Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.002
Mu Lin , Qi Li , Wei Chen , Gang Liu , Dongzhen Wang , Lijiang Zhao , Tianchen Sheng , Wenlong Zhou , Liyang Wang , Zhaosheng Nie , Bin Zhao , Xuejun Qiao , Zilong Chen
An M6.2 earthquake struck Jishishan County, Gansu, on December 18, 2023, with its epicenter located in the arc-shaped tectonic belt formed by the Lajishan-Jishishan Fault. Continuous high-rate global navigational satellite system (GNSS) data were utilized to simulate real-time data resolution, enabling the rapid determination of coseismic static and dynamic deformation caused by the earthquake and the estimation of empirical magnitude. Far-field body waves served as constraints for the source rupture process, facilitating the analysis of potential seismogenic fault structures. GNSS stations within 30 km of the epicenter exhibited significant coseismic responses: horizontal peak displacement and velocity reached approximately 6.3 cm and 6.1 cm/s, respectively. Additionally, quasi-real-time differential positioning and post-event precise point positioning results were consistent throughout the source process. Vertical velocity, calculated via epoch-by-epoch differential velocity determination, showed clear coseismic signals, with peak values increasing to 2.6 cm/s. The empirical magnitude, based on displacement, was 5.99, while the magnitude derived from the velocity waveform amplitude was 6.05, both consistent with the moment magnitude. The dynamic displacement distribution preliminarily suggests directional effects of northward rupture propagation, aligning with subsequent aftershock occurrences. Finite fault inversion results, based on the two nodal planes of the focal mechanism, indicate that asperity ruptures concentrated at the hypocenter played a major role. These ruptures propagated from the hypocenter to shallow regions and northward, lasting approximately 10 s. Although the coseismic deformation determined by sparse high-rate GNSS cannot constrain the specific fault dip angle, the relationship between rupture propagation direction from the seismic source model and aftershock distribution suggests a northeast-dipping fault. Moreover, seismic source models representing single faults as geometric structures can only simulate permanent formations. In contrast, the conjugate fault model, which aligns with aftershock distributions, more accurately explains high-rate GNSS displacement waveforms. Considering both regional tectonics and geological survey results, the seismogenic fault is believed to be a local northeast-dipping blind thrust fault. Northward rupture propagation may have caused the movement of conjugate faults. This study is an effective case of using high-rate GNSS for rapid earthquake response, providing a reference basis for understanding the seismic activity patterns and earthquake disaster prevention in the region.
{"title":"High-rate GNSS-based rapid determination of coseismic deformation and source characteristics for the 2023 M6.2 Jishishan earthquake","authors":"Mu Lin , Qi Li , Wei Chen , Gang Liu , Dongzhen Wang , Lijiang Zhao , Tianchen Sheng , Wenlong Zhou , Liyang Wang , Zhaosheng Nie , Bin Zhao , Xuejun Qiao , Zilong Chen","doi":"10.1016/j.eqs.2025.01.002","DOIUrl":"10.1016/j.eqs.2025.01.002","url":null,"abstract":"<div><div>An <em>M</em>6.2 earthquake struck Jishishan County, Gansu, on December 18, 2023, with its epicenter located in the arc-shaped tectonic belt formed by the Lajishan-Jishishan Fault. Continuous high-rate global navigational satellite system (GNSS) data were utilized to simulate real-time data resolution, enabling the rapid determination of coseismic static and dynamic deformation caused by the earthquake and the estimation of empirical magnitude. Far-field body waves served as constraints for the source rupture process, facilitating the analysis of potential seismogenic fault structures. GNSS stations within 30 km of the epicenter exhibited significant coseismic responses: horizontal peak displacement and velocity reached approximately 6.3 cm and 6.1 cm/s, respectively. Additionally, quasi-real-time differential positioning and post-event precise point positioning results were consistent throughout the source process. Vertical velocity, calculated via epoch-by-epoch differential velocity determination, showed clear coseismic signals, with peak values increasing to 2.6 cm/s. The empirical magnitude, based on displacement, was 5.99, while the magnitude derived from the velocity waveform amplitude was 6.05, both consistent with the moment magnitude. The dynamic displacement distribution preliminarily suggests directional effects of