with a magnitude greater than 5. Black and white beach-balls are thrusts; red and white—shear; and orange circles—mechanism undetermined [<a href="#B29-jmse-12-02231" class="html-bibr">29</a>,<a href="#B30-jmse-12-02231" class="html-bibr">30</a>,<a href="#B31-jmse-12-02231" class="html-bibr">31</a>]. Fracture zones (FZ) are shown by dash lines [<a href="#B32-jmse-12-02231" class="html-bibr">32</a>,<a href="#B33-jmse-12-02231" class="html-bibr">33</a>]. Magnetic anomalies are shown according to [<a href="#B32-jmse-12-02231" class="html-bibr">32</a>,<a href="#B34-jmse-12-02231" class="html-bibr">34</a>]—dark blue line is a 34 anomaly with an age of 83 Ma; violet line—33 anomaly with an age of 79 Ma [<a href="#B24-jmse-12-02231" class="html-bibr">24</a>]. ODP LEG 116 sites are marked by a red star [<a href="#B25-jmse-12-02231" class="html-bibr">25</a>]. ANS—Afanasy Nikitin seamount. The black box notes the study area shown in Figure (<b>b</b>). Black solid lines—seismic profiles (lines 1–6) from the SO258/2 cruise of the R/V Sonne [<a href="#B35-jmse-12-02231" class="html-bibr">35</a>]; black dot-dashed line—seismic profile from the 22nd cruise of the R/V “Professor Shtokman” [<a href="#B11-jmse-12-02231" class="html-bibr">11</a>,<a href="#B19-jmse-12-02231" class="html-bibr">19</a>].</p>
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<p>Geophysics of the Central Indian Ocean Basin. (<b>a</b>) Bottom topography; (<b>b</b>) Free air gravity; (<b>c</b>) Airy isostatic gravity; (<b>d</b>) Complete Bouguer Gravity Anomaly. The white rectangle indicates the position of the detailed survey polygon.</p>
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<p>Magnetic field of the Central Indian Ocean Basin. (<b>a</b>) Magnetic anomaly reduced to Ecuador; (<b>b</b>) Magnetic field tilt derivative. The white rectangle indicates the position of the detailed survey polygon.</p>
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<p>Bottom relief map (<b>a</b>) and 3D-model (<b>b</b>) of the atypical linear tectonic block “branch”. The location of the nearest earthquake epicenter is shown by the orange circle.</p>
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<p>The fragment of the seabed relief map of the Central Indian Ocean Basin (part of the “branch” polygon, Western domain of the Main Anticline). White lenses—tension cracks; white dashed lines—shear fractures; comb—normal faults (ticks directed towards the fault dip); τ—tangential stress.</p>
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<p>The fragment of the seabed relief map of the Central Indian Ocean Basin (part of the “branch” polygon, Central domain of the Main Anticline). The legend is the same as in <a href="#jmse-12-02231-f005" class="html-fig">Figure 5</a>.</p>
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<p>The fragment of the seabed relief map of the Central Indian Ocean Basin (part of the “branch” polygon, Eastern domain of the Main Anticline). White double triangles show the main compression direction. The other legend is the same as in <a href="#jmse-12-02231-f005" class="html-fig">Figure 5</a>.</p>
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<p>The fragment of the seabed relief map of the Central Indian Ocean Basin, polygon “branch”. Dash-dotted lines mark inclined faults (triangular ticks indicate dip direction), while dotted lines indicate fold axes. Y—general Riedel shift, P—secondary Riedel shears, T—extension fractures, R′—antithetic shears, a1–a11—folds, rf—reverse faults, uf—faults with actively sinking foot walls. For other symbols and signs, see <a href="#jmse-12-02231-f005" class="html-fig">Figure 5</a>.</p>
