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36 pages, 2997 KiB  
Review
A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures
by Neda Godarzi and Farzad Hejazi
CivilEng 2025, 6(1), 3; https://doi.org/10.3390/civileng6010003 - 13 Jan 2025
Viewed by 101
Abstract
Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, [...] Read more.
Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices. Full article
(This article belongs to the Section Structural and Earthquake Engineering)
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<p>Various stages of this review’s structural health monitoring mechanism.</p>
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<p>Objectives of structural health monitoring.</p>
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<p>Structural Health Monitoring System’s components.</p>
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<p>Frequency Domain Analysis Detection process algorithm (FDD).</p>
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<p>(<b>a</b>) High-damping rubber bearing (HDRB) and (<b>b</b>) shear behavior in HDRB.</p>
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<p>The act of base isolation.</p>
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<p>Modal analysis.</p>
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<p>The steps of the model updating process in this review.</p>
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<p>A summary of the research steps in this literature review.</p>
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19 pages, 22002 KiB  
Article
Plio–Quaternary Tectonic Activity in the Northern Nubian Belts: The Main Driving Forces
by Enzo Mantovani, Marcello Viti, Daniele Babbucci and Caterina Tamburelli
Appl. Sci. 2025, 15(2), 587; https://doi.org/10.3390/app15020587 - 9 Jan 2025
Viewed by 323
Abstract
It is suggested that the occurrence of tectonic activity in the northern Nubian belts (Tell-Rif and Atlas systems) since the Late Pliocene can be interpreted as one of the processes that were produced in the central and western Mediterranean zones by the collision [...] Read more.
It is suggested that the occurrence of tectonic activity in the northern Nubian belts (Tell-Rif and Atlas systems) since the Late Pliocene can be interpreted as one of the processes that were produced in the central and western Mediterranean zones by the collision of the Adriatic continental promontory with the Anatolian–Aegean Tethyan system. Since then, the consumption of the residual low-buoyancy domains in the Mediterranean area was allowed by a major change in the plate mosaic and the related kinematics. The new tectonic setting started with the decoupling of a large portion of the Adriatic domain (Adria plate) from Nubia, through the formation of a long discontinuity crossing the Ionian domain (Victor Hensen–Medina fault) and the Hyblean–Pelagian domain (Sicily channel fault system). Once decoupled, the Adria plate underwent a clockwise rotation, at the expense of E–W shortening in the Hyblean–Pelagian domain and in the northern Nubian margin. The shortening in the Pelagian domain was accommodated by the northward escape of the Adventure wedge, which in turn caused the northward displacement of the eastern Maghrebian sector. The indentation of these structures into the Alpine–Apennine material lying east of the Corsica–Sardinia block induced an east to southeastward escape of wedges (southern Apennines and Calabria). This occured at the expense of the remnant Ionian Tethys oceanic domain and the thinned Adriatic margin. The extensional regime that developed in the wake of the migrating wedges led to the formation of the central and southern Tyrrhenian basins. In the northern Nubian belts, the westward push of the Adria–Hyblean–Pelagian domain has been accommodated by oroclinal bending, thrusting and uplifting across the Tell and Atlas belts. This geodynamic context might explain some features of the seismicity time pattern observed in the Tell system. Full article
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<p>Tectonic sketch of the central and western Mediterranean regions. (1) Continental Eurasian domains, (2) continental (a) and thinned continental (b) African/Adriatic domains, (3) Tethyan belt constituted by ophiolitic units (a) and metamorphic massifs (b), (4) other orogenic belts, (5) old oceanic domains, (6) Atlas belt, (7) zones affected by intense (a) or moderate (b) crustal thinning, and (8, 9, 10) compressional extensional and strike-slip tectonic features. Blue arrows indicate the present kinematic pattern with respect to Europe, mainly based on geodetic observations (e.g., [<a href="#B40-applsci-15-00587" class="html-bibr">40</a>,<a href="#B41-applsci-15-00587" class="html-bibr">41</a>]), considering that this kinematic pattern has been strongly influenced by the post-seismic relaxation triggered by the seismic sequence that developed along the north Anatolian fault since 1939 [<a href="#B9-applsci-15-00587" class="html-bibr">9</a>,<a href="#B42-applsci-15-00587" class="html-bibr">42</a>,<a href="#B43-applsci-15-00587" class="html-bibr">43</a>] (see <a href="#sec3-applsci-15-00587" class="html-sec">Section 3</a> for explanations). Ad = Adventure wedge, CA = Calabrian arc, Cam = Campidano graben, CS = Corsica–Sardinia block, ECA = external Calabrian arc, Ho = Hodna basin, Hy = Hyblean plateau, MM = Moroccan Meseta, MR = Mediterranean ridge, NC = north Constantine fault, Si = Sicily, SV = Schio-Vicenza fault, TFS = Transmoroccan fault system, VHM = Victor Hensen–Medina fault system.</p>
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<p>Neogenic evolution of the central and western Mediterranean regions. (<b>a</b>) Late Miocene, NT = northern Tyrrhenian. (<b>b</b>) Middle–Late Pliocene. Eg = Egadi fault, Ja = Jarrafa fault, LP = Libyan promontory, NAp = northern Apennines, SAp = southern Apennines, Sc = Sciacca fault, SCH = Sicily channel, SR = Scicli-Ragusa fault, SV = Schio-Vicenza fault, Tr = Tripolitania fault, Va = Vavilov basin, VHM = Victor Hensen–Medina fault. (<b>c</b>) Pleistocene. Au = Aures, CP = Calabria–Peloritani wedge, GK = Greater Kabylia, HA = High Atlas, HP = High Plateau, LK = Lesser Kabylia, Ma = Marsili basin, MR = Mediterranean ridge, Pa = Palinuro fault, SA = Saharan Atlas, TA = Tunisian Atlas, Ta = Taormina fault, TFS = Transmoroccan fault system. See the text for explanations. Arrows indicate the long-term kinematic pattern [<a href="#B9-applsci-15-00587" class="html-bibr">9</a>,<a href="#B11-applsci-15-00587" class="html-bibr">11</a>,<a href="#B72-applsci-15-00587" class="html-bibr">72</a>]. Colors, symbols and other abbreviations as in <a href="#applsci-15-00587-f001" class="html-fig">Figure 1</a>.</p>
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<p>Tentative reconstruction of the Plio–Quaternary deformation pattern of the northern Nubian belts, driven by the westward motion of the Pelagian domain and the north–northeastward motion of Nubia with respect to Eurasia (red arrows). (<b>a</b>) Early Pliocene. Al = Alboran wedge, Mo = Morocco block, TFS = Transmoroccan fault/fold system. (<b>b</b>) Late Pleistocene. Ad = Adventure wedge, MA = Middle Atlas, Ja = Jarrafa fault, Ho = Hodna basin, SCH = Sicily channel, TM = Tyrrhenian Maghrebides, Tr = Tripolitania fault. (<b>c</b>) Contouring of the main Atlas and Tell belts, in the morphological map of [<a href="#B83-applsci-15-00587" class="html-bibr">83</a>], considered in the evolutionary reconstruction of <a href="#applsci-15-00587-f002" class="html-fig">Figure 2</a> and <a href="#applsci-15-00587-f003" class="html-fig">Figure 3</a>. See the text for explanations. The red arrows show the motion of Nubia and the Adventure wedge with respect to Eurasia [<a href="#B9-applsci-15-00587" class="html-bibr">9</a>,<a href="#B72-applsci-15-00587" class="html-bibr">72</a>]. Symbols and colors as in <a href="#applsci-15-00587-f001" class="html-fig">Figure 1</a> and <a href="#applsci-15-00587-f002" class="html-fig">Figure 2</a>.</p>
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<p>Plate configuration and Pleistocene kinematic pattern in the Mediterranean region, with respect to Eurasia [<a href="#B10-applsci-15-00587" class="html-bibr">10</a>,<a href="#B72-applsci-15-00587" class="html-bibr">72</a>]. IBE, MOR and NUB indicate the Euler rotation poles of the Iberian (green), Moroccan (gray) and Nubian plates with respect to Eurasia. Blue, red and dark green arrows indicate the plate motions predicted by the IBE, MOR and NUB poles, respectively. The gray arrows in the Anatolian–Aegean system are inferred from geological evidence. Al = Alboran wedge, Ca = Canary Islands, Go = Gorringe bank.</p>
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<p>Distribution of major earthquakes (1600–2024) in the study area. Data from [<a href="#B130-applsci-15-00587" class="html-bibr">130</a>,<a href="#B131-applsci-15-00587" class="html-bibr">131</a>,<a href="#B132-applsci-15-00587" class="html-bibr">132</a>,<a href="#B133-applsci-15-00587" class="html-bibr">133</a>,<a href="#B134-applsci-15-00587" class="html-bibr">134</a>,<a href="#B135-applsci-15-00587" class="html-bibr">135</a>,<a href="#B136-applsci-15-00587" class="html-bibr">136</a>,<a href="#B137-applsci-15-00587" class="html-bibr">137</a>,<a href="#B138-applsci-15-00587" class="html-bibr">138</a>,<a href="#B139-applsci-15-00587" class="html-bibr">139</a>,<a href="#B140-applsci-15-00587" class="html-bibr">140</a>,<a href="#B141-applsci-15-00587" class="html-bibr">141</a>] and Researcher Institutions for Seismology (IRIS), available at <a href="https://ds.iris.edu/ieb/" target="_blank">https://ds.iris.edu/ieb/</a> accessed on 1 September 2024.</p>
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<p>Table of the main earthquakes (magnitude M ≥ 5.5) that occurred since 1700 in the Nubian zone shown in the map (blue polygon). The number in the last column indicates the respective reference: (1) [<a href="#B134-applsci-15-00587" class="html-bibr">134</a>], (2) [<a href="#B143-applsci-15-00587" class="html-bibr">143</a>], (3) [<a href="#B131-applsci-15-00587" class="html-bibr">131</a>], (4) [<a href="#B133-applsci-15-00587" class="html-bibr">133</a>], (5) Researcher Institutions for Seismology (IRIS), available at <a href="https://ds.iris.edu/ieb/" target="_blank">https://ds.iris.edu/ieb/</a> accessed on 1 September 2024, (6) [<a href="#B137-applsci-15-00587" class="html-bibr">137</a>], (7) [<a href="#B135-applsci-15-00587" class="html-bibr">135</a>], and (8) [<a href="#B140-applsci-15-00587" class="html-bibr">140</a>]. The histogram shows the time pattern of the number of earthquakes with M ≥ 5.5 in the decades since 1700.</p>
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14 pages, 7866 KiB  
Article
The First Seismic Imaging of the Holy Cross Fault in the Łysogóry Region, Poland
by Eslam Roshdy, Artur Marciniak, Rafał Szaniawski and Mariusz Majdański
Appl. Sci. 2025, 15(2), 511; https://doi.org/10.3390/app15020511 - 7 Jan 2025
Viewed by 459
Abstract
The Holy Cross Mountains represent an isolated outcrop of Palaeozoic rocks located in the Trans-European Suture Zone, which is the boundary between the Precambrian East European Craton and Phanerozoic mobile belts of South-Western Europe. Despite extensive structural history studies, high-resolution seismic profiling has [...] Read more.