northward rupture propagation, aligning with subsequent aftershock occurrences. Finite fault inversion results, based on the two nodal planes of the focal mechanism, indicate that asperity ruptures concentrated at the hypocenter played a major role. These ruptures propagated from the hypocenter to shallow regions and northward, lasting approximately 10 s. Although the coseismic deformation determined by sparse high-rate GNSS cannot constrain the specific fault dip angle, the relationship between rupture propagation direction from the seismic source model and aftershock distribution suggests a northeast-dipping fault. Moreover, seismic source models representing single faults as geometric structures can only simulate permanent formations. In contrast, the conjugate fault model, which aligns with aftershock distributions, more accurately explains high-rate GNSS displacement waveforms. Considering both regional tectonics and geological survey results, the seismogenic fault is believed to be a local northeast-dipping blind thrust fault. Northward rupture propagation may have caused the movement of conjugate faults. This study is an effective case of using high-rate GNSS for rapid earthquake response, providing a reference basis for understanding the seismic activity patterns and earthquake disaster prevention in the region.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 187-200"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.006
Fei Zhao , Jie Li , Ming Zhu , Yifei Xu , Guoqing Chen , Jianhui Dong , Jianjun Zhao
On September 5, 2022, an MS6.8 earthquake struck Luding County, Kardze Prefecture, Sichuan Province—an area that is particularly vulnerable to geological changes. The earthquake caused considerable damage along the highway, leading to road disruptions and blockages, further isolating earthquake-stricken areas. Accordingly, a rapid survey of the main highways in this area was conducted, and 507 damage points were identified. Roadbed damage accounted for more than 70% of the total damages. Co-seismic disasters were primarily distributed along the highways on both sides of the Dadu River in the reservoir area of the Dagangshan Hydropower Station, Caoke Township, and Detuo Township. Further, six factors under three categories of the spatial distribution of highway damage in the earthquake-stricken areas were analyzed. The rate of highway damage was positively correlated with the seismic intensity but negatively correlated with the fault and river distances. The earthquake intensity had the most significant impact: 37.5% of road disruptions were found in areas with an intensity of IX; this percentage was 1.6 and 5.8 times greater than those found in areas with intensities of VIII and VII, respectively. The roads with the most significant damage were in regions with intensities above VIII, faults within 5 km, slopes within 30°–70°, rivers within 100 m, and the presence of granite. This indicated that these factors aggravated highway disruption, resulting in more than 90% of damaged highways in strongly shaken regions. Our findings may provide guidance for efficient highway recovery following earthquakes.
{"title":"Analysis on the characteristics and spatial distribution patterns of highway damage caused by the 2022 MS6.8 Luding earthquake","authors":"Fei Zhao , Jie Li , Ming Zhu , Yifei Xu , Guoqing Chen , Jianhui Dong , Jianjun Zhao","doi":"10.1016/j.eqs.2025.01.006","DOIUrl":"10.1016/j.eqs.2025.01.006","url":null,"abstract":"<div><div>On September 5, 2022, an <em>M</em><sub>S</sub>6.8 earthquake struck Luding County, Kardze Prefecture, Sichuan Province—an area that is particularly vulnerable to geological changes. The earthquake caused considerable damage along the highway, leading to road disruptions and blockages, further isolating earthquake-stricken areas. Accordingly, a rapid survey of the main highways in this area was conducted, and 507 damage points were identified. Roadbed damage accounted for more than 70% of the total damages. Co-seismic disasters were primarily distributed along the highways on both sides of the Dadu River in the reservoir area of the Dagangshan Hydropower Station, Caoke Township, and Detuo Township. Further, six factors under three categories of the spatial distribution of highway damage in the earthquake-stricken areas were analyzed. The rate of highway damage was positively correlated with the seismic intensity but negatively correlated with the fault and river distances. The earthquake intensity had the most significant impact: 37.5% of road disruptions were found in areas with an intensity of IX; this percentage was 1.6 and 5.8 times greater than those found in areas with intensities of VIII and VII, respectively. The roads with the most significant damage were in regions with intensities above VIII, faults within 5 km, slopes within 30°–70°, rivers within 100 m, and the presence of granite. This indicated that these factors aggravated highway disruption, resulting in more than 90% of damaged highways in strongly shaken regions. Our findings may provide guidance for efficient highway recovery following earthquakes.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 201-217"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.003
