Seismic networks worldwide are designed to monitor seismic ground motion. This process includes identifying seismic events in the signals, picking and associating seismic phases, determining the event’s location, and calculating its magnitude. Although machine-learning (ML) methods have shown significant improvements in some of these steps individually, there are other stages in which traditional non-ML algorithms outperform ML approaches. We introduce SeisMonitor, a Python open-source package to monitor seismic activity that uses ready-made ML methods for event detection, phase picking and association, and other well-known methods for the rest of the steps. We apply these steps in a totally automated process for almost 7 yr (2016–2022) in three seismic networks located in Colombian territory, the Colombian seismic network and two local and temporary networks in northern South America: the Middle Magdalena Valley and the Caribbean-Mérida Andes seismic arrays. The results demonstrate the reliability of this method in creating automated seismic catalogs, showcasing earthquake detection capabilities and location accuracy similar to standard catalogs. Furthermore, it effectively identifies significant tectonic structures and emphasizes local crustal faults. In addition, it has the potential to enhance earthquake processing efficiency and serve as a valuable supplement to manual catalogs, given its ability at detecting minor earthquakes and aftershocks.
全球地震网络旨在监测地震地面运动。这一过程包括识别信号中的地震事件、挑选和关联地震相位、确定事件位置以及计算震级。虽然机器学习（ML）方法在其中一些步骤上有了显著的改进，但在其他一些阶段，传统的非 ML 算法也优于 ML 方法。我们介绍的 SeisMonitor 是一款用于监测地震活动的 Python 开源软件包，它使用现成的 ML 方法进行事件检测、相位选择和关联，并在其余步骤中使用其他众所周知的方法。我们在哥伦比亚境内的三个地震网络（哥伦比亚地震网络和南美洲北部的两个本地临时网络：中马格达莱纳河谷和加勒比海-梅里达安第斯地震阵列）中应用了这些步骤，整个过程完全自动化，历时近 7 年（2016-2022 年）。研究结果表明，这种方法在创建自动地震目录方面非常可靠，其地震探测能力和定位精度与标准目录类似。此外，它还能有效识别重要的构造结构，并强调局部地壳断层。此外，该方法还具有提高地震处理效率的潜力，并且由于其检测小震和余震的能力，可作为人工地震目录的重要补充。
{"title":"Colombian Seismic Monitoring Using Advanced Machine-Learning Algorithms","authors":"Emmanuel Castillo, Daniel Siervo, G. Prieto","doi":"10.1785/0220240036","DOIUrl":"https://doi.org/10.1785/0220240036","url":null,"abstract":"\u0000 Seismic networks worldwide are designed to monitor seismic ground motion. This process includes identifying seismic events in the signals, picking and associating seismic phases, determining the event’s location, and calculating its magnitude. Although machine-learning (ML) methods have shown significant improvements in some of these steps individually, there are other stages in which traditional non-ML algorithms outperform ML approaches. We introduce SeisMonitor, a Python open-source package to monitor seismic activity that uses ready-made ML methods for event detection, phase picking and association, and other well-known methods for the rest of the steps. We apply these steps in a totally automated process for almost 7 yr (2016–2022) in three seismic networks located in Colombian territory, the Colombian seismic network and two local and temporary networks in northern South America: the Middle Magdalena Valley and the Caribbean-Mérida Andes seismic arrays. The results demonstrate the reliability of this method in creating automated seismic catalogs, showcasing earthquake detection capabilities and location accuracy similar to standard catalogs. Furthermore, it effectively identifies significant tectonic structures and emphasizes local crustal faults. In addition, it has the potential to enhance earthquake processing efficiency and serve as a valuable supplement to manual catalogs, given its ability at detecting minor earthquakes and aftershocks.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Runqing Huang, Xinlei Sun, Peng Zhang, Yangfan Deng
After the impoundment of the Xinfengjiang Reservoir (XFJR) in Guangdong, China, numerous earthquakes occurred in the area, including a magnitude 6.1 event in 1962. Analysis of historical earthquakes indicates that M ≥ 4 earthquakes began occurring in the northwestern XFJR in 2012, and seismicity has gradually migrated from the southeastern to the northwestern reservoir (NWR). However, the mechanisms governing the migration of seismicity and the current upper-crustal structure beneath the reservoir area remain unclear. In our study, we conducted tomographic imaging by combining waveform data from short-period and permanent stations to construct a 3D velocity model. Our high-resolution velocity models revealed a horizontal fractured zone at ∼5 km depth that extends from the southeastern to northwestern XFJR, and a steep fault that extends to about 9 km depth. These two fractured zones may interact with each other, allowing for fluid infiltration and contributing to earthquake triggering via pore pressure diffusion in the XFJR areas. Furthermore, the calculation of Coulomb stress changes indicated that microearthquakes in the southeastern XFJR may contribute to the seismicity in the NWR. However, the influences of M ≥ 4 earthquakes in the northwestern XFJR on subsequent M ≥ 4 earthquakes in the southeastern XFJR vary differently. Our results provide crucial insights for understanding the migration of microearthquakes in the XFJR area.