The Holy Cross Mountains represent an isolated outcrop of Palaeozoic rocks located in the Trans-European Suture Zone, which is the boundary between the Precambrian East European Craton and Phanerozoic mobile belts of South-Western Europe. Despite extensive structural history studies, high-resolution seismic profiling has not been applied to this region until now. This research introduces near-surface seismic imaging of the Holy Cross Fault, separating two tectonic units of different stratigraphic and deformation history. In our study, we utilize a carefully designed weight drop source survey with 5 m shot and receiver spacing and 4.5 Hz geophones. The imaging technique, combining seismic reflection profiling and travel time tomography, reveals detailed fault geometries down to 400 m. Precise data processing, including static corrections and noise attenuation, significantly enhanced signal-to-noise ratio and seismic resolution. Furthermore, the paper discusses various fault imaging techniques with their shortcomings. The data reveal a complex network of intersecting fault strands, confirming general thrust fault geometry of the fault system, that align with the region’s tectonic evolution. These findings enhance understanding of the Holy Cross Mountains’ structural framework and provide valuable reference data for future studies of similar tectonic environments. Full article
(This article belongs to the Special Issue Earthquake Engineering and Seismic Risk)
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<p>(<b>A</b>) Tectonic map of Poland with marked Holy Cross Mountain region (HCM). (<b>B</b>) Geological map of the Holy Cross Mountains (after [<a href="#B22-applsci-15-00511" class="html-bibr">22</a>], modified). (<b>C</b>) Geological map of the study area. Red line shows the seismic profile crossing Holy Cross Fault (HCF).</p>
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<p>Stratigraphy of the Łysogóry Region (after [<a href="#B8-applsci-15-00511" class="html-bibr">8</a>], modified). The Upper Silurian-Lower Devonian units are based on geological maps. Abbreviation (BF: Bronkowice Fm., GPF: Góry Pieprzowe Fm., GS: graptolite shales, MGC: Miedziana Góra Conglomerate, RF: Rachtanka Fm., WF: Wisniówka Fm., WoF: Wojciechowice Fm).</p>
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<p>Overview of seismic acquisition setup. (<b>A</b>) shows a surface elevation map with the seismic line marked; (<b>B</b>) captures the field acquisition setup using the PEG-40 seismic impact source; and (<b>C</b>) provides a schematic representation of the two-deployment acquisition layout.</p>
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<p>Shot gathers illustrating various recorded waveform types with red lines indicating geometry integrity. The first breaks are easily visible at all offsets. Rich wavefield including S waves and surface waves is visible for the whole record.</p>
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<p>First break tomographic image of P-wave velocities (<b>top</b>) and detrended model showing perturbations from smoothed velocity field (<b>bottom</b>). The gray area is not covered by seismic rays. Transparent gray area is verified with a limited number of seismic rays.</p>
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<p>Comparison of Brute Stack (<b>A</b>) and Final Stack (<b>B</b>) with Corresponding Amplitude Spectra. Significant enhancements in data quality and amplitude spectrum can be observed in the final stack.</p>
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<p>The final reflection seismic image with marked recognized faults.</p>
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25 pages, 16213 KiB  
Article
Imaging Shallow Velocity Structure of an Inactive Fault by Airgun Seismic Source: A Case Study of Xiliushui Fault in Qiliang Mountain
by Manzhong Qin, Baichen Wu, Yi Wang, Xueyi Shang, Yuansheng Zhang, Xuzhou Liu, Xiao Guo, Rui Zou, Yahong Wang and Dianfeng Sun
Geosciences 2025, 15(1), 16; https://doi.org/10.3390/geosciences15010016 - 7 Jan 2025
Viewed by 296
Abstract
We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell’s [...] Read more.
We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell’s law of seismic ray propagation to determine a simplified ambient velocity model. Based on the flexible and precise spectral element method, we computed broadband synthetic seismograms for a shallow low-velocity fault zone (FZ) to model the direct P-wave travel time delay and incident angle of the wavefield near the FZ. The FZ extent range and boundaries were inverted by apparent travel time delays and amplification patterns across the fault. According to prior information on the properties of the direct P-waves, we could constrain the inverse modeling and conduct a grid search for the fault parameters. The velocity reduction between the FZ and host rock, along with the dip angle of the FZ, were also constrained by the P-wave travel time delay systematic analysis and incoming angle of the P-waves. We found that the Xiliushui fault has a 70~80 m-wide low-velocity fault damage zone in which the P-wave velocity is reduced to ~40% with respect to the host rock. The fault damage zone dips ~35°southwest and extends to ~165 m in depth. The repeatability and environment protection characteristics of the airgun seismic survey and the economic benefits of a limited number of instruments setting are prominent. Full article
(This article belongs to the Special Issue Geophysical Inversion)
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<p>The location of the Gansu Qilian Mountain active seismic source and Xiliushui fault zone. (<b>a</b>) In the monitoring area (blue box), the red star represents the location of the airgun and reservoir. In the topographic map of the window, red line represents Xiliushui fault. (<b>b</b>) A linear dense array with a length of 500 m across the Xiliushui fault; the fault (red line) is outcropped on the surface. (<b>c</b>) The strike and the dip of the fault are SE and SW from surface geology survey, respectively. The aerial view of reservoir and station location in the study area is shown in <a href="#geosciences-15-00016-f0A1" class="html-fig">Figure A1</a>.</p>
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<p>(<b>a</b>–<b>c</b>) The three-component observation waveforms (blue) of airgun seismic source excitation recorded at Station_N24 (24 m); the red traces are the stacked waveforms. (<b>d</b>) The frequency spectrum of station near the FZ recorded at Station_S20 (−20 m), N24 (24 m), and N60 (60 m).</p>
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<p>(<b>a</b>) The Z-component (blue line) and R-component (red line) observation waveforms in the bandpass 8–12 Hz of airgun seismic source excitation recorded at Station_S90 (−90 m), S60 (−60 m), S30 (−30 m), N00 (fault trace), N15 (15 m), N30 (30 m), N60 (60 m), N90 (90 m). (<b>b</b>) Particle motion of the waveform in the different color-shaded window in (<b>a</b>). The big point represents the end point of the particle motion.</p>
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<p>The multi-band (0–20 Hz) filtering (<b>a</b>–<b>c</b>) and corresponding amplitude envelope (<b>d</b>–<b>f</b>) of observation waveforms at representative station_S60, N24 and N60, where the raw wave represents observation stacked waveform from R-component, and the red dotted line indicates the arrival time of surface wave.</p>
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<p>The filter-stacked (<b>a</b>,<b>b</b>) observation waveforms in the Z- and R-component stations across the fault. The dotted line represents the arrival time of P-body wave (black dotted line) and surface wave (purple dotted line), and red dotted rectangle boxes represent the fault zone waveform observed by three-components stations close to the fault zone.</p>
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<p>(<b>a</b>) Fault zone trapped waves (FZTWs) following surface wave arrivals (along the line of the array) after being rotated clockwise to the fault-parallel component (45°). (<b>b</b>) Distributions of normalized peak ground velocities (PGVs; red dots) and root mean square (RMS) amplitudes (blue stars) of the surface waveforms shown in (<b>a</b>). The black curve represents the likelihood of FZTWs (i.e., the normalized multiplication of PGV and RMS values) and is used to identify FZTWs. The green bar outlines the stations with FZTWs.</p>
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<p>The spectral ratio results along the entire array obtained from horizontal component. The white dashed zone indicates high spectral ratio value.</p>
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<p>The cross-correlation method calculates the P-wave and surface wave, generated in R-component by airgun excitation. (<b>a</b>) represents the P-wave arrival time and (<b>b</b>) represents the surface wave arrival time. The yellow and blue curves represent the results obtained using different reference stations. Dotted line represents fitting regional velocity. P-wave velocity is 4.67 km/s and surface wave is 2.8 km/s as the regional maximal seismic wave velocity, which is the model velocity at the bottom. The green bar outlines the stations in fault zone. Arrival time (<b>c</b>) and period characteristics (<b>d</b>) of surface wave in R-component (<a href="#geosciences-15-00016-f005" class="html-fig">Figure 5</a>b) of all stations across the fault zone. The green bar outlines the stations in fault zone.</p>
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<p>A workflow showing the key steps in the seismic forward modeling method used in the study: forward modeling, CC value calculation, and velocity analysis. (<b>a</b>) Fault model in depth and width. (<b>b</b>) Snapshot of the wave propagation and the shot gathered from the same source. (<b>c</b>) Synthetic waveform figure. (<b>d</b>) The waveform of the source wavelet: Ricker with a frequency of 10 Hz.</p>
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<p>The misfit 2D maps of the dip angle and central location (horizontal shift value relative to the surface fault trace) of the FZ at the optimal values of velocity reduction ratio, width and depth. Trade-off between the dip angle and the fault zone central location can be observed.</p>
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<p>(<b>a</b>) P-wave arrival times curve of observations (green) and predictions (red) near the FZ and their modification value after detrending. (<b>b</b>) Delay time curve of observation and prediction after detrending and equal scaling in (<b>a</b>). (<b>c</b>) Observed P-wave incoming angle (blue dots) and P-wave incoming angle (green dots) of fault zone velocity model in (<b>a</b>).</p>
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<p>The location of the Gansu Qilian Mountain active seismic source (<b>a</b>), and the aerial view of reservoir and topography in the study area. (<b>a</b>) Regional geotectonic map; the green rectangle is the research area in <a href="#geosciences-15-00016-f001" class="html-fig">Figure 1</a>. HYF is Qilian-Haiyuan Fault; ATF is Altyn-Tagh Fault; KF is Kunlun Fault; XHF is Xianshuihe Fault. (<b>b</b>) The red circle represents airgun source, the red points represent stations in the array (white line).</p>
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<p>Flash snapshot in basic forward model. The red and blue represent wavefield; green represents array zone. (<b>a</b>–<b>f</b>) is the wave field snapshot at different source times.</p>
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<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting dip of FZ (±5°) based on optimal FZ’s parameter model. (<b>a</b>) dip: 35° + 5°, detrend NCC value of travel delay time is 0.8; (<b>b</b>) dip: 35°− 5°, detrend NCC value of travel delay time is 0.73.</p>
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<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting location of FZ core (±5 m) based on optimal FZ’s parameter model. (<b>a</b>) FZ core location: 25 m + 5 m, detrend NCC value of travel delay time is 0.58; (<b>b</b>) dip: 25 m − 5 m, detrend NCC value of travel delay time is 0.8.</p>
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<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting depth of FZ (±5 m) based on optimal FZ’s parameter model. (<b>a</b>) FZ’s depth: 165 m + 5 m, detrend NCC value of travel delay time is 0.76; (<b>b</b>) FZ’s depth: 165 m− 5 m, detrend NCC value of travel delay time is 0.77.</p>
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<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting velocity reduction of FZ (45%) based on optimal FZ parameter model. (<b>a</b>) FZ velocity reduction to 45% + 5%; detrend in NCC value of travel delay time is 0.75; (<b>b</b>) FZ velocity reduction to 45% − 5%; detrend in NCC value of travel delay time is 0.77.</p>
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14 pages, 14484 KiB  
Article
The Association Between Sand Body Distribution and Fault of Zhuhai Formation on the North Slope of Baiyun Sag, Pearl River Mouth Basin, China
by Geer Zhao, Rui Zhu, Zhenyu Si and Mengmeng Liu
Appl. Sci. 2025, 15(1), 412; https://doi.org/10.3390/app15010412 - 4 Jan 2025
Viewed by 429
Abstract
This paper is predominantly intended to explore the distribution rule of the sand body of the Zhuhai Formation on the north slope of the Baiyun Sag. The Zhuhai Formation was deposited during a rifting phase. Influenced by tectonic movements, the investigated area developed [...] Read more.