Binbin Mi , Jianghai Xia , Hao Zhang
Passive surface wave imaging has been a powerful tool for near-surface characterization in urban areas, which extracts surface wave signals from ambient seismic noise and then estimates subsurface shear wave velocity by inversion of the measured phase velocity. The high-frequency (approximately >1 Hz) seismic noise fields in urban environments are dominantly induced by human activities such as the vehicle traffic. Traffic seismic sources are nonrandomly distributed in time and space. Applying standard interferometric techniques to recordings from these nonrandom noise sources makes the Green’s function liable to estimation errors. We analyze the influence of using nonrandom traffic seismic sources for surface wave imaging. With nonrandom traffic seismic sources in time, spurious signals are generated in the cross-correlation function. With nonrandom traffic seismic sources in space, surface-wave phase velocities could be overestimated in the dispersion measurement. We provide an overview of solutions for surface-wave imaging with nonrandom traffic seismic sources in time and space, aiming to improve the retrieval of high-frequency surface waves and achieve reliable results from ultrashort (tens of seconds) observations for near-surface characterization.
{"title":"Surface-wave imaging with nonrandom traffic seismic sources","authors":"Binbin Mi , Jianghai Xia , Hao Zhang","doi":"10.1016/j.eqs.2025.01.003","DOIUrl":"10.1016/j.eqs.2025.01.003","url":null,"abstract":"<div><div>Passive surface wave imaging has been a powerful tool for near-surface characterization in urban areas, which extracts surface wave signals from ambient seismic noise and then estimates subsurface shear wave velocity by inversion of the measured phase velocity. The high-frequency (approximately >1 Hz) seismic noise fields in urban environments are dominantly induced by human activities such as the vehicle traffic. Traffic seismic sources are nonrandomly distributed in time and space. Applying standard interferometric techniques to recordings from these nonrandom noise sources makes the Green’s function liable to estimation errors. We analyze the influence of using nonrandom traffic seismic sources for surface wave imaging. With nonrandom traffic seismic sources in time, spurious signals are generated in the cross-correlation function. With nonrandom traffic seismic sources in space, surface-wave phase velocities could be overestimated in the dispersion measurement. We provide an overview of solutions for surface-wave imaging with nonrandom traffic seismic sources in time and space, aiming to improve the retrieval of high-frequency surface waves and achieve reliable results from ultrashort (tens of seconds) observations for near-surface characterization.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 253-262"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.007
Wenpei Miao , Guoliang Li , Fenglin Niu , Kai Tao , Yonghua Li
Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards. In this study, we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models, that is, Shen2016, FWEA18, and USTClitho1.0, leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region. Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms. The results show that the Shen2016 model, derived from traditional surface wave tomography, performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau, followed by FWEA18, built from full-waveform inversion of long-period body and surface waves. The USTClitho1.0 model, although inverted from body wave datasets, is comparable with FWEA18 in fitting Rayleigh waves. The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands, possibly caused by small-scale structural heterogeneities in the shallower crust. We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high- and low-velocity anomalies. The underestimated amplitude is up to 4%, which is non-negligible considering that the overall amplitude of anomalies is only 5%−10% in the crust. These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.