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<p>Seismic profile (initial and interpreted) of the 22nd cruise of the R/V “Professor Shtokman”; the line position is shown in <a href="#jmse-12-02231-f001" class="html-fig">Figure 1</a>b [<a href="#B11-jmse-12-02231" class="html-bibr">11</a>,<a href="#B19-jmse-12-02231" class="html-bibr">19</a>]. 1. AA—Early Pliocene unconformity; 2. A—Late Miocene unconformity corresponding to the main phase of intraplate deformation (~8 Ma); 3—Faults.</p>
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<p>Main seismic units (SU1, SU2, SU3) within the study area and around the oceanic drilling sites ODP 717−719 (location of line 2 is shown in <a href="#jmse-12-02231-f001" class="html-fig">Figure 1</a>b). In the inset, there is the correlation of the seismic section with the sediment density curve of site 717 according to [<a href="#B53-jmse-12-02231" class="html-bibr">53</a>]. The boundary between SU1 and SU2 is shown in orange; the boundary between SU2 and SU3 is shown in green; black short narrow—approximate position of site 717 on line 2.</p>
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<p>A 3D model of the studied area with a cross-section along line 4 (location of line 4 is shown in <a href="#jmse-12-02231-f001" class="html-fig">Figure 1</a>b) The plane marks the approximate position of the main shear zone. σ1—compressive stress; τ—shear stress; black arrows indicate a direction of the vertical displacement of blocks.</p>
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21 pages, 20991 KiB  
Article
Petrogenesis of Diorite-Porphyrite in the Southern Xintai Area of the Mid-Western Shandong Peninsula, North China Craton: Insights from Geochronology, Mineralogy, Geochemistry, and Sr-Nd-Hf Isotopes
by Lijie Jin, Jilin Wang, Pinrui Qin, Chunjia Li, Shuang Xu, Zhixin Han, Wei Wang, Wei Liu, Zisheng Wang, Jilei Gao and Fangfang Li
Minerals 2024, 14(12), 1220; https://doi.org/10.3390/min14121220 - 29 Nov 2024
Viewed by 526
Abstract
The Early Cretaceous intermediate intrusive rocks have important significance in understanding the crust–mantle interaction, iron mineralization, and tectonic evolution in the western Shandong Peninsula. In this study, we present new zircon U–Pb ages, and Hf isotope, whole-rock geochemistry, Sr–Nd isotopes, and the mineral [...] Read more.
The Early Cretaceous intermediate intrusive rocks have important significance in understanding the crust–mantle interaction, iron mineralization, and tectonic evolution in the western Shandong Peninsula. In this study, we present new zircon U–Pb ages, and Hf isotope, whole-rock geochemistry, Sr–Nd isotopes, and the mineral chemistry of the diorite-porphyrite in the southern Xintai area, mid-western Shandong Peninsula. The diorite-porphyrite formed at ca. 125 Ma. They have intermediate SiO2 (59.57–62.29 wt.%) and MgO (2.78–3.58 wt.%) contents, high Mg# values (53–56), high Sr (589–939 ppm) and low Y (9.2–10.8 ppm) contents, and high Sr/Y ratios (54–94), showing adakitic affinity. The diorite-porphyrite exhibits lower zircon εHf(t) values (−30.1 to 7.5) and whole-rock εNd(t) values (−3.5 to −6.0), with (87Sr/86Sr)i ratios of 0.70514–0.70567. We suggest that the diorite-porphyrite was derived from the partial melting of the local delamination of lower continental crust and then by the interaction with the enriched lithospheric mantle. The genesis of diorite-porphyrite may be related to the rollback process of the Paleo-Pacific slab in the Early Cretaceous. This geodynamic process induced the melting of the enriched lithospheric mantle, subducted oceanic crust, and local delamination of lower continental crust, which produced different types of adakitic magmatism in the western Shandong Peninsula. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Tectonic subdivision of the NCC (after [<a href="#B43-minerals-14-01220" class="html-bibr">43</a>]). (<b>b</b>) Simplified geological map of Shandong Peninsula (after [<a href="#B27-minerals-14-01220" class="html-bibr">27</a>]). (<b>c</b>) Geological sketch map of the southern Xintai area of the mid-western Shandong Peninsula.</p>