This paper is predominantly intended to explore the distribution rule of the sand body of the Zhuhai Formation on the north slope of the Baiyun Sag. The Zhuhai Formation was deposited during a rifting phase. Influenced by tectonic movements, the investigated area developed a set of contemporaneous normal faults extending in the near W-E direction. The formation of faults alters the palaeomorphology, exerting a certain influence on the distribution of sedimentary sand deposits. To clarify the correlation between faults and sand bodies will be advantageous for an even distribution of sand bodies in the Zhuhai Formation. This paper systematically integrates the results of previous research findings, drillcore logging and analysis, and 3D seismic data. The seismic sedimentology method is adopted to identify three types of fracture systems and four types of associations between the sand body distribution and faults in the investigated area. In line with the difference of the fault inclination and spatial relationship, faults can be divided into three types, namely, the graben-type, transition zone, and syntropy-type. Graben-type fault combinations exhibit the opposite dip. Syntropy-type fault combinations display the same dip. Transition zone faults intersect at a tiny angle. It is noteworthy that the existence of a fault will exert a certain influence on the sediment transport direction and distribution pattern. On the basis of the fault group classification, four associations between the sand body distribution and graben-type, transport-type, syntropy-ladder-type, and syntropy-lifting-type faults are identified by considering taking into account these base shape factors. The syntropy-ladder type is conducive to the extension of the sediment along the source direction. Both graben-type and syntropy-lifting-type faults can accumulate sediments. The transport type changes the direction of the sediment supply. Full article
(This article belongs to the Section Earth Sciences)
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<p>The structural position of the study area and the comprehensive column chart [<a href="#B37-applsci-15-00412" class="html-bibr">37</a>] ((<b>a</b>) geographical location map of the study area, (<b>b</b>) composite bar chart).</p>
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<p>Composite histogram of well BY-A.</p>
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<p>Distribution of faults of the Zhuhai Formation on the north slope of Baiyun Sag.</p>
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<p>Seismic section and sedimentary morphology of contemporaneous faults.</p>
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15 pages, 32385 KiB  
Technical Note
Aftershock Spatiotemporal Activity and Coseismic Slip Model of the 2022 Mw 6.7 Luding Earthquake: Fault Geometry Structures and Complex Rupture Characteristics
by Qibo Hu, Hongwei Liang, Hongyi Li, Xinjian Shan and Guohong Zhang
Remote Sens. 2025, 17(1), 70; https://doi.org/10.3390/rs17010070 - 28 Dec 2024
Viewed by 445
Abstract
On 5 September 2022, the moment magnitude (Mw) 6.7 Luding earthquake struck in the Xianshuihe Fault system on the eastern edge of the Tibet Plateau, illuminating the seismic gap in the Moxi segment. The fault system geometry and rupture process of this earthquake [...] Read more.
On 5 September 2022, the moment magnitude (Mw) 6.7 Luding earthquake struck in the Xianshuihe Fault system on the eastern edge of the Tibet Plateau, illuminating the seismic gap in the Moxi segment. The fault system geometry and rupture process of this earthquake are relatively complex. To better understand the underlying driving mechanisms, this study first uses the Interferometric Synthetic Aperture Radar (InSAR) technique to obtain static surface displacements, which are then combined with Global Positioning System (GPS) data to invert the coseismic slip distribution. A machine learning approach is applied to extract a high-quality aftershock catalog from the original seismic waveform data, enabling the analysis of the spatiotemporal characteristics of aftershock activity. The catalog is subsequently used for fault fitting to determine a reliable fault geometry. The coseismic slip is dominated by left-lateral strike-slip motion, distributed within a depth range of 0–15 km, with a maximum fault slip > 2 m. The relocated catalog contains 15,571 events. Aftershock activity is divided into four main seismic clusters, with two smaller clusters located to the north and south and four interval zones in between. The geometry of the five faults is fitted, revealing the complexity of the Xianshuihe Fault system. Additionally, the Luding earthquake did not fully rupture the Moxi segment. The unruptured areas to the north of the mainshock, as well as regions to the south near the Anninghe Fault, pose a potential seismic hazard. Full article
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Graphical abstract

Graphical abstract
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<p>Tectonic map of the Luding earthquake region. (<b>a</b>) The red star marks the mainshock epicenter in Luding, and the blue star marks the Kangding earthquake, while the green star indicates historical earthquakes with magnitudes ≥ 6.5 in the past 300 years. The colored triangles denote the durations recorded by broadband seismic stations during the study period. The violet squares represent cities, and the black dots indicate the distribution of detected seismic activity. (<b>b</b>) The blue arrow represents the horizontal displacement derived from the Global Positioning System (GPS). Blue dots indicate GPS stations. The inset map indicates the regional tectonics.</p>
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<p>Phase picking and association. (<b>a</b>) An example of earthquake association using USTC-Pickers to determine the arrival times of the P-wave (blue line) and S-wave (red line). Only the vertical component of the waveform is displayed, and it is band-pass filtered in the frequency range of 1–10 Hz; (<b>b</b>) travel time–hypocentral distance curves for the associated events. The black line represents the fitted approximate velocities of the P-wave (6.0 km/s) and S-wave (3.5 km/s).</p>
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<p>Comparison between the detected catalog and the manual catalog and the distribution of seismic release energy. (<b>a</b>) Distribution of magnitude differences between coexisting events after magnitude correction for both catalogs. (<b>b</b>) Characteristics of the magnitude–frequency distribution for the manual and detected catalogs. The red bars represent events identified by machine learning, while the blue bars correspond to events identified manually. (<b>c</b>) Distribution of seismic energy, with a grid size of 0.01° × 0.01°. The red star marks the location of the mainshock.</p>
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<p>Sentinel-1 interferograms and line-of-sight (LOS) displacements from descending track. (<b>a</b>,<b>b</b>) The red stars indicate the location of the Luding earthquake.</p>
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<p>Spatial distribution of aftershocks at different times. (<b>a</b>–<b>e</b>) Aftershock activity is shown by colored dots, with the color representing depth. The red star is the location of the mainshock. The black circles indicate divided aftershock groups (S1–S4), and the black lines divide aftershock gaps (G1–G4). (<b>f</b>) Spatial–temporal aftershock evolution. The solid black line shows the direction of expansion of seismic activity over time.</p>
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<p>Regional fault profile. (<b>a</b>) The dashed blue line indicates the cross-section profiles, the red star indicates the location of the mainshock, and the black triangle indicates the Gongga Mountain. The red surface indicates the fault plane used to invert the coseismic slip. (<b>b</b>–<b>k</b>) The solid black line indicates the fault fit, and the parameters are given in the text. The dashed black line indicates the trend of changes in the depth of seismic activity. The time in hours until aftershocks occur is indicated by the color.</p>
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<p>Coseismic slip model and shear stress changes for the Luding earthquake. (<b>a</b>) The blue arrows indicate the direction of the slip, with the length representing the amount of slippage. The gray dashed lines represent contours of equal slippage. (<b>b</b>) The red star indicates the epicenter, and the red beach ball indicates the source mechanism solution.</p>
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<p>The relationship between coseismic slip and the distribution of aftershocks at different times. (<b>a</b>–<b>f</b>) The steel-blue dots indicate aftershocks; The gray dashed lines represent contours of equal slippage;Red star indicates the location of the Luding earthquake.</p>
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<p>A 3D resistivity structure depth slice. (<b>a</b>) Black dots indicate aftershocks, and red stars indicate the mainshock. The white arrow indicates the stress direction derived from the inversion of the source mechanism. (<b>b</b>–<b>f</b>) Cross-sections of resistivity structure.</p>
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17 pages, 15511 KiB  
Article
Light Oil Reservoir Source and Filling Stage in the Chepaizi Uplift, Junggar Basin Evidence from Fluid Inclusions and Organic Geochemistry
by Hongjun Liu, Pengying He and Zhihuan Zhang
Processes 2025, 13(1), 24; https://doi.org/10.3390/pr13010024 - 26 Dec 2024
Viewed by 180
Abstract
The light oil wells within the Neogene Shawan Formation have been extensively drilled in the Chepaizi Uplift, reflecting an increase that provides new targets for unconventional resources in the Junggar Basin of northwestern China. However, the original sources of light oil remain controversial, [...] Read more.
The light oil wells within the Neogene Shawan Formation have been extensively drilled in the Chepaizi Uplift, reflecting an increase that provides new targets for unconventional resources in the Junggar Basin of northwestern China. However, the original sources of light oil remain controversial, as several source rocks could potentially generate the oil. For this study, we collected light oils and sandstone cores for biomarker detection using gas chromatography–mass spectrometry (GC-MS). Additionally, fluid inclusions were observed and described, and the homogenization temperatures of saltwater inclusions were measured to confirm the oil charging history in conjunction with well burial and thermal history analysis. Based on these geochemical characteristics and carbon isotopic analysis, the results indicate that light oil in the Chepaizi Uplift zone primarily originates from Jurassic hydrocarbon source rocks in the Sikeshu depression, with some contribution from Cretaceous hydrocarbon source rocks. Jurassic hydrocarbon source rocks reached a peak of hydrocarbon generation in the middle to late Neogene. The resulting crude oil predominantly migrated along unconformities or faults to accumulate at the bottom of the Cretaceous or Tertiary Shawan Formation, forming anticlinal or lithologic oil reservoirs. Some oil reservoirs contain mixtures of Cretaceous immature crude oil. During the Neogene light oil accumulation process, the burial rate of reservoirs was high, and the efficiency of charging and hydrocarbon supply was relatively high as well. Minimal loss occurred during the migration of light oil, which significantly contributed to its rapid accumulation. Full article
(This article belongs to the Section Energy Systems)
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<p>The location of Chepaizi Uplift and drilled wells. (<b>a</b>) the location of Chepaizi Uplift in China, (<b>b</b>) the geological map and well locations in Chepaizi Uplift.</p>
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<p>The stratigraphy column of the well Pai 2 in the Chepaizi Uplift.</p>
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<p>GC–MS chromatography of saturated hydrocarbons from sandstone extracts or light oil in Pai 2, Pai 8, and Pai 206 wells.</p>
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<p>GC–MS chromatography of saturated hydrocarbons from the crude oil in the Ka 6 well.</p>
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<p>Liquid fluid inclusions in the Pai 2-86 well, 1370 m. (<b>a</b>,<b>b</b>) The intergranular pores of the sandstone are filled with light crude oil, all displaying strong light green or light yellow-green fluorescence. (<b>c</b>,<b>d</b>) Distributed along the enlargement rims of quartz grains are gas hydrocarbon inclusions, appearing gray, and exhibiting weak circular light green fluorescence. Along the intergranular (rock fragment) pores of the sandstone, there is a widespread presence of light crude oil, displaying strong light green or light yellow-green fluorescence. Locally, noticeable secondary quartz enlargement phenomena are observed. The abundance of fluid inclusions in the rock is low (GOI ≤ 0.5%), primarily distributed in linear or belt-like patterns along micro-fractures cutting through quartz or feldspar grains or in belt-like patterns around quartz enlargement rims. Liquid hydrocarbons within the inclusions appear transparent or gray-brown and exhibit light green fluorescence, while gaseous hydrocarbons appear gray and do not fluoresce. Among these inclusions, liquid hydrocarbon inclusions constitute approximately 10% ±, gas–liquid hydrocarbon inclusions constitute approximately 60% ±, and gas hydrocarbon inclusions constitute approximately 30% ± of the total.</p>
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<p>Gas–liquid hydrocarbon inclusions in the Pai 206-12 well, 1370 m. (<b>a</b>,<b>b</b>) In the intergranular calcite cement of the sandstone, there are isolated gas–liquid hydrocarbon inclusions distributed, showing transparent colorless to gray, exhibiting light green fluorescence (gas–liquid ratio approximately 5%). (<b>c</b>,<b>d</b>) Distributed along the micro-fracture surfaces of quartz grains are gas–liquid hydrocarbon inclusions, appearing transparent colorless to gray, exhibiting light blue fluorescence. The majority of intergranular pores within the sandstone contain light crude oil, displaying strong light blue fluorescence. The abundance of fluid inclusions within the rock is extremely high (GOI is 35% ±), with inclusions distributed in sheet-like or belt-like patterns along micro-fractures cutting through quartz and feldspar grains or occurring as isolated or clustered within late-stage calcite cement. Liquid hydrocarbons within the inclusions appear transparent, colorless to pale yellow, exhibiting light blue, light blue-green, and light yellow fluorescence, while gaseous hydrocarbons appear gray. Among these inclusions, liquid hydrocarbon inclusions constitute approximately 15% ±, gas–liquid hydrocarbon inclusions constitute approximately 70% ±, and gas hydrocarbon inclusions constitute approximately 15% ± of the total.</p>
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<p>Gas inclusions in the Pai 206 well, 1006.4 m. (<b>a</b>) Distributed along the micro-fractures cutting through quartz grains are gas hydrocarbon inclusions, liquid hydrocarbon inclusions, and hydrocarbon-bearing brine inclusions, appearing gray, gray-yellow, and transparent colorless. (<b>b</b>) Liquid hydrocarbon inclusions exhibit light yellow fluorescence. One sample was analyzed, with a weight of 5 × 6 µm, from another grain.</p>
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<p>Sican 1 well: (<b>a</b>) burial and thermal history of formations; and (<b>b</b>) deposition rate of formations.</p>
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<p>Ka6 Well: (<b>a</b>) burial and thermal history of formations; and (<b>b</b>) deposition rate of formations.</p>
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<p>Burial and thermal history of formations in well Pai 2-86 (<b>a</b>) and Pai 206 (<b>b</b>).</p>
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<p>Light oil accumulation pathways in the Chepaizi Uplift.</p>
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22 pages, 6291 KiB  
Article
Origin of the Miaoling Gold Deposit, Xiong’ershan District, China: Findings Based on the Trace Element Characteristics and Sulfur Isotope Compositions of Pyrite
by Simo Chen, Junqiang Xu, Yanchen Yang, Shijiong Han, Peichao Ding, Zhaoyang Song, Tianwen Chen and Daixin Zhang
Minerals 2025, 15(1), 6; https://doi.org/10.3390/min15010006 - 24 Dec 2024
Viewed by 328
Abstract
The Xiong’ershan district is situated on the southern margin of the North China Craton (NCC) and located within the Qinling–Dabieshan Orogen’s orogenic zone. It is adjacent to the XiaoQinling mining district and exhibits very favorable geological conditions for mineralization, as the district contains [...] Read more.