{"title":"Assessment of shear wave velocity models in the Southeast Qinghai-Xizang Plateau with full-wave simulation","authors":"Wenpei Miao , Guoliang Li , Fenglin Niu , Kai Tao , Yonghua Li","doi":"10.1016/j.eqs.2025.01.007","DOIUrl":"10.1016/j.eqs.2025.01.007","url":null,"abstract":"<div><div>Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards. In this study, we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models, that is, Shen2016, FWEA18, and USTClitho1.0, leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region. Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms. The results show that the Shen2016 model, derived from traditional surface wave tomography, performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau, followed by FWEA18, built from full-waveform inversion of long-period body and surface waves. The USTClitho1.0 model, although inverted from body wave datasets, is comparable with FWEA18 in fitting Rayleigh waves. The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands, possibly caused by small-scale structural heterogeneities in the shallower crust. We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high- and low-velocity anomalies. The underestimated amplitude is up to 4%, which is non-negligible considering that the overall amplitude of anomalies is only 5%−10% in the crust. These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 159-171"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.005
Qinghua Liu , Laiyu Lu , Tongwei Qin , Lijun Chang
The spatial autocorrelation (SPAC) (also known as the Aki’s spectral method) of ambient seismic noise has been widely adopted in surface wave phase velocity extraction. In two-dimensional cases, the surface wave velocity can be calculated by fitting the SPAC coefficients with the zero-order Bessel function of the first kind or using the zeros of the Aki’s spectrum. This method has also been extended to active-source records. This study examined the application of the zeros of Aki’s spectra on active-source records using theoretical analysis and numerical simulation. We show that the zeros of the Aki’s spectrum should be associated with the zeros of the cosine function instead of the zeros of the zero-order Bessel function when extracting the phase velocity of the surface wave, considering the data acquisition and processing of the active-source records. The proposed method was then applied to the active-source data from methane explosion experiments collected using a dense array in Tongzhou, the subcenter of Beijing, for extracting the phase velocity of Rayleigh wave. The extracted dispersion curves were integrated with those obtained by beamforming the ambient noise to yield broadband dispersion curves at 0.3–6 Hz. This provides insightful results at high frequencies, at which less information can be obtained through the passive-source beamforming. The combing phase velocities from active-source with those obtained from ambient noise provide a better constrain on the shallow structure. Based on the combined fundamental mode dispersion curves at 28 excitation points, the S-wave velocity structure below the dense array is obtained by depth inversion. Due to the constraints imposed by the high-frequency information from active source, the estimated vS30 are more reliable and can be used to the site classification.
{"title":"Determination of surface-wave phase velocities by zeros of Aki’s spectrum of active-source records: Application to the dense array in Tongzhou, China","authors":"Qinghua Liu , Laiyu Lu , Tongwei Qin , Lijun Chang","doi":"10.1016/j.eqs.2025.01.005","DOIUrl":"10.1016/j.eqs.2025.01.005","url":null,"abstract":"<div><div>The spatial autocorrelation (SPAC) (also known as the Aki’s spectral method) of ambient seismic noise has been widely adopted in surface wave phase velocity extraction. In two-dimensional cases, the surface wave velocity can be calculated by fitting the SPAC coefficients with the zero-order Bessel function of the first kind or using the zeros of the Aki’s spectrum. This method has also been extended to active-source records. This study examined the application of the zeros of Aki’s spectra on active-source records using theoretical analysis and numerical simulation. We show that the zeros of the Aki’s spectrum should be associated with the zeros of the cosine function instead of the zeros of the zero-order Bessel function when extracting the phase velocity of the surface wave, considering the data acquisition and processing of the active-source records. The proposed method was then applied to the active-source