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<p>Representative photographs and photomicrographs of diorite-porphyrite in the Xintai area. (<b>a</b>) The diorite-porphyrite intruded into the Lower Cambrian Mantou Formation; (<b>b</b>,<b>c</b>) Field photographs of diorite-porphyrite; (<b>d</b>,<b>e</b>) SEM images of porphyritic texture in diorite-porphyrite; (<b>f</b>) Representative plagioclase phenocryst in cross-polarized light.</p>
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<p>Typical zircon CL images and zircon U–Pb concordia diagrams of diorite-porphyrite.</p>
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<p>Diagrams of (<b>a</b>) (K<sub>2</sub>O + Na<sub>2</sub>O) versus SiO<sub>2</sub> (TAS; [<a href="#B56-minerals-14-01220" class="html-bibr">56</a>]); (<b>b</b>) SiO<sub>2</sub> versus K<sub>2</sub>O [<a href="#B57-minerals-14-01220" class="html-bibr">57</a>]; A/CNK versus A/NK [<a href="#B58-minerals-14-01220" class="html-bibr">58</a>]. (<b>c</b>) Data sources: Adakitic rocks from Tiezhai [<a href="#B22-minerals-14-01220" class="html-bibr">22</a>]; Mengyin and Liujing [<a href="#B33-minerals-14-01220" class="html-bibr">33</a>]; Mafic intrusive rocks [<a href="#B11-minerals-14-01220" class="html-bibr">11</a>]; Laiwu ore-related intrusive rocks [<a href="#B55-minerals-14-01220" class="html-bibr">55</a>].</p>
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<p>Variation in (<b>a</b>) MgO, (<b>b</b>) Fe<sub>2</sub>O<sub>3</sub><sup>T</sup>, (<b>c</b>) CaO, (<b>d</b>) Na<sub>2</sub>O, (<b>e</b>) TiO<sub>2</sub>, (<b>f</b>) Al<sub>2</sub>O<sub>3</sub>, (<b>g</b>) K<sub>2</sub>O, and (<b>h</b>) P<sub>2</sub>O<sub>5</sub> versus SiO<sub>2</sub> for the samples. Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Chondrite-normalized REE patterns and (<b>b</b>) primitive mantle-normalized element spidergrams. The values used for normalization are adopted from [<a href="#B59-minerals-14-01220" class="html-bibr">59</a>]. Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>Diagram of (<sup>87</sup>Sr/<sup>86</sup>Sr)<sub>i</sub> versus ε<sub>Nd</sub>(t) values.</p>
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<p>Diagram of zircon <sup>206</sup>Pb/<sup>238</sup>U ages versus ε<sub>Hf</sub>(t) values of adakitic rocks, mafic intrusions/dykes and alkaline complex.</p>
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<p>Diagrams of (<b>a</b>) hornblende classification [<a href="#B64-minerals-14-01220" class="html-bibr">64</a>]; (<b>b</b>) hornblende equilibrium temperature versus equilibrium pressure; (<b>c</b>) plagioclase Or-Ab-An [<a href="#B65-minerals-14-01220" class="html-bibr">65</a>]; (<b>d</b>,<b>e</b>) variation in An and FeO values of plagioclase phenocrysts.</p>
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<p>Diagrams of (<b>a</b>) Ni versus MgO; (<b>b</b>) CaO/Al<sub>2</sub>O<sub>3</sub> versus MgO [<a href="#B27-minerals-14-01220" class="html-bibr">27</a>]. Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>Plots of (<b>a</b>) Sr/Y versus Y and (<b>b</b>) (La/Yb)<sub>N</sub> versus Yb<sub>N</sub> (after [<a href="#B68-minerals-14-01220" class="html-bibr">68</a>]). Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>Plots of (<b>a</b>) Mg# versus SiO<sub>2</sub> (after [<a href="#B80-minerals-14-01220" class="html-bibr">80</a>]), (<b>b</b>) Ni versus SiO<sub>2</sub>, and (<b>c</b>) Cr versus SiO<sub>2</sub> (after [<a href="#B81-minerals-14-01220" class="html-bibr">81</a>]). Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>Plots of (<b>a</b>) Ba versus Nb/Y, (<b>b</b>) Th/Yb versus Ba/La, (<b>c</b>) Ba/Y versus Nb/Y, (<b>d</b>) Th/Yb versus Sr/Nd (after [<a href="#B76-minerals-14-01220" class="html-bibr">76</a>]). Data sources are the same as <a href="#minerals-14-01220-f004" class="html-fig">Figure 4</a>.</p>