The Xiong’ershan district is situated on the southern margin of the North China Craton (NCC) and located within the Qinling–Dabieshan Orogen’s orogenic zone. It is adjacent to the XiaoQinling mining district and exhibits very favorable geological conditions for mineralization, as the district contains numerous gold deposits, positioning it as one of the key gold-producing areas of China. The Miaoling gold deposit is a hydrothermal deposit and is controlled by the Mesozoic nearly NS-trending fault. The ore bodies are hosted in the Mesoproterozoic Xiong’er Group of the Changcheng System of volcanic rocks, with reserves reaching large-scale levels. Pyrite is the main gold-bearing mineral and can be classified into four generations: early-stage fine- to medium-grained euhedral to subhedral cubic pyrite (Py1); medium- to coarse-grained euhedral to subhedral cubic granular pyrite in quartz veins (Py2a); fine-grained subhedral to anhedral disseminated pyrite in altered rocks (Py2b); and late-stage anhedral granular and fine-veinlet pyrite in later quartz veins (Py3). Through in situ trace element analysis of the pyrite using LA-ICP-MS, a positive correlation between Au and As was observed during the main mineralization stage; gold mainly exists as a solid solution within the pyrite lattice, and the ablation signal curve reflecting the intensity of trace element signals showed that gold also occurs as micron-scale mineral inclusions. The trace element content suggested a gradual increase in oxygen fugacity from Stage 1 to Stage 2, followed by a decrease from Stage 2 to Stage 3. The Co/Ni values in the pyrite (0.56 to 62.02, with an average of 12.34) exhibited characteristics of magmatic hydrothermal pyrite. The in situ sulfur isotope analysis of the pyrite using LA-MC-ICP-MS showed δ34S values of 4.24‰ for Stage 1, −6.63‰ to −13.79‰ for Stage 2, and −4.31‰ to −5.15‰ for Stage 3. Considering sulfur isotope fractionation, the δ34S value of the hydrothermal fluid during the main mineralization stage was calculated to be between 0.31‰ and 2.68‰. Full article
(This article belongs to the Special Issue The Formation and Evolution of Gold Deposits in China)
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<p>(<b>a</b>) Map of the distribution and locations of plates in China [<a href="#B25-minerals-15-00006" class="html-bibr">25</a>]. (<b>b</b>) Geological map of the southern margin of the NCC [<a href="#B5-minerals-15-00006" class="html-bibr">5</a>]. (<b>c</b>) Regional geological map of the Xiong’ershan district [<a href="#B7-minerals-15-00006" class="html-bibr">7</a>].</p>
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<p>(<b>a</b>) Geological diagram of Miaoling gold deposit [<a href="#B12-minerals-15-00006" class="html-bibr">12</a>]. (<b>b</b>) Projection of ore body in Miaoling gold deposit [<a href="#B39-minerals-15-00006" class="html-bibr">39</a>]. (<b>c</b>) Exploration line profile of Miaoling gold deposit [<a href="#B40-minerals-15-00006" class="html-bibr">40</a>].</p>
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<p>(<b>a</b>) F8 fault structure alteration zone. (<b>b</b>,<b>c</b>) Primary ore body in the F8 fault.</p>
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<p>Field and underground photos of quartz veins and altered rocks in four stages of the Miaoling gold deposit: (<b>a</b>) Stage 1 quartz vein; (<b>b</b>) two stages of crossing–cutting quartz veins; (<b>c</b>) Stage 2 quartz vein and pyrite; (<b>d</b>) pyrite in altered rocks; (<b>e</b>) Stage 3 quartz–polymetallic sulfide vein; (<b>f</b>) specularite vein; (<b>g</b>) Stage 4 quartz–fluorite vein; (<b>h</b>) carbonate vein; (<b>i</b>) epidotization; Py—pyrite; Py2a—pyrite in quartz vein; Py2b—pyrite in altered rocks; Ccp—chalcopyrite; Sp—sphalerite; Gn—galena; Spe—specularite; Qtz—quartz; Fl—fluorite; Cal—calcite; Ep—epidotization.</p>
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<p>An overview of the paragenetic sequence from the four stages of mineralization in the Miaoling gold deposit.</p>
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<p>Microscopic photos of pyrite in Miaoling gold deposit: (<b>a</b>,<b>b</b>) pyrite in Stage 1 quartz vein; (<b>c</b>–<b>d</b>) pyrite in Stage 2 quartz vein; (<b>e</b>–<b>f</b>) pyrite in altered rocks; (g) pyrite in Stage 3 quartz vein; (<b>h</b>–<b>i</b>) polymetallic sulfide in Stage 3 quartz vein. Py—pyrite; Gn—galena; Ccp—chalcopyrite; Sp—sphalerite.</p>
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<p>Box and whisker plots of trace element concentrations in pyrite, acquired by means of LA-ICP-MS analysis, from the Miaoling gold deposit.</p>
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<p></p>
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<p></p>
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<p>(<b>a</b>) A histogram of the sulfur isotopic compositions of sulfides from the Miaoling gold deposit. (<b>b</b>) The ranges of δ<sup>34</sup>S values in the Miaoling gold deposit and other deposits [<a href="#B5-minerals-15-00006" class="html-bibr">5</a>,<a href="#B12-minerals-15-00006" class="html-bibr">12</a>,<a href="#B84-minerals-15-00006" class="html-bibr">84</a>,<a href="#B87-minerals-15-00006" class="html-bibr">87</a>,<a href="#B88-minerals-15-00006" class="html-bibr">88</a>,<a href="#B91-minerals-15-00006" class="html-bibr">91</a>].</p>
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16 pages, 14248 KiB  
Article
Holocene Activity Characteristics and Seismic Risk of Major Earthquakes in the Middle Segment of the Jinshajiang Fault Zone, East of the Qinghai–Tibetan Plateau
by Mingjian Liang, Naifei Luo, Yunxi Dong, Ling Tan, Jinrong Su and Weiwei Wu
Appl. Sci. 2025, 15(1), 9; https://doi.org/10.3390/app15010009 - 24 Dec 2024
Viewed by 318
Abstract
The Jinshajiang fault zone is the western boundary fault of the Sichuan–Yunnan block, located east of the Qinghai–Tibetan Plateau. It is a complex tectonic suture belt with multi-phase activity and is characterized by multiple sets of parallel or intersecting faults. Using high-resolution image [...] Read more.