data from methane explosion experiments collected using a dense array in Tongzhou, the subcenter of Beijing, for extracting the phase velocity of Rayleigh wave. The extracted dispersion curves were integrated with those obtained by beamforming the ambient noise to yield broadband dispersion curves at 0.3–6 Hz. This provides insightful results at high frequencies, at which less information can be obtained through the passive-source beamforming. The combing phase velocities from active-source with those obtained from ambient noise provide a better constrain on the shallow structure. Based on the combined fundamental mode dispersion curves at 28 excitation points, the S-wave velocity structure below the dense array is obtained by depth inversion. Due to the constraints imposed by the high-frequency information from active source, the estimated <em>v</em><sub>S30</sub> are more reliable and can be used to the site classification.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 218-233"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2025.01.004
Xiaotian Xue , Shunping Pei , Zhi Wang , Hanlin Liu , Wei Liu , Lei Li , Jiawei Li , Qian Hua
On February 6, 2023, the Türkiye Earthquake Doublet, consisting of two major earthquakes with magnitudes of MW7.8 and MW7.5, respectively, occurred within 9 h and devastated the Kahramanmaraş province in southwest Turkey. The geodynamic background of this area is exceedingly complicated owing to the combined action of the Anatolian Plate and the neighboring Eurasian, African, and Arabian plates, which contain many faults, the most prominent of which is the East Anatolian Fault Zone (EAFZ). These two earthquakes occurred on the Pazarcık Segment (PAZ.S) of the EAFZ and the Çardak Fault (CAR. F). The investigation of co-seismic changes in the velocity structure of the subterranean medium inside the focus area is critical for our understanding of earthquake ruptures. We chose <styled-content style-type="number">51572</styled-content> travel times before the earthquake doublet from January 1, 2014, to February 5, 2023, and <styled-content style-type="number">88371</styled-content> travel times after the earthquakes from February 6 to March 5, 2023, and utilized time-lapse tomography to derive the co-seismic changes in P-wave velocity. The results demonstrated that the P-wave velocity decreased around the center zone, with considerable surface displacement from the two earthquakes caused by rock breakup and stress release. The P-wave velocity increased in two areas: east of the Pazarcik Earthquake, where the Bozova Fault is located, and west of the Elbistan Earthquake. We believe that these two locations are compression zones generated by the strike-slip surface displacement. Similarly, the decrease in velocity in the areas adjacent to the Malatya Fault (MAL.F) and between the Amanos Segment (AM.S) of the EAFZ and the Savur Fault (SA.F) shows that these two locations were exposed to tension as a result of the co-seismic horizontal displacement on the surface. This study showed that in addition to the area close to the epicenter, the large earthquake can affect the velocity structure of faults far away from the main shock.
{"title":"Co-seismic P-wave velocity changes of 2023 Türkiye Earthquake Doublet","authors":"Xiaotian Xue , Shunping Pei , Zhi Wang , Hanlin Liu , Wei Liu , Lei Li , Jiawei Li , Qian Hua","doi":"10.1016/j.eqs.2025.01.004","DOIUrl":"10.1016/j.eqs.2025.01.004","url":null,"abstract":"<div><div>On February 6, 2023, the Türkiye Earthquake Doublet, consisting of two major earthquakes with magnitudes of <em>M</em><sub>W</sub>7.8 and <em>M</em><sub>W</sub>7.5, respectively, occurred within 9 h and devastated the Kahramanmaraş province in southwest Turkey. The geodynamic background of this area is exceedingly complicated owing to the combined action of the Anatolian Plate and the neighboring Eurasian, African, and Arabian plates, which contain many faults, the most prominent of which is the East Anatolian Fault Zone (EAFZ). These two earthquakes occurred on the Pazarcık Segment (PAZ.S) of the EAFZ and the Çardak Fault (CAR. F). The investigation of co-seismic changes in the velocity structure of the subterranean medium inside the focus area is critical for our understanding of earthquake ruptures. We chose <styled-content style-type=\"number\">51572</styled-content> travel times before the earthquake doublet from January 1, 2014, to February 5, 2023, and <styled-content style-type=\"number\">88371</styled-content> travel times after the earthquakes from February 6 to March 5, 2023, and