{"title":"Seismicity Migration and the Upper Crustal Structure in the Xinfengjiang Reservoir","authors":"Runqing Huang, Xinlei Sun, Peng Zhang, Yangfan Deng","doi":"10.1785/0220230369","DOIUrl":"https://doi.org/10.1785/0220230369","url":null,"abstract":"\u0000 After the impoundment of the Xinfengjiang Reservoir (XFJR) in Guangdong, China, numerous earthquakes occurred in the area, including a magnitude 6.1 event in 1962. Analysis of historical earthquakes indicates that M ≥ 4 earthquakes began occurring in the northwestern XFJR in 2012, and seismicity has gradually migrated from the southeastern to the northwestern reservoir (NWR). However, the mechanisms governing the migration of seismicity and the current upper-crustal structure beneath the reservoir area remain unclear. In our study, we conducted tomographic imaging by combining waveform data from short-period and permanent stations to construct a 3D velocity model. Our high-resolution velocity models revealed a horizontal fractured zone at ∼5 km depth that extends from the southeastern to northwestern XFJR, and a steep fault that extends to about 9 km depth. These two fractured zones may interact with each other, allowing for fluid infiltration and contributing to earthquake triggering via pore pressure diffusion in the XFJR areas. Furthermore, the calculation of Coulomb stress changes indicated that microearthquakes in the southeastern XFJR may contribute to the seismicity in the NWR. However, the influences of M ≥ 4 earthquakes in the northwestern XFJR on subsequent M ≥ 4 earthquakes in the southeastern XFJR vary differently. Our results provide crucial insights for understanding the migration of microearthquakes in the XFJR area.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"38 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intersections between small faults and larger faults are ubiquitous throughout the world, including the strike-slip San Andreas system in southern California. In particular, orthogonal intersections may exist in the Brawley seismic zone (BSZ) in the Salton Sea region between small left-lateral strike-slip faults and the main southern San Andreas fault (SSAF). This area often experiences earthquake swarms, which poses the question of whether moderate earthquakes on these left-lateral cross faults (CFs) may propagate to the nearby SSAF, triggering a large, damaging event. To address this question, we present a collection of dynamic rupture scenarios describing the interaction of a representative CF intersecting the highly prestressed SSAF in the BSZ. Our models span a variety of CF earthquake rupture scenarios that vary in magnitude (Mw∼5.2–6.1), rupture depth, location, and directivity to test their potential to trigger the SSAF. We use our models to investigate how the above parameters play an interconnected role in developing ruptures that might trigger the SSAF. Our results highlight that adjacency to the SSAF and shallow rupture enhance the ability of moderate-size CF earthquakes to propagate onto the SSAF. We also show that earthquakes starting at the opposite edge of the CF from the intersection are less likely to trigger the SSAF unless they propagate over at least half of the CF length. Our experiments provide for the first time a benchmark of comparison and insights into rupture parameters that might control the initiation of a significant SSAF event from a smaller CF earthquake. They may also give insight into the general interactions of small faults with larger intersecting faults, such as in the case of the recent 2023 Kahramanmaraş, Türkiye, earthquake.