The Jinshajiang fault zone is the western boundary fault of the Sichuan–Yunnan block, located east of the Qinghai–Tibetan Plateau. It is a complex tectonic suture belt with multi-phase activity and is characterized by multiple sets of parallel or intersecting faults. Using high-resolution image interpretation, seismic geological surveys, and trench studies, we examined the Holocene activity and obtained the paleoseismic sequences on the middle segment of the fault zone. Thus, we could analyze the kinematic characteristics of the fault and its potential risk of strong earthquakes. Our results indicated that the predominant movement of the fault zone was strike-slip motion. In the Jinshajiang fault zone, the Late Quaternary horizontal slip rates of the north-northeast-trending Yarigong fault and the northeast-trending Ciwu fault were 3.6 ± 0.6 mm/a and 2.5 ± 0.5 mm/a, respectively. Three paleoseismic events were identified on the Yarigong fault, dated 6745–3848, 3742–1899, and 1494–1112 cal BP, and on the Ciwu fault, constrained to 32,566–29,430, 24,056–22,990, and 2875–2723 cal BP. The last major earthquake on the Ciwu fault occurred approximately 2800 years ago; therefore, its future seismic hazard deserves attention. Full article
(This article belongs to the Special Issue Paleoseismology and Disaster Prevention)
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<p>Tectonic framework and seismic activity of the Jinshajiang fault zone and adjacent regions. The black dashed rectangle represents the study area.</p>
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<p>(<b>a</b>) Distribution map of major faults and field survey sites in the middle section of the Jinshajiang fault zone and its adjacent areas. (<b>b</b>) On the basis of the GF-7 satellite data, a hillshade map was generated to interpret the detailed fault tracks of the Yarigong and Ciwu faults.</p>
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<p>(<b>a</b>,<b>b</b>) Geological profile of the Jinshajiang fault zone in Hongdong Village. (<b>c</b>) Photograph of the top of the geological section showing the fault displaced top strata.</p>
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<p>Right-lateral offset of a gully and profiles of the dating sample collection south of Yarigong Town. (<b>a</b>,<b>b</b>) The right-lateral offset of the gully was obtained using unmanned aerial vehicle (UAV) photogrammetry. The white rectangle in panel (<b>b</b>) indicates the sampling location of the dating samples. (<b>c</b>,<b>d</b>) The dating samples were collected from the T3 and T4 terraces of the Muqu River, respectively.</p>
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<p>Fault profiles exposed along the Yarigong fault. (<b>a</b>–<b>c</b>) The fault profiles are exposed north of the Ran, Dalong, and Lide villages, respectively, where the fault has displaced Late Quaternary strata. In (<b>a</b>), the fault has displaced the Holocene alluvial layer. And the fault has displaced the late Pleistocene alluvial layer in (<b>b</b>). The red arrows indicate the fault traces.</p>
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<p>Photograph of the fault profile and its explanatory profile.</p>
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<p>Structural landform of the Ciwu fault southwest of Nidou Village. (<b>a</b>) Fault track of the newest activity of the Ciwu fault on the alluvial fan. (<b>b</b>,<b>c</b>) Linear fault trough gullies and reverse fault scarps. The red arrows indicate the fault traces.</p>
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<p>(<b>a</b>) Right-lateral offset of the T2 terrace of the Ciwu River and location of the dating sample. The image was obtained using UAV photogrammetry. (<b>b</b>) Photograph showing the dating sample collection section in the T2 terrace of the Ciwu River.</p>
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<p>(<b>a</b>) Tectonic landform near trench TC1 according to UAV photogrammetry. (<b>b</b>) Photograph of the linear fault trough landform. The red arrows indicate the fault trace.</p>
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<p>Photograph of the southern wall of the Bugge trench and explanatory profile. The radiocarbon dating sample ages of the TC1 trench are detailed in <a href="#applsci-15-00009-t002" class="html-table">Table 2</a>.</p>
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<p>(<b>a</b>) Tectonic landform near trench TC2 shown via UAV photogrammetry. The gully to the south of the site is right-laterally offset by approximately 18 ± 2 m. (<b>b</b>) Photograph of the trench site.</p>
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<p>Photograph of the southern wall of the Bugge trench and explanatory profile. The radiocarbon dating sample ages of the TC2 trench are detailed in <a href="#applsci-15-00009-t002" class="html-table">Table 2</a>.</p>
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<p>Paleoseismic sequences of the Yarigong and Ciwu faults, the raw carbon ages of which were calibrated using OxCal 4.4.4 [<a href="#B42-applsci-15-00009" class="html-bibr">42</a>]. (<b>a</b>,<b>b</b>) are the paleoseismic sequences revealed in trench TC1 and TC2, respectively.</p>
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19 pages, 32077 KiB  
Article
Present-Day Tectonic Deformation Characteristics of the Northeastern Pamir Margin Constrained by InSAR and GPS Observations
by Junjie Zhang, Xiaogang Song, Donglin Wu and Xinjian Shan
Remote Sens. 2024, 16(24), 4771; https://doi.org/10.3390/rs16244771 - 21 Dec 2024
Viewed by 423
Abstract
The Pamir is located on the northwestern margin of the Tibetan Plateau, which is an area of intense continental deformation and part of the famous India–Himalaya collision zone. The dominant structural deformation in the eastern Pamir is characterized by a 250 km long [...] Read more.
The Pamir is located on the northwestern margin of the Tibetan Plateau, which is an area of intense continental deformation and part of the famous India–Himalaya collision zone. The dominant structural deformation in the eastern Pamir is characterized by a 250 km long east–west extensional fault system, known as the Kongur Shan extensional system (KSES), which has developed a series of faults with different orientations and characteristics, resulting in highly complex structural deformation and lacking sufficient geodetic constraints. We collected Sentinel-1 SAR data from December 2016 to March 2023, obtained high-resolution ascending and descending LOS velocities and 3D deformation fields, and combined them with GPS data to constrain the current motion characteristics of the northeastern Pamirs for the first time. Based on the two-dimensional screw dislocation model and using the Bayesian Markov chain Monte Carlo (MCMC) inversion method, the kinematic parameters of the fault were calculated, revealing the fault kinematic characteristics in this region. Our results demonstrate that the present-day deformation of the KSES is dominated by nearly E–W extension, with maximum extensional motion concentrated in its central segment, reaching peak extension rates of ~7.59 mm/yr corresponding to the Kongur Shan. The right-lateral Muji fault at the northern end exhibits equivalent rates of extensional motion with a relatively shallow locking depth. The strike-slip rate along the Muji fault gradually increases from west to east, ranging approximately between 4 and 6 mm/yr, significantly influenced by the eastern normal fault. The Tahman fault (TKF) at the southernmost end of the KSES shows an extension rate of ~1.5 mm/yr accompanied by minor strike-slip motion. The Kashi anticline is approaching stability, while the Mushi anticline along the eastern Pamir frontal thrust (PFT) remains active with continuous uplift at ~2 mm/yr, indicating that deformation along the Tarim Basin–Tian Shan boundary has propagated southward from the South Tian Shan thrust (STST). Overall, this study demonstrates the effectiveness of integrated InSAR and GPS data in constraining contemporary deformation patterns along the northeastern Pamir margin, contributing to our understanding of the region’s tectonic characteristics. Full article
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<p>Tectonics and seismicity of the study area. (<b>a</b>) The yellow and red rectangle shows the spatial footprint of the Sentinel-1 InSAR coverage. Blue arrows show the GPS horizontal velocity field with respect to the stable Eurasian plate [<a href="#B28-remotesensing-16-04771" class="html-bibr">28</a>]. Circles of different colors represent earthquake events of varying magnitudes. (<b>b</b>) Fault structures in the eastern part of PFT. Red and pink focal mechanisms represent the mainshock and aftershocks of the 1985 Wuqia earthquake. Brown focal mechanisms represent the mainshock of the 2016 Aketao earthquake. (<b>c</b>) Fault segmentation in KESE. S1–S5 correspond to different segments, respectively. STST = southern Tian Shan thrust, PFT = Pamir frontal thrust, KSES = Kongur Shan extensional system, MPT = main Pamir thrust, KKF = Karakax fault, KYTS = Kashgar–Yecheng transfer system, TFF = Talas–Fergana fault, TT = Tuomuluoan thrust, MF = Muji fault, KATF = King Ata Tagh normal fault, KSF = Kongur Shan normal fault, MAF = Muztagh Ata normal fault, TF = Tahman normal fault, TKF = Tashkorgan normal fault.</p>
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<p>The Sentinel-1 A/B data processing workflow. It consists of three steps, including interferograms generation, SBAS time series analysis, and three-dimensional deformation field solution.</p>
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<p>Perpendicular and temporal baseline plot showing the network of interferograms on one ascending track (<b>a</b>) and one descending track (<b>b</b>) used in this study. The number of total interferograms are labelled for each track.</p>
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<p>Interpolated GNSS velocities map. The interpolated GNSS velocities using the method outlined by Shen et al. [<a href="#B47-remotesensing-16-04771" class="html-bibr">47</a>], (<b>a</b>) corresponds to EW and (<b>b</b>) corresponds to NS. GNSS velocities are resampled to a resolution of 0.01 degrees. Different colored circles represent different GPS data, and the color bars for GPS various data and the interpolated velocity field are identical.</p>
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<p>The satellite line-of-sight (LOS) velocity fields of the northeastern Pamir margin. Red lines represent the fault crossing profiles, each profile for 60 km long and 10 km wide, distributed along six sub-faults of the KSES. (<b>a</b>) corresponds to ascending track and (<b>b</b>) corresponds to descending track.</p>
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<p>Joint InSAR-GPS three-dimensional deformation field. (<b>a</b>–<b>c</b>) are east–west, north–south, and vertical components, respectively.</p>
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<p>GPS profiles and results. (<b>a</b>) Four profiles in KSES. Each profile is 300 km long and 50 km wide. (<b>b</b>) The GPS data was projected parallel to and perpendicular to the local fault, respectively, and combined with fault strike.</p>
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<p>The cross-fault profiles of ascending and descending LOS deformation velocities. (<b>a</b>) represents ascending LOS velocity profiles, and (<b>b</b>) represents descending LOS velocity profiles. (<b>a</b>–<b>f</b>) correspond successively to the six profiles in <a href="#remotesensing-16-04771-f005" class="html-fig">Figure 5</a>. Black dots are binned average values every 1 km along the profile. Gray vertical stripes indicate the mountains on profiles. Red and purple lines are the best-fitting models. The black dotted line indicates the fault location.</p>
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<p>An example of Bayesian MCMC inversion results for profile aa’. Posterior marginal probability density functions illustrating parameter estimation and uncertainty quantification. (<b>Top</b>): profile aa’ topography from the Copernicus DEM data with 30 m spatial resolution (average elevation: white line; min/max: gray lines). (<b>Middle</b>): InSAR LOS velocities with the best-fitting predicted velocities. (<b>Bottom</b>): model-predicted fault-parallel, fault-normal, and vertical velocities.</p>
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<p>The LOS velocities and vertical component of the eastern PFT. (<b>a</b>–<b>c</b>) are ascending, descending, and vertical components, respectively. The abnormal deformation area corresponding to the black circle and black rectangle are caused by industrial activity. The satellite images corresponding to these two regions are shown in <a href="#app1-remotesensing-16-04771" class="html-app">Figure S5</a>. (<b>d</b>–<b>f</b>) correspond to the results of profiles aa’, bb’, and cc’ in (<b>a</b>–<b>c</b>), respectively. The pink, blue, and green points correspond to the results of profiles aa’, bb’, and cc’, respectively.</p>
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19 pages, 5843 KiB  
Article
Identification of Strike-Slip Faults and Their Control on the Permian Maokou Gas Reservoir in the Southern Sichuan Basin (SW China): Fault Intersections as Hydrocarbon Enrichment Zones
by Jiawei Liu, Guanghui Wu, Hai Li, Wenjin Zhang, Majia Zheng, Hui Long, Chenghai Li and Min Deng
Energies 2024, 17(24), 6438; https://doi.org/10.3390/en17246438 - 20 Dec 2024
Viewed by 365
Abstract
The Middle Permian Maokou Formation carbonate rocks in the southern Sichuan Basin are import targets for hydrocarbon exploration, with numerous gas fields discovered in structural traps. However, as exploration extends into slope and syncline zones, the limestone reservoirs become denser, and fluid distribution [...] Read more.