utilized time-lapse tomography to derive the co-seismic changes in P-wave velocity. The results demonstrated that the P-wave velocity decreased around the center zone, with considerable surface displacement from the two earthquakes caused by rock breakup and stress release. The P-wave velocity increased in two areas: east of the Pazarcik Earthquake, where the Bozova Fault is located, and west of the Elbistan Earthquake. We believe that these two locations are compression zones generated by the strike-slip surface displacement. Similarly, the decrease in velocity in the areas adjacent to the Malatya Fault (MAL.F) and between the Amanos Segment (AM.S) of the EAFZ and the Savur Fault (SA.F) shows that these two locations were exposed to tension as a result of the co-seismic horizontal displacement on the surface. This study showed that in addition to the area close to the epicenter, the large earthquake can affect the velocity structure of faults far away from the main shock.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 263-272"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.eqs.2024.11.001
Hongyu Ni , Junlun Li , Huajian Yao , Xianliang Huang , Lingli Li , Dongrui Zhou , Xiaoli Wang , Shuyuan Yu , Yuanchao Lu , Jianfang Yu , Haigang Zheng , Guili Zhou , Hanwen Zou , Wen Yang , Ming Zhang , Guoyi Chen , Ye Lin , Guanling Peng , Zefeng Li , Haipeng Li
<div><div>At 20:08, on September 18, 2024, an <em>M</em>4.7 earthquake occurred along the Tanlu fault zone in the Feidong County of Hefei, Anhui Province. This earthquake is the largest event in the modern history of Hefei, which caused substantial social impact. To reveal the seismogenic structure of the <em>M</em>4.7 Feidong earthquake sequence and assess seismic risks, we use data from both the permanent seismic network and a temporary dense nodal array deployed in the epicentral region prior to the mainshock for: (1) accurate location of the earthquake sequence and determination of the focal mechanisms; (2) obtaining the spatiotemporal distribution, <em>b</em>-value, and half-day occurrence frequency of the earthquake sequence. The Sentinel-1 satellite data are used to analyze the coseismic displacement. Additionally, velocity models from regional tomography and local high-resolution 2D active- and passive-source surveys across the Tanlu fault zone in the epicentral area are also used to reveal the detailed geometry of the seismogenic fault. The results indicate: (1) the <em>M</em>4.7 Feidong earthquake sequence is concentrated around 10.5 km in depth along a NW-dipping, subvertical fault which trends NE and is approximately 5 km in length; the focal mechanism solution also reveals that the fault hosting the mainshock is a subvertical strike-slip fault, driven by the regional compressional stress in ENE-WSW; the coseismic horizontal displacement on the surface caused by the <em>M</em>4.7 mainshock has a maximum value close to 1 mm; (2) the regional velocity model shows significant lateral variation in <em>v</em><sub>S</sub> in the source region, with the mainshock occurring in the area with higher velocity; high-resolution P-wave velocity structures obtained by full waveform inversion from active sources, and S-wave velocity structures from passive-source ambient noise tomography indicate that the mainshock occurred along the boundary between high- and low-velocity bodies, and the seismogenic fault dips NW; the deep seismic reflection profiling shows that the mainshock occurred within the Jurassic strata; (3) based on these results, we suggest the seismogenic fault for the <em>M</em>4.7 Feidong earthquake is either the Zhuding-Shimenshan fault, one of the major faults in the Tanlu fault zone, or a hidden fault to the east; the intersection of the NE-trending Tanlu fault zone and the WNW-trending Feizhong fault, along with significant velocity variations, likely create local stress concentrations which could have triggered the <em>M</em>4.7 Feidong earthquake sequence; (4) the strong aftershocks following the <em>M</em>4.7 Feidong mainshock did not further extend the fault rupture zone; the active period of the Zhuding-Shimenshan fault was the late Early Pleistocene to Middle Pleistocene, and the imaging results indicate that this fault does not cut through the shallow Feidong depression. In conjunction with the small coseismic rupture area, i