{"title":"Direct Dynamic Triggering Scenarios of the Southern San Andreas Fault by Moderate-Magnitude Cross-Fault Earthquakes in the Brawley Seismic Zone, California","authors":"Christodoulos Kyriakopoulos, D. Oglesby","doi":"10.1785/0220230326","DOIUrl":"https://doi.org/10.1785/0220230326","url":null,"abstract":"\u0000 Intersections between small faults and larger faults are ubiquitous throughout the world, including the strike-slip San Andreas system in southern California. In particular, orthogonal intersections may exist in the Brawley seismic zone (BSZ) in the Salton Sea region between small left-lateral strike-slip faults and the main southern San Andreas fault (SSAF). This area often experiences earthquake swarms, which poses the question of whether moderate earthquakes on these left-lateral cross faults (CFs) may propagate to the nearby SSAF, triggering a large, damaging event. To address this question, we present a collection of dynamic rupture scenarios describing the interaction of a representative CF intersecting the highly prestressed SSAF in the BSZ. Our models span a variety of CF earthquake rupture scenarios that vary in magnitude (Mw∼5.2–6.1), rupture depth, location, and directivity to test their potential to trigger the SSAF. We use our models to investigate how the above parameters play an interconnected role in developing ruptures that might trigger the SSAF. Our results highlight that adjacency to the SSAF and shallow rupture enhance the ability of moderate-size CF earthquakes to propagate onto the SSAF. We also show that earthquakes starting at the opposite edge of the CF from the intersection are less likely to trigger the SSAF unless they propagate over at least half of the CF length. Our experiments provide for the first time a benchmark of comparison and insights into rupture parameters that might control the initiation of a significant SSAF event from a smaller CF earthquake. They may also give insight into the general interactions of small faults with larger intersecting faults, such as in the case of the recent 2023 Kahramanmaraş, Türkiye, earthquake.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"120 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Acoustic waves are widely used to characterize explosive sources such as volcanoes, meteorites, and controlled explosions. This study examines the potential role of ground coupled airwaves (GCA), which effectively propagate at acoustic speeds (∼0.34 km/s) before coupling to the ground near seismometers, in aiding local discrimination between low-yield explosions in shallow boreholes and earthquakes. GCA generated by shallow borehole explosions from the 2014 imaging magma under St. Helens experiment (ML 0.9–2.3) and earthquakes (ML 2–3.4) from 2014 to 2016, were recorded by various seismometers at <150 km source–receiver distance. Potential GCA are analyzed using arrays of broadband seismometers (number of seismometers, n = 85), nodal seismometers with 10-Hz geophones atop the surface (n = 904), and Texan dataloggers with shallowly buried 4.5-Hz geophones (n = 2535). Array-based detections are defined using the distributions of short-time average over long-time average functions in time windows during and adjacent to the predicted GCA arrival for direct source–receiver transmission. GCA are detected for 14 of 23 borehole explosions and 0 of 34 earthquakes. All detections occurred during times of low-mean wind speed (<0.5 m/s) at ground-based weather stations. GCA amplitudes exhibit strong spatial variability, and the number of spatially distributed receivers appears more important for GCA detection than the type of seismometer installation. GCA detections were compared with seismic P/S amplitude ratios, which are a common source discriminant, and field logs of whether the borehole explosions ejected any mass or deformed the surface. No clear correlation was found with either type of source information, suggesting that heterogeneous propagation and near-receiver effects like wind noise are more influential than variations in source processes among the 23 explosions. Our results indicate that local seismic detection of GCA may valuably complement discrimination metrics like P/S ratios, with a low tendency for false-positive indications of explosions but a high tendency for false negatives.
{"title":"Local Detection of Ground Coupled Acoustic Waves with Seismic Arrays and Their Potential Role in the Discrimination of Explosions and Earthquakes","authors":"Olumide Adedeji, Brandon Schmandt","doi":"10.1785/0220230367","DOIUrl":"https://doi.org/10.1785/0220230367","url":null,"abstract":"\u0000 Acoustic waves are widely used to characterize explosive sources such as volcanoes, meteorites, and controlled explosions. This study examines the potential role of ground coupled airwaves (GCA), which effectively propagate at acoustic speeds (∼0.34 km/s) before coupling to the ground near seismometers, in aiding local discrimination between low-yield explosions in shallow boreholes and earthquakes. GCA generated by shallow borehole explosions from the 2014 imaging magma under St. Helens experiment (ML 0.9–2.3) and earthquakes (ML 2–3.4) from 2014 to 2016, were recorded by various seismometers at <150 km source–receiver distance. Potential GCA are analyzed using arrays of broadband seismometers (number of seismometers, n = 85), nodal seismometers with 10-Hz geophones atop the surface (n = 904), and Texan dataloggers with shallowly buried 4.5-Hz geophones (n = 2535). Array-based detections are defined using the distributions of short-time average over long-time average functions in time windows during and adjacent to the predicted GCA arrival for direct source–receiver transmission. GCA are detected for 14 of 23 borehole explosions and 0 of 34 earthquakes. All detections occurred during times of low-mean wind speed (<0.5 m/s) at ground-based weather stations. GCA amplitudes exhibit strong spatial variability, and the number of spatially distributed receivers appears more important for GCA detection than the type of seismometer installation. GCA detections were compared with seismic P/S amplitude ratios, which are a common source discriminant, and field logs of whether the borehole explosions ejected any mass or deformed the surface. No clear correlation was found with either type of source information, suggesting that heterogeneous propagation and near-receiver effects like wind noise are more influential than variations in source processes among the 23 explosions. Our results indicate that local seismic detection of GCA may valuably complement discrimination metrics like P/S ratios, with a low tendency for false-positive indications of explosions but a high tendency for false negatives.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"120 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We apply the spatial autocorrelation (SPAC) method to construct the 3D subsurface shear-wave velocity structure model using the short-period dense seismic array (containing 725 nodal geophones) located at the Guangdong–Hong Kong–Macao Greater Bay area (GBA). We first divided the dense array into numerous subarrays, with each subarray consisting of nine nodal geophones, and obtained 562 subarrays that can provide 1D VS profiles of the same quantity. Then, the SPAC method and genetic algorithm are utilized to extract the dispersion curve of the Rayleigh wave from the raw microtremor data and invert VS structure, respectively. Finally, a 3D VS structure model from the surface to 3.3 km depth is derived by combining all 1D VS structures. Relatively low-velocity anomalies above 700 m are considered unconsolidated shallow sediments as well as relatively high-velocity anomalies beneath 1100 m are attributed to consolidated granite bedrock. Meanwhile, low-velocity anomalies that are identified through the vertical VS profile at a depth of about 900–3000 m can be contributed to the fractured zone, and striped low-velocity anomalies in the horizontal VS maps reveal the location of the deeply buried faults in the study area. The results also mean that the SPAC method combined with the records of short-period dense seismic array can be effectively applied to image subsurface structures in high-populated urban area. The development of this noise-resistance and environment-friendly geophysical technique provides a reliable and effective way to explore the complicated subsurface geological structures, which is of great significance to urban engineering construction and earthquake disaster reduction work in densely populated urban agglomerations.