The Middle Permian Maokou Formation carbonate rocks in the southern Sichuan Basin are import targets for hydrocarbon exploration, with numerous gas fields discovered in structural traps. However, as exploration extends into slope and syncline zones, the limestone reservoirs become denser, and fluid distribution becomes increasingly complex, limiting efficient exploration and development. Identifying the key factors controlling natural gas accumulation is therefore critical. This study is the first to apply deep learning techniques to fault detection in the southern Sichuan Basin, identifying previously undetected WE-trending subtle strike-slip faults (vertical displacement < 20 m). By integrating well logging, seismic, and production data, we highlight the primary factors influencing natural gas accumulation in the Maokou Formation. The results demonstrate that 80% of production comes from less than 30% of the well, and that high-yield wells are strongly associated with faults, particularly in slope and syncline zones where such wells are located within 200 m of fault zones. The faults can increase the drilling leakage of the Maokou wells by (7–10) times, raise the reservoir thickness to 30 m, and more than double the production. Furthermore, 73% of high-yield wells are concentrated in areas of fault intersection with high vertical continuity. Based on these insights, we propose four hydrocarbon enrichment models for anticline and syncline zones. Key factors controlling gas accumulation and high production include fault intersections, high vertical fault continuity, and local structural highs. This research demonstrates the effectiveness of deep learning for fault detection in complex geological settings and enhances our understanding of fault systems and carbonate gas reservoir exploration. Full article
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<p>(<b>a</b>) Tectonic divisions of the Sichuan Basin in China and location of the study area; (<b>b</b>) Surface geological map of the southern Sichuan Basin; (<b>c</b>) Geological section across southern Sichuan Basin; (<b>d</b>) Tectonic-stratigraphic column in the southern Sichuan Basin (after reference [<a href="#B19-energies-17-06438" class="html-bibr">19</a>]).</p>
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<p>(<b>a</b>) The seismic section showing the impact of evaporate and fault scale on the imaging of the deep fault; (<b>b</b>) the planar coherence at the bottom of the Cambrian showing only NNE-trending thrust faults in the Luzhou area.</p>
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<p>The architecture of the Unet (modified from [<a href="#B34-energies-17-06438" class="html-bibr">34</a>]).</p>
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<p>The seismic section of coherence (<b>a</b>,<b>c</b>) and DL attribute (<b>b</b>,<b>d</b>) showing the fault. (The section location is displayed in <a href="#energies-17-06438-f005" class="html-fig">Figure 5</a>.)</p>
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<p>Planar coherence attributes of coherence and DL attribute at the bottom of the Cambrian (<b>a</b>,<b>b</b>), and the bottom of the Upper Permian (<b>c,d</b>). (The location is displayed in <a href="#energies-17-06438-f006" class="html-fig">Figure 6</a>.)</p>
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<p>The interpretation sketch of fault system at (<b>a</b>) the bottom of the Upper Permian, (<b>b</b>) the bottom of the Upper Ordovician and (<b>c</b>) the bottom of the Cambrian in the Luzhou area (Є<sub>1</sub>q: base of the Cambrian; O<sub>3</sub>w: base of the Upper Ordovician; P<sub>2</sub>l: base of the Upper Permian).</p>
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<p>The thrust system in the Luzhou area (Є<sub>2</sub>g: base of the middle Cambrian; O<sub>3</sub>w: base of the Upper Ordovician; P<sub>1</sub>l: base of the Permian; P<sub>2</sub>l: base of the Upper Permian; T<sub>1</sub>f: base of the Lower Triassic; T<sub>1</sub>j: base of the Lower Triassic Jialingjing Formation; T<sub>3</sub>x: base of the Upper Triassic; The section location is displayed in <a href="#energies-17-06438-f006" class="html-fig">Figure 6</a>).</p>
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<p>The WE-trending (<b>a</b>,<b>b</b>) and NW-trending (<b>c</b>) fault system in the Luzhou area (O<sub>3</sub>w: base of the Upper Ordovician; P<sub>1</sub>l: base of the Permian; P<sub>2</sub>l: base of the Upper Permian; The section location is displayed in <a href="#energies-17-06438-f006" class="html-fig">Figure 6</a>).</p>
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<p>The cumulative gas and water production of the Maokou Formation in the Luzhou area. (High-yield well: The cumulative gas production &gt; 1 × 10<sup>8</sup> m<sup>3</sup>; Medium-yield well: 0.5 × 10<sup>8</sup> m<sup>3</sup> &lt; The cumulative gas production &lt; 1 × 10<sup>8</sup> m<sup>3</sup>; Low-yield well: The cumulative gas production &lt; 0.5 × 10<sup>8</sup> m<sup>3</sup>).</p>
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<p>Production curve of high-yield well of Maokou reservoir in the Luzhou area. (<b>a</b>) Gas production of Well Y71 in anticline zone; (<b>b</b>) Gas production of Well J41 in slope zone; (<b>c</b>) Gas production of Well J002-x2 in syncline zone.</p>
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<p>Hydrocarbon accumulation model of fault-controlled gas system in the southern Sichuan Basin. (Strike-slip faults serve as favorable lateral and vertical pathways for oil and gas migration and accumulation in the syncline zones, while thrust faults are advantageous channels for oil and gas migration in the anticline zones. Additionally, local traps can be separated by strike-slip faults.)</p>
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<p>DL attribute displays the vertical continuity structure of thrust (<b>a</b>–<b>c</b>) and strike-slip fault (<b>d</b>–<b>f</b>). From left to right, the continuity of fault gradually increases.</p>
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<p>(<b>a</b>) Relationship between cumulative gas production and distance to fault; (<b>b</b>) Relationship between fault direction and cumulative gas production; (<b>c</b>) Relationship between reservoir thickness and distance to fault; (<b>d</b>) Relationship between drilling leakage and distance to fault.</p>
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<p>The enrichment model of Maokou gas reservoir controlled by fault in the Luzhou area. (<b>a</b>) Enrichment model of strike-slip faults and thrust faults intersecting in anticline zone; (<b>b</b>) Enrichment model of thrust faults and the Permian reverse faults intersecting in anticline zone; (<b>c</b>) Enrichment model of strike-slip faults uplift in syncline zone; (<b>d</b>) Enrichment model of the Permian reverse faults and strike-slip faults intersecting in syncline zone.</p>
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27 pages, 8627 KiB  
Article
Mining-Induced Earthquake Risk Assessment and Control Strategy Based on Microseismic and Stress Monitoring: A Case Study of Chengyang Coal Mine
by Weichen Sun, Enyuan Wang, Jingye Li, Zhe Liu, Yunpeng Zhang and Jincheng Qiu
Appl. Sci. 2024, 14(24), 11951; https://doi.org/10.3390/app142411951 - 20 Dec 2024
Viewed by 390
Abstract
As large-scale depletion of shallow coal seams and increasing mining depths intensify, the frequency and intensity of mining-induced earthquake events have significantly risen. Due to the complex formation mechanisms of high-energy mining-induced earthquakes, precise identification and early warning cannot be achieved with a [...] Read more.
As large-scale depletion of shallow coal seams and increasing mining depths intensify, the frequency and intensity of mining-induced earthquake events have significantly risen. Due to the complex formation mechanisms of high-energy mining-induced earthquakes, precise identification and early warning cannot be achieved with a single monitoring method, posing severe challenges to coal mine safety. Therefore, this study conducts an in-depth risk analysis of two high-energy mining-induced earthquake events at the 3308 working face of Yangcheng Coal Mine, integrating microseismic monitoring, stress monitoring, and seismic source mechanism analysis. The results show that, by combining microseismic monitoring, seismic source mechanism inversion, and dynamic stress analysis, critical disaster-inducing factors such as fault activation, high-stress concentration zones, and remnant coal pillars were successfully identified, further revealing the roles these factors play in triggering mining-induced earthquakes. Through multi-dimensional data integration, especially the effective detection of the microseismic “silent period” as a key precursor signal before high-energy mining-induced earthquake events, a critical basis for early warning is provided. Additionally, by analyzing the spatiotemporal distribution patterns of different risk factors, high-risk areas within the mining region were identified and delineated, laying a foundation for formulating precise prevention and control strategies. The findings of this study are of significant importance for mining-induced earthquake risk management, providing effective assurance for safe production in coal mines and other mining environments with high seismic risks. The proposed analysis methods and control strategies also offer valuable insights for seismic risk management in other mining industries, ensuring safe operations and minimizing potential losses. Full article
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<p>Structural layout of the 3308 working face in relation to surrounding mining areas and faults.</p>
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<p>Occurrence characteristics of the coal seam at the working face.</p>
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<p>Online arrangement of KJ550 stress sensors at the 3308 working face.</p>
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<p>Spatial arrangement of microseismic sensors around the 3308 working face at Yangcheng Coal Mine.</p>
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<p>Stress variation trend of track conveyor roadway sensors 23–28 on the working face (3–12 September): (<b>a</b>) stress graph of track conveyor roadway sensor 23 (3–12 September); (<b>b</b>) stress graph of track conveyor roadway sensor 24 (3–12 September); (<b>c</b>) stress graph of track conveyor roadway sensor 25 (3–12 September); (<b>d</b>) stress graph of track conveyor roadway sensor 26 (3–12 September); (<b>e</b>) stress graph of track conveyor roadway sensor 27 (3–12 September); (<b>f</b>) stress graph of track conveyor roadway sensor 28 (3–12 September).</p>
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<p>Stress variation trend of track conveyor roadway sensors 23–28 on the working face (3–12 September): (<b>a</b>) stress graph of track conveyor roadway sensor 23 (3–12 September); (<b>b</b>) stress graph of track conveyor roadway sensor 24 (3–12 September); (<b>c</b>) stress graph of track conveyor roadway sensor 25 (3–12 September); (<b>d</b>) stress graph of track conveyor roadway sensor 26 (3–12 September); (<b>e</b>) stress graph of track conveyor roadway sensor 27 (3–12 September); (<b>f</b>) stress graph of track conveyor roadway sensor 28 (3–12 September).</p>
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<p>Stress variation trend of belt roadway sensors 23–28 on the working face (3–12 September): (<b>a</b>) stress graph of belt roadway sensor 22 (3–12 September); (<b>b</b>) stress graph of belt roadway sensor 23 (3–12 September); (<b>c</b>) stress graph of belt roadway sensor 24 (3–12 September); (<b>d</b>) stress graph of belt roadway sensor 25 (3–12 September); (<b>e</b>) stress graph of belt roadway sensor 26 (3–12 September); (<b>f</b>) stress graph of belt roadway sensor 27 (3–12 September).</p>
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<p>Stress variation trend of belt roadway sensors 23–28 on the working face (3–12 September): (<b>a</b>) stress graph of belt roadway sensor 22 (3–12 September); (<b>b</b>) stress graph of belt roadway sensor 23 (3–12 September); (<b>c</b>) stress graph of belt roadway sensor 24 (3–12 September); (<b>d</b>) stress graph of belt roadway sensor 25 (3–12 September); (<b>e</b>) stress graph of belt roadway sensor 26 (3–12 September); (<b>f</b>) stress graph of belt roadway sensor 27 (3–12 September).</p>
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<p>Stress variation trend of track conveyor roadway sensors 23–28 on the working face (13–22 December): (<b>a</b>) stress graph of track conveyor roadway sensor 23 (13–22 December); (<b>b</b>) stress graph of track conveyor roadway sensor 24 (13–22 December); (<b>c</b>) stress graph of track conveyor roadway sensor 25 (13–22 December); (<b>d</b>) stress graph of track conveyor roadway sensor 26 (13–22 December); (<b>e</b>) stress graph of track conveyor roadway sensor 27 (13–22 December); (<b>f</b>) stress graph of track conveyor roadway sensor 28 (13–22 December).</p>
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<p>Stress variation trend of track conveyor roadway sensors 23–28 on the working face (13–22 December): (<b>a</b>) stress graph of track conveyor roadway sensor 23 (13–22 December); (<b>b</b>) stress graph of track conveyor roadway sensor 24 (13–22 December); (<b>c</b>) stress graph of track conveyor roadway sensor 25 (13–22 December); (<b>d</b>) stress graph of track conveyor roadway sensor 26 (13–22 December); (<b>e</b>) stress graph of track conveyor roadway sensor 27 (13–22 December); (<b>f</b>) stress graph of track conveyor roadway sensor 28 (13–22 December).</p>
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<p>Stress variation trend of belt roadway sensors 23–28 on the working face (13–22 December): (<b>a</b>) stress graph of belt roadway sensor 22 (13–22 December); (<b>b</b>) stress graph of belt roadway sensor 23 (13–22 December); (<b>c</b>) stress graph of belt roadway sensor 24 (13–22 December); (<b>d</b>) stress graph of belt roadway sensor 25 (13–22 December); (<b>e</b>) stress graph of belt roadway sensor 26 (13–22 December); (<b>f</b>) stress graph of belt roadway sensor 27 (13–22 December).</p>
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<p>Microseismic statistics and location information of the 3308 working face (25 August–14 September).</p>
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<p>Microseismic statistics and location information of the 3308 working face (5–24 December).</p>
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<p>Distribution map of microseismic event locations: (<b>a</b>) microseismic event location base map (3–12 September); (<b>b</b>) microseismic event location base map (13–22 December).</p>
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<p>Microseismic data and source mechanism of the 10 September mining-induced earthquake event: (<b>a</b>) original microseismic waveform; (<b>b</b>) source mechanism.</p>
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<p>Microseismic data and source mechanism of the 20 December mining-induced earthquake event: (<b>a</b>) original microseismic waveform; (<b>b</b>) source mechanism.</p>
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<p>Risk Zone Delineation Process.</p>
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<p>Risk zone delineation for the 3308 working face.</p>
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19 pages, 5149 KiB  
Article
Hydrocarbon Potential Assessment Methods in Complex Fault Zones: A Case Study of the Southern Pinghu Structural Belt, East China Sea
by Donghui Jiang, Sujie Yan, Renhai Pu, Yunwen Guan, Xinxu Dong, Shuo Chen and Siyu Su
Energies 2024, 17(24), 6419; https://doi.org/10.3390/en17246419 - 20 Dec 2024
Viewed by 373
Abstract
Frequent tectonic activity in rift basins has led to complex fault zones, which have led to extensive hydrocarbon distributions and tremendous resource potential. This study investigated the hydrocarbon potential in the southern Pinghu structural belt, focusing on fault traps in complex fault zones. [...] Read more.