{"title":"Preliminary study of the tectonic structure and seismogenic environment of the M4.7 Feidong earthquake sequence on September 18, 2024 in Hefei","authors":"Hongyu Ni , Junlun Li , Huajian Yao , Xianliang Huang , Lingli Li , Dongrui Zhou , Xiaoli Wang , Shuyuan Yu , Yuanchao Lu , Jianfang Yu , Haigang Zheng , Guili Zhou , Hanwen Zou , Wen Yang , Ming Zhang , Guoyi Chen , Ye Lin , Guanling Peng , Zefeng Li , Haipeng Li","doi":"10.1016/j.eqs.2024.11.001","DOIUrl":"10.1016/j.eqs.2024.11.001","url":null,"abstract":"<div><div>At 20:08, on September 18, 2024, an <em>M</em>4.7 earthquake occurred along the Tanlu fault zone in the Feidong County of Hefei, Anhui Province. This earthquake is the largest event in the modern history of Hefei, which caused substantial social impact. To reveal the seismogenic structure of the <em>M</em>4.7 Feidong earthquake sequence and assess seismic risks, we use data from both the permanent seismic network and a temporary dense nodal array deployed in the epicentral region prior to the mainshock for: (1) accurate location of the earthquake sequence and determination of the focal mechanisms; (2) obtaining the spatiotemporal distribution, <em>b</em>-value, and half-day occurrence frequency of the earthquake sequence. The Sentinel-1 satellite data are used to analyze the coseismic displacement. Additionally, velocity models from regional tomography and local high-resolution 2D active- and passive-source surveys across the Tanlu fault zone in the epicentral area are also used to reveal the detailed geometry of the seismogenic fault. The results indicate: (1) the <em>M</em>4.7 Feidong earthquake sequence is concentrated around 10.5 km in depth along a NW-dipping, subvertical fault which trends NE and is approximately 5 km in length; the focal mechanism solution also reveals that the fault hosting the mainshock is a subvertical strike-slip fault, driven by the regional compressional stress in ENE-WSW; the coseismic horizontal displacement on the surface caused by the <em>M</em>4.7 mainshock has a maximum value close to 1 mm; (2) the regional velocity model shows significant lateral variation in <em>v</em><sub>S</sub> in the source region, with the mainshock occurring in the area with higher velocity; high-resolution P-wave velocity structures obtained by full waveform inversion from active sources, and S-wave velocity structures from passive-source ambient noise tomography indicate that the mainshock occurred along the boundary between high- and low-velocity bodies, and the seismogenic fault dips NW; the deep seismic reflection profiling shows that the mainshock occurred within the Jurassic strata; (3) based on these results, we suggest the seismogenic fault for the <em>M</em>4.7 Feidong earthquake is either the Zhuding-Shimenshan fault, one of the major faults in the Tanlu fault zone, or a hidden fault to the east; the intersection of the NE-trending Tanlu fault zone and the WNW-trending Feizhong fault, along with significant velocity variations, likely create local stress concentrations which could have triggered the <em>M</em>4.7 Feidong earthquake sequence; (4) the strong aftershocks following the <em>M</em>4.7 Feidong mainshock did not further extend the fault rupture zone; the active period of the Zhuding-Shimenshan fault was the late Early Pleistocene to Middle Pleistocene, and the imaging results indicate that this fault does not cut through the shallow Feidong depression. In conjunction with the small coseismic rupture area, i","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 234-252"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The three-dimensional (3D) geometry of a fault is a critical control on earthquake nucleation, dynamic rupture, stress triggering, and related seismic hazards. Therefore, a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards. Utilising the SKUA GoCAD software, we constructed detailed seismic fault models for the 2021 MS6.4 Yangbi earthquake in Yunnan, China, using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow. Our analysis revealed a NW-striking main fault with a high-angle SW dip, accompanied by two branch faults. Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault, whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern. Additionally, a third ENE-striking short fault was identified NE of the main fault. In combination with the spatial distribution of pre-existing faults, our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW- and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone. The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones, through either cascade or conjugate rupture modes, can cause unexpected moderate-large earthquakes and severe disasters, necessitating attention in regions like southeast Xizang, which have complex fault systems.