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<p>Schematic diagram of the evolution in the Early Cretaceous. Modified form [<a href="#B27-minerals-14-01220" class="html-bibr">27</a>].</p>
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15 pages, 10534 KiB  
Article
Genetic Type and Formation Evolution of Mantle-Derived Olivine in Ultramafic Xenolith of Damaping Basalt, Northern North China Block
by Cun Zhang, Fan Yang, Zengsheng Li, Leon Bagas, Lu Niu, Xinyi Zhu and Jianjun Li
Minerals 2024, 14(12), 1207; https://doi.org/10.3390/min14121207 - 27 Nov 2024
Viewed by 707
Abstract
Olivine in deep-seated ultramafic xenoliths beneath the North China Block serves as a crucial proxy for decoding the compositions, properties, and evolution of the lithospheric mantle. Here, we conduct an investigation on olivine (including gem-grade) hosted in ultramafic xenoliths from Damaping basalt in [...] Read more.
Olivine in deep-seated ultramafic xenoliths beneath the North China Block serves as a crucial proxy for decoding the compositions, properties, and evolution of the lithospheric mantle. Here, we conduct an investigation on olivine (including gem-grade) hosted in ultramafic xenoliths from Damaping basalt in the northern part of the North China Block. This contribution presents the results from petrographic, Raman spectroscopic, and major and trace elemental studies of olivine, with the aim of characterising the formation environment and genetic type of the olivine. The analysed olivine samples are characterised by high Mg# values (close to 91%) possessing refractory to fertile features and doublet bands with unit Raman spectra beams of 822 and 853 cm−1, which are indicative of a forsterite signature. Major and trace geochemistry of olivine indicates the presence of mantle xenolith olivine. All the analytical olivine assays ≤0.1 wt % CaO, ~40 wt % SiO2, and ≤0.05 wt % Al2O3. Furthermore, olivine displays significantly different concentrations of Ti, Y, Sc, V, Co, and Ni. The Ni/Co values in olivine range from 21.21 to 22.98, indicating that the crystallisation differentiation of basic magma relates to oceanic crust recycling. The V/Sc values in mantle/xenolith olivine vary from 0.54 to 2.64, indicating a more oxidised state of the mantle. Rare earth element (REE) patterns show that the LREEs and HREEs of olivine host obviously differentiated characteristics. The HREE enrichments of olivine and the LREE depletion of clinopyroxene further assert that the mantle in the Damaping area underwent partial melting. The wide variations of Mg# values in olivine and the Cr# values in clinopyroxene, along with major element geochemistry indicate transitional characteristics of different peridotite xenoliths. This is possibly indicative of a newly accreted lithospheric mantle interaction with an old lithospheric mantle at the time of the basaltic eruption during the Paleozoic to Cenozoic. Full article
(This article belongs to the Section Mineral Deposits)
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Figure 1

Figure 1
<p>Location of the Damaping region showing (<b>a</b>) the tectonic framework of the northern North China Block, showing different sub-divisions and the location of the study area; and (<b>b</b>) the geology of the Damaping olivine deposits (modified after Yu et al., 2005) [<a href="#B29-minerals-14-01207" class="html-bibr">29</a>].</p>