Frequent tectonic activity in rift basins has led to complex fault zones, which have led to extensive hydrocarbon distributions and tremendous resource potential. This study investigated the hydrocarbon potential in the southern Pinghu structural belt, focusing on fault traps in complex fault zones. Through fault sealing analysis and gas detection attenuation methods, this study aims to improve exploration success rates. The application outcomes demonstrate that the Shale Gouge Ratio (SGR) threshold for achieving the lateral sealing of faults in the southern Pinghu structural belt is 34%, with a critical fault throw of 100 m. Regions where the fault’s lateral sealing zone corresponds with areas exhibiting anomalous gas responses are deemed promising for hydrocarbon accumulation. Additional analysis indicates that favorable fault trap development occurs along the foot walls of significant faults, particularly in the eastern sector of the study area. The findings are corroborated by actual drilling data, affirming the efficacy of these methods in pinpointing hydrocarbon traps within complex fault zones and offering valuable insights for their broader global application. Full article
(This article belongs to the Section H3: Fossil)
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<p>Location of the southern Pinghu structural belt and its stratigraphic development characteristics. (<b>a</b>) Location of the East China Sea Shelf Basin; (<b>b</b>) unit division of the Xihu Sag structure, where the blue and dark blue areas represent the central bulge zone of the Xihu Sag, the orange−yellow and yellow areas depict the slope positions on both wings, the purple area highlights the Diaoyu Island Uplift to the east in disconformable contact with the depression, and the white area indicates the adjacent uplifted structural zone, showcasing the relationship between the sag and surrounding geological structures; (<b>c</b>) distribution of fault systems in the southern Pinghu structural belt, the gradient from orange to blue in the seismic reflection horizon of T30 signifies the increasing depth; (<b>d</b>) comprehensive bar chart of the Pinghu Formation.</p>
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<p>The FTKD of C7 well. (<b>a</b>) Knipe diagram of C7 well; (<b>b</b>) triangle diagram with oblique projection of SGR values; (<b>c</b>) representative maps of different properties of fault planes (the final manifestation of FTKD); (<b>d</b>) T32 structural map showing location of faults and well; (<b>e</b>) profile location map of faults, where red represents the general distribution of faults, green denotes the typical F2 faults, and blue signifies the characteristic F1 faults.; (<b>f</b>) the displacement variation map of F1 fault with depth.</p>
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<p>Comparison diagram of the frequency spectra between gas−bearing and non−gas stratum.</p>
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<p>Stratigraphic correlation and lithological characteristics of the Pinghu Formation in the southern Pinghu structural belt.</p>
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<p>Numerical display of fault closure in the southern Pinghu structural belt. (<b>a</b>–<b>h</b>) SGR value constrained by well location in the study area; (<b>i</b>) comparison diagrams of risk throw and sgr threshold at different well locations.</p>
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<p>Allan diagram of the F1 fault in the southern Pinghu structural belt along the strike(Points O and P designate the starting path of the profile, as depicted in <a href="#energies-17-06419-f007" class="html-fig">Figure 7</a>). (<b>a</b>) Distribution of bottom wall; (<b>b</b>) the distribution of rising wall; (<b>c</b>) the lithological juxtaposition relationship on both sides of the fault plane; (<b>d</b>) seismic profile of the vertical F1 fault T30–T32 SGR response; (<b>e</b>) SGR distribution status at the segment plane.</p>
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<p>Abnormal ① characteristics in the southern Pinghu structural belt. (<b>a</b>) Trap morphology; (<b>b</b>) the extent of the structural trap is shown on the T32 structural map; (<b>c</b>) the range of traps constrained by fault sealing is depicted on the T32 structural map; (<b>d</b>) abnormal range of gas content; (<b>e</b>) gas attenuation range on the T32 structural map.</p>
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<p>Assessment and prospective reservoir distribution map of the middle section fault in the southern Pinghu structural belt. (<b>a</b>) Gas occurrence display and T32 fault superimposed planar map; (<b>b</b>) Classification display of the T32 fault, gas occurrence range, and closure range display; (<b>c</b>) Seismic profile of gas anomalies in the Pinghu Formation. (<b>d</b>) Possible lateral migration trajectory of the Pinghu Formation along the F to G seismic line.</p>
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19 pages, 26960 KiB  
Article
The Northern Giona Fault Zone, a Major Active Structure Through Central Greece
by Leonidas Gouliotis and Dimitrios Papanikolaou
GeoHazards 2024, 5(4), 1370-1388; https://doi.org/10.3390/geohazards5040065 - 18 Dec 2024
Viewed by 478
Abstract
The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates [...] Read more.
The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates a significant elevation difference of approximately 1500 m between the northern Giona footwall and the southern Iti hanging wall. The footwall comprises imbricated Mesozoic carbonates of the Parnassos unit, which exhibit large-scale drag folding near and parallel to the fault. The hanging wall comprises deformed sedimentary rocks of the Beotian unit and tectonic klippen of the Eastern Greece unit, forming a southward-tilted neotectonic block with subsidence near the Northern Giona Fault and uplift near the Ypati fault to the north. These two E-W faults represent younger structures disrupting the older NNW-trending tectonic framework. Fault scarps are observed all along the 14 km length of the Northern Giona fault accompanied by cataclastic zones, separating the carbonate formations of the Parnassos Unit from thick scree, slide blocks, boulders and olistholites. Inversion of fault-slip data has shown a mean slip vector of 45°, N004°E, which aligns with the current regional extensional deformation of the area, as confirmed by focal mechanism solutions. Based on the general asymmetry of the alpine units in the hanging wall, we interpret a listric fault geometry at depth using slip-line analysis and we forward modelled a disrupted fault-propagation fold using kinematic trishear algorithms, estimating a total displacement of 6500 m and a throw of approximately 2000 m. Seismic activity in the area of the Northern Giona Fault includes a magnitude 6.1 earthquake in 1852, which caused casualties, rockfalls and extensive damage, as well as a magnitude 5.1 event in 1983. The expected seismic magnitude is deterministically estimated between 6.2 and 6.7, depending on the potential westward continuation of the Northern Giona Fault beyond the Mornos River to the Northern Vardoussia saddle. The seismic hazard zone includes several villages located near the fault, particularly on the hanging wall, where intense landslide activity during seismic events could result in severe damage to regional infrastructure. The neotectonic development of the Northern Giona Fault highlights the importance of extending seismotectonic research into the mountainous regions of central Greece within the alpine formations, beyond the post-orogenic sedimentary basins. Full article
(This article belongs to the Special Issue Active Faulting and Seismicity—2nd Edition)
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<p>Morphological map of the mountainous region of central Greece between the Corinth Gulf and the Sperchios Valley/Maliakos Gulf. The northern boundary is defined by the northern slopes of Iti Mt, where the Ypati Fault (YF) creates a significant topographic difference of 2000 m, separating the mountainous area from the Sperchios Valley. To the south, the northern slopes of Giona Mt. align with the Northern Giona Fault (NGF), marking a topographic difference of 1500 m between Giona Mt and Iti Mt.</p>
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<p>Three-dimensional perspective of the studied area with view from the east-northeast (<b>top</b>) and view from the west (<b>bottom</b>). In both views, the NGF and YF are indicated along the abrupt northern slopes of Giona and Iti Mts, respectively. These two subparallel faults have shaped the landscape, creating high mountain peaks at their northern edges on the uplifted footwalls and subsidence to the southern edges.</p>
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<p>Map illustrating the distribution of seismic epicenters for instrumental (solid circles) and historical earthquakes (purple triangles), along with focal mechanisms for M &gt; 4 (NOA—<a href="http://emsc-csem.org" target="_blank">http://emsc-csem.org</a>), GPS velocity vectors [<a href="#B7-geohazards-05-00065" class="html-bibr">7</a>] and neotectonic faults (black lines). Central Greece’s active deformation results primarily from displacements on E-W-trending faults and some NE-SW strike-slip events.</p>
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<p>(<b>Top</b>) Geotectonic map of the mountainous region of central Greece between the Sperchios valley to the north and the Corinth gulf to the south. Yellow dashed rectangle shows the extent of the detailed geological map along the NGF of Figure 6. (<b>Middle</b>) NNW—SSE cross section from the Orthrys Mt to the Corinth Gulf showing the geometry of the alpine units and the major neotectonic boundaries, including the YF and the NGF. Dashed blue line indicates the top carbonate of the Parnassos unit. (<b>Bottom</b>) 2D forward model across the NGF showing the deformation of a 10 km layer-cake model with the top horizon corresponding to the pre-Pliocene tectonic framework as built in Figure 11. The Mw 5.1 19 September 1983 earthquake is plotted on the profile alongside the NGF. 1: Pindos Unit, 2: Penteoria unit, 3: Vardoussia unit, 4: Parnassos unit, 5: Beotian unit, 6: Eastern Greece unit, 7: Late Oligocene–Miocene molassic sediments, 8: Late Miocene-Quaternary sediments, 9: Neotectonic and active fault, 10: Miocene Extensional Detachment, including the Itea-Amfissa detachment (IAD) 11: major thrust fault.</p>
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<p>Panoramic view looking SSW of the NGF (red dashed line) along the northern slopes of Giona Mt. The high-elevated area of northern Giona belonging to the Parnassos unit occur in the footwall whereas the ophiolites and related sediments of the uppermost Eastern Greece unit occur in the hanging wall.</p>
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<p>Geological map and cross section of the northern Giona region. 1: alluvial deposits, 2: scree deposits, 3: Neogene deposits, 4: Molassic sediments of Oligocene—Middle Miocene, 5: Triassic—Jurassic carbonates of the SubPelagonian unit, 6: Jurassic ophiolites, 7: Beotian Unit with Jurassic—Cretaceous pelagic limestones, 8: Eocene flysch of the Parnassos unit, 9: Mesozoic Carbonate platform of the Parnassos unit, with an older b2 (black) and a younger b3 (red) bauxite horizons in the cross section, 10: Eocene flysch of the Vardousssia unit, 11: Olistholites mainly of Carbonate rocks, 12: Neotectonic and active normal fault, 13: IAD, Itea-Amfissa Detachment, Miocene extensional Detachment, 14: Overthrust, 15: thrust, 16. Base of gravity slide. FW1, HW1: Imbricated tectonic units of the Parnassos nappe. FW, HW: NGF’s footwall and hanging wall.</p>