{"title":"Building the 3D seismic fault models for the 2021 MS6.4 Yunnan Yangbi earthquake: The potential role of pre-existing faults in generating unexpected moderate-strong earthquakes in southeast Xizang","authors":"Xiao Sun, Jinyu Zhang, Renqi Lu, Wei Wang, Peng Su, Guanshen Liu, Fang Xu","doi":"10.1016/j.eqs.2024.12.001","DOIUrl":"10.1016/j.eqs.2024.12.001","url":null,"abstract":"<div><div>The three-dimensional (3D) geometry of a fault is a critical control on earthquake nucleation, dynamic rupture, stress triggering, and related seismic hazards. Therefore, a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards. Utilising the SKUA GoCAD software, we constructed detailed seismic fault models for the 2021 <em>M</em><sub>S</sub>6.4 Yangbi earthquake in Yunnan, China, using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow. Our analysis revealed a NW-striking main fault with a high-angle SW dip, accompanied by two branch faults. Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault, whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern. Additionally, a third ENE-striking short fault was identified NE of the main fault. In combination with the spatial distribution of pre-existing faults, our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW- and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone. The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones, through either cascade or conjugate rupture modes, can cause unexpected moderate-large earthquakes and severe disasters, necessitating attention in regions like southeast Xizang, which have complex fault systems.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 3","pages":"Pages 172-186"},"PeriodicalIF":1.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.eqs.2024.11.005
Mona Mohammed , Omar M. Saad , Arabi Keshk , Hatem M. Ahmed
The level of ground shaking, as determined by the peak ground acceleration (PGA), can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures. Predicting the PGA immediately after an earthquake occurs allows for the issuing of a warning by an earthquake early warning system. In this study, we propose a deep learning model, ConvMixer, to predict the PGA recorded by weak-motion velocity seismometers in Japan. We use 5-s three-component seismograms, from 2 s before until 3 s after the P-wave arrival time of the earthquake. Our dataset comprised more than 50,000 single-station waveforms recorded by 10 seismic stations in the K-NET, Kiki-NET, and Hi-Net networks between 2004 and 2023. The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions. The proposed ConvMixer network had a mean absolute error of 2.143 when applied to the test set and outperformed benchmark deep learning models. In addition, the proposed ConvMixer demonstrated the ability to predict the PGA at the corresponding station site based on 1-second waveforms obtained immediately after the arrival time of the P-wave.
{"title":"Predicting peak ground acceleration using the ConvMixer network","authors":"Mona Mohammed , Omar M. Saad , Arabi Keshk , Hatem M. Ahmed","doi":"10.1016/j.eqs.2024.11.005","DOIUrl":"10.1016/j.eqs.2024.11.005","url":null,"abstract":"<div><div>The level of ground shaking, as determined by the peak ground acceleration (PGA), can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures. Predicting the PGA immediately after an earthquake occurs allows for the issuing of a warning by an earthquake early warning system. In this study, we propose a deep learning model, ConvMixer, to predict the PGA recorded by weak-motion velocity seismometers in Japan. We use 5-s three-component seismograms, from 2 s before until 3 s after the P-wave arrival time of the earthquake. Our dataset comprised more than 50,000 single-station waveforms recorded by 10 seismic stations in the K-NET, Kiki-NET, and Hi-Net networks between 2004 and 2023. The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions. The proposed ConvMixer network had a mean absolute error of 2.143 when applied to the test set and outperformed benchmark deep learning models. In addition, the proposed ConvMixer demonstrated the ability to predict the PGA at the corresponding station site based on 1-second waveforms obtained immediately after the arrival time of the P-wave.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 2","pages":"Pages 126-135"},"PeriodicalIF":1.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.eqs.2024.11.003
Lupei Zhu
Scientific research is a journey into an uncharted territory. Researchers need to have the big picture for navigation and at the same time be detail-oriented, as details make a difference. Here I offer a few tips for conducting research that I summarized based on my 30+ years of research experience.
{"title":"A few nifty tips for conducting scientific research","authors":"Lupei Zhu","doi":"10.1016/j.eqs.2024.11.003","DOIUrl":"10.1016/j.eqs.2024.11.003","url":null,"abstract":"<div><div>Scientific research is a journey into an uncharted territory. Researchers need to have the big picture for navigation and at the same time be detail-oriented, as details make a difference. Here I offer a few tips for conducting research that I summarized based on my 30+ years of research experience.</div></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"38 2","pages":"Pages 156-158"},"PeriodicalIF":1.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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