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<p>Representative field and sample photographs. (<b>a</b>) Damaping olivine open pit showing major volcanic rock; (<b>b</b>) basalt with deep-seated olivine; (<b>c</b>) fresh basalt without mantle xenoliths; (<b>d</b>) basalt with abundant olivine including gem-grade grains displaying different colours; (<b>e</b>) large bright green gem-grade olivine separated from xenoliths; and (<b>f</b>) green olivine with impurities (mainly magnetite) under binocular microscope. Note: the olivine (yellow circle in figure (<b>e</b>)) is magnified under a gem microscope.</p>
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<p>Representative photomicrographs under cross-polarised light showing (<b>a</b>–<b>d</b>) mineral assemblages of olivine samples. Mineral abbreviations: Ol—olivine; Cpx—clinopyroxene; Opx—orthopyroxene.</p>
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<p>Representative Raman spectra of olivine in the ultramafic xenolith from Damaping basalt, showing forsterite peaks.</p>
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<p>Geochemical diagrams showing the classification of pyroxenes (modified after Morimoto (1988) [<a href="#B38-minerals-14-01207" class="html-bibr">38</a>] and Zhang et al. (2023) [<a href="#B39-minerals-14-01207" class="html-bibr">39</a>]); two main types of pyroxene are identified based on the diagram, with red and purple icons indicating clinopyroxene and orthopyroxene, respectively.</p>
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<p>Plot of Mg# vs. SiO<sub>2</sub>, CaO, Na<sub>2</sub>O, and Al<sub>2</sub>O<sub>3</sub> for assays of the olivine samples. (<b>a</b>) Mg# vs. SiO<sub>2</sub> of olivine samples from mantle xenoliths. (<b>b</b>) Mg# vs. CaO of olivine from mantle xenoliths. (<b>c</b>) Mg# vs. Na<sub>2</sub>O of olivine from mantle xenoliths. (<b>d</b>) Mg# vs. Al<sub>2</sub>O<sub>3</sub> of olivine from mantle xenoliths.</p>
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<p>Normalised assays for the two types of olivine showing (<b>a</b>,<b>b</b>) chondrite-normalised REE patterns and primitive mantle-normalised trace element variation diagram in olivine; (<b>c</b>,<b>d</b>) chondrite-normalised REE patterns and primitive mantle-normalised trace element variation diagram in clinopyroxene (Cpx); (<b>e</b>,<b>f</b>) chondrite-normalised REE pattern and primitive mantle-normalised trace element variation diagram of the studied orthopyroxene (Opx). The geochemical data of upper crust are from Taylor and Mclennan (1995) [<a href="#B40-minerals-14-01207" class="html-bibr">40</a>], and of chondrite are from McDonough and Sun (1995) [<a href="#B41-minerals-14-01207" class="html-bibr">41</a>].</p>
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<p>(<b>a</b>) Mg# vs. MnO of olivine samples from mantle xenoliths. (<b>b</b>) Al<sub>2</sub>O<sub>3</sub> vs. Mg# of clinopyroxene from mantle xenoliths. (<b>c</b>) Al<sub>2</sub>O<sub>3</sub> vs. Na<sub>2</sub>O of clinopyroxene from mantle xenoliths. Previous data (olivine and clinopyroxene in websterite xenoliths from Hannuoba) are cited by Duan et al. (2022) [<a href="#B45-minerals-14-01207" class="html-bibr">45</a>]. The diagrams are plotted based on previous investigations [<a href="#B46-minerals-14-01207" class="html-bibr">46</a>,<a href="#B47-minerals-14-01207" class="html-bibr">47</a>,<a href="#B48-minerals-14-01207" class="html-bibr">48</a>,<a href="#B49-minerals-14-01207" class="html-bibr">49</a>]. (<b>d</b>) Mg# vs. Cr# values for clinopyroxene (Cpx). Published related geochemical data (Mg# and Cr#) are from Yu et al. (2006) [<a href="#B50-minerals-14-01207" class="html-bibr">50</a>].</p>
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