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<p>View to the west of the footwall at the central part of the NGF, directly east of the Vrayla peak, at 1800–2000 m altitude. The Parnassos carbonate sequence is characterized by two members in this site: a lower one of Late Jurassic age (Js) and an upper one of Late Jurassic-Early Cretaceous age (J13-K6) separated by a bauxite horizon (b2—[<a href="#B24-geohazards-05-00065" class="html-bibr">24</a>]). In the sketch, solid lines indicate W-dipping strata, while dashed lines indicate N-dipping strata. This change in dip azimuth is characteristic of a kilometric scale normal drag developed near the NGF.</p>
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<p>View from the east of the NGF. Two notable sites (<b>A</b>,<b>B</b>) where the grooved and striated fault surface is exposed and measurable, showing top-to-N movement.</p>
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<p>Outcrops of the fault surfaces along the northern Giona slopes. (<b>A</b>) Fault surface (red arrows) at the central part of the NGF. (<b>B</b>) Close view of a north-dipping fault plane along the high slopes at the western side of the NGF. (<b>C</b>) Characteristic cross-section of the fault zone in the eastern part of the NGF, showing a well-developed damage zone that grade to thick fault core (red arrow). (<b>D</b>) Curved fault surface at the eastern termination of the NGF close to the Gravia village.</p>
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<p>Fault-slip data and calculation of stress tensor with the method of direct inversion for the NGF. Lower hemisphere, equal-area stereographic projections. <b>Left pane</b> shows fault-slip data and the calculated stress axes (σ1, σ2, σ3). <b>Middle pane</b> is a fluctuation histogram of the deviation angle (angle between measured and calculated slip vectors) and stress ratio R(σ2 − σ3)/(σ1 − σ3). <b>Right pane</b> shows the P–T axes.</p>
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<p>Construction of a 10 km-layer cake model illustrating a five-stage progressive deformation of the NGF footwall and hanging wall through the application of a trishear kinematic model with increasing displacements of 1300 m, 2600 m, 3900 m, 5200 &amp; 6500 m. Details of the trishear model are provided within text. The top layer represents the regional pre-tectonic level, corresponding to the pre-Pliocene deformed state, which includes early orogenic Late Eocene thrust faults (solid lines with triangles) overlying the Parnassos flysch. The Parnassos flysch comprises two members: the lower red pelites and the upper pelitic-sandstone, separated by a dashed line. Unconformably overlying these units are late-orogenic Miocene molasse deposits (wavy brown line) within the Iti and northern Giona fault blocks. The northward-dipping listric geometry of the NGF at depth is based on slip-line analysis [<a href="#B45-geohazards-05-00065" class="html-bibr">45</a>].</p>
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<p>Map showing the spatial distribution of seismic intensity recorded by various catastrophic phenomena associated with the two significant earthquakes in the region. The solid ellipse represents the macroseismic intensities from the 14 July 1852 earthquake, which had a magnitude of 6.1 (yellow star). The dashed ellipse outlines the area affected by the microseismicity (magnitudes between 2.0 and 4.2) that followed the 19 September 1983, earthquake, which had a magnitude of 5.1 and a fault plane solution of an ENE-WSW normal fault.</p>
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26 pages, 46995 KiB  
Article
New Evidence of Holocene Faulting Activity and Strike-Slip Rate of the Eastern Segment of the Sunan–Qilian Fault from UAV-Based Photogrammetry and Radiocarbon Dating, NE Tibetan Plateau
by Pengfei Niu, Zhujun Han, Peng Guo, Siyuan Ma and Haowen Ma
Remote Sens. 2024, 16(24), 4704; https://doi.org/10.3390/rs16244704 - 17 Dec 2024
Viewed by 492
Abstract
The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with [...] Read more.
The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics. Full article
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Figure 1

Figure 1
<p>The distribution of the major active faults and earthquake epicenters (M ≥ 6.0) along the northeastern margin of the Tibetan Plateau. (<b>a</b>) The red box indicates the area shown in panel (<b>b</b>), while the black arrows indicate the direction of block movement. Abbreviations: ATF, Altyn Tagh fault; KF, Kunlun fault; QHF, Qilian-Haiyuan fault; XF, Xianshuihe fault. (<b>b</b>) The locations and characteristics of the faults are based on [<a href="#B9-remotesensing-16-04704" class="html-bibr">9</a>]. The seismic data are sourced from the China Earthquake Information Network (<a href="https://news.ceic.ac.cn/index.html?time=1698442872" target="_blank">https://news.ceic.ac.cn/index.html?time=1698442872</a>, accessed on 3 October 2024), while the GPS velocity field relative to the stable Eurasian continent is derived from [<a href="#B21-remotesensing-16-04704" class="html-bibr">21</a>]. Abbreviations: ATF, Altyn Tagh fault; CMF, Changma fault; TLSF, Tuolaishan fault; HLHF, Halahu fault; SN-QLF, Sunan-Qilian fault; ES-SQF, Eastern segment of the Sunan–Qilian Fault; LLLF, Lenglongling fault; JQHF, Jinqianghe fault; MMSF, Maomaoshan fault; LHSF, Laohushan fault; HYF, Haiyuan fault; GLF, Gulang fault; XS-TJSF, Xiangshan-Tianjingshan fault.</p>
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<p>The distribution map of the eastern segment of the Sunan–Qilian Fault. (<b>a</b>) A fault distribution map, with the fault trace based on [<a href="#B9-remotesensing-16-04704" class="html-bibr">9</a>], primarily interpreted using high-resolution remote sensing images (Google Earth, 0.4 m resolution). (<b>b</b>) Geomorphic features along and on both sides of the fault.</p>
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<p>The faulted geomorphic features north of Ebao town (base map: Google Earth 2024 image). (<b>a</b>) Google Earth imagery; (<b>b</b>) fault trace with Google Earth imagery as the base map.</p>
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<p>A shaded relief map of the mountainous area north of Ebao town, captured using UAVs.</p>
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<p>Fault and displaced geomorphic features in the Qingsha River section. (<b>a</b>) Shaded relief map generated from the Unmanned Aerial Vehicle (UAV)-derived digital elevation model (DEM), with a resolution of 0.24 m. The contour interval is 2 m. (<b>b</b>) Interpreted map of displaced geomorphic features; (<b>c</b>–<b>f</b>) are close-up views.</p>
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<p>The measurement and restoration of T2/T1 riser displacement in the Qingsha River section using LaDiCaozsoftware (V2.1). (<b>a</b>) Shaded relief map of the T2/T1 riser on the left bank of the Qingsha River; the cyan line indicates the fault location, the light yellow lines show the trend of the risers on both sides of the fault, and the red and blue lines mark the locations of topographic profiles of the risers; (<b>b</b>) the optimal displacement restoration map of the T2/T1 riser; (<b>c</b>) the original riser and gully topographic profile (top left), the restored riser and gully topographic profile (bottom left), and the misfit distribution map for displacement measurements (right).</p>
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<p>Trench profile mosaic (<b>a</b>) and interpretation map (<b>b</b>) at the bend of the Qingsha River section.</p>
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<p>Close-up photo and interpretation map of the Qingsha River trench profile. (<b>a</b>) Close-up photo. (<b>b</b>) Fault interpretation map.</p>
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<p>The stratigraphic profile of the top of the T2 terrace in the Qingsha River section and sampling locations.</p>
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<p>Fault and displacement geomorphology features in the Dangzhong River section: (<b>a</b>) Shaded relief map generated from the Unmanned Aerial Vehicle (UAV)-derived digital elevation model (DEM), with a resolution of 0.24 m. The contour interval is 2 m. (<b>b</b>) Interpreted map of displaced geomorphic features.</p>
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<p>The displacement measurement and restoration of the T2/T1 riser in the Dangzhong River section based on LaDiCaoz software (V2.1). (<b>a</b>) Shaded relief map of the T2/T1 riser on the left bank of the Dangzhong River; the cyan line indicates the fault location, the light yellow lines show the trend of the risers on both sides of the fault, and the red and blue lines mark the locations of topographic profiles of the risers; (<b>b</b>) the optimal displacement restoration map of the T2/T1 riser; (<b>c</b>) the original riser and gully topographic profile (top left), the restored riser and gully topographic profile (bottom left), and the misfit distribution map for displacement measurements (right).</p>
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<p>Trench profile mosaic (<b>a</b>) and interpretation map (<b>b</b>) at the bend of the Dangzhong River.</p>
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<p>The stratigraphic profile of the top of the T2 terrace in the Dangzhong River section and sampling locations.</p>
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<p>The geological slip rate distribution map of the QHF [<a href="#B5-remotesensing-16-04704" class="html-bibr">5</a>,<a href="#B7-remotesensing-16-04704" class="html-bibr">7</a>,<a href="#B8-remotesensing-16-04704" class="html-bibr">8</a>,<a href="#B24-remotesensing-16-04704" class="html-bibr">24</a>,<a href="#B27-remotesensing-16-04704" class="html-bibr">27</a>,<a href="#B28-remotesensing-16-04704" class="html-bibr">28</a>,<a href="#B30-remotesensing-16-04704" class="html-bibr">30</a>,<a href="#B31-remotesensing-16-04704" class="html-bibr">31</a>,<a href="#B32-remotesensing-16-04704" class="html-bibr">32</a>,<a href="#B33-remotesensing-16-04704" class="html-bibr">33</a>,<a href="#B34-remotesensing-16-04704" class="html-bibr">34</a>,<a href="#B35-remotesensing-16-04704" class="html-bibr">35</a>,<a href="#B36-remotesensing-16-04704" class="html-bibr">36</a>,<a href="#B37-remotesensing-16-04704" class="html-bibr">37</a>,<a href="#B38-remotesensing-16-04704" class="html-bibr">38</a>,<a href="#B62-remotesensing-16-04704" class="html-bibr">62</a>,<a href="#B63-remotesensing-16-04704" class="html-bibr">63</a>,<a href="#B64-remotesensing-16-04704" class="html-bibr">64</a>,<a href="#B65-remotesensing-16-04704" class="html-bibr">65</a>,<a href="#B66-remotesensing-16-04704" class="html-bibr">66</a>,<a href="#B67-remotesensing-16-04704" class="html-bibr">67</a>]. Abbreviations: CMF, Changma fault; TLSF, Tuolaishan fault; HLHF, Halahu fault; SN-QLF, Sunan-Qilian fault; ES-SQF, Eastern segment of the Sunan–Qilian Fault; LLLF, Lenglongling fault; JQHF, Jinqianghe fault; MMSF, Maomaoshan fault; LHSF, Laohushan fault; HYF, Haiyuan fault; GLF, Gulang fault; XS-TJSF, Xiangshan-Tianjingshan fault.</p>
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<p>The influence of the 2022 Menyuan earthquake on the Coulomb stress of ES-SQF. Abbreviations: ES-SQF, Eastern segment of the Sunan–Qilian Fault. (<b>a</b>) A depth of 5 km; (<b>b</b>) A depth of 10 km.</p>
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