利用位于粤港澳大湾区（GBA）的短周期地震密集阵（包含725个节点检波器），采用空间自相关（SPAC）方法构建了三维地下剪切波速度结构模型。我们首先将密集地震阵划分为许多子阵，每个子阵由 9 个节点检波器组成，得到了 562 个子阵，这些子阵可以提供相同数量的一维 VS 剖面。然后，利用 SPAC 方法和遗传算法分别从原始微震数据中提取瑞利波的频散曲线和反演 VS 结构。最后，结合所有一维 VS 结构，得出了从地表到 3.3 千米深度的三维 VS 结构模型。700米以上的相对低速异常被认为是未固结的浅层沉积物，而1100米以下的相对高速异常则被认为是固结的花岗岩基岩。同时，通过垂直VS剖面发现的约900-3000米深度的低速异常可能是断裂带的结果，而水平VS图中的条状低速异常则揭示了研究区域深埋断层的位置。研究结果还表明，结合短周期密集地震阵记录的 SPAC 方法可以有效地应用于人口稠密的城市地区的地下结构成像。这种抗噪声、环境友好型地球物理技术的发展，为探索复杂的地下地质构造提供了可靠有效的方法，对人口密集城市群的城市工程建设和防震减灾工作具有重要意义。
{"title":"Fine Shear-Wave Velocity Structures of Subsurface beneath the Guangdong–Hong Kong–Macao Greater Bay Area with Dense Seismic Array and SPAC Method","authors":"QiAn Pan, Xuzhang Shen, Xiuwei Ye, Liwei Wang","doi":"10.1785/0220230310","DOIUrl":"https://doi.org/10.1785/0220230310","url":null,"abstract":"\u0000 We apply the spatial autocorrelation (SPAC) method to construct the 3D subsurface shear-wave velocity structure model using the short-period dense seismic array (containing 725 nodal geophones) located at the Guangdong–Hong Kong–Macao Greater Bay area (GBA). We first divided the dense array into numerous subarrays, with each subarray consisting of nine nodal geophones, and obtained 562 subarrays that can provide 1D VS profiles of the same quantity. Then, the SPAC method and genetic algorithm are utilized to extract the dispersion curve of the Rayleigh wave from the raw microtremor data and invert VS structure, respectively. Finally, a 3D VS structure model from the surface to 3.3 km depth is derived by combining all 1D VS structures. Relatively low-velocity anomalies above 700 m are considered unconsolidated shallow sediments as well as relatively high-velocity anomalies beneath 1100 m are attributed to consolidated granite bedrock. Meanwhile, low-velocity anomalies that are identified through the vertical VS profile at a depth of about 900–3000 m can be contributed to the fractured zone, and striped low-velocity anomalies in the horizontal VS maps reveal the location of the deeply buried faults in the study area. The results also mean that the SPAC method combined with the records of short-period dense seismic array can be effectively applied to image subsurface structures in high-populated urban area. The development of this noise-resistance and environment-friendly geophysical technique provides a reliable and effective way to explore the complicated subsurface geological structures, which is of great significance to urban engineering construction and earthquake disaster reduction work in densely populated urban agglomerations.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"121 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During hydraulic fracturing, real-time acquisition of the spatiotemporal distribution of microseismic in the reservoir is essential in evaluating the risk of induced seismicity and optimizing injection parameters. By integrating deep learning with migration-based location methods, we develop an automatic microseismic locating workflow (named DMLoc). DMLoc applies deep learning to automate phase picking and leverage the phase arrival probability function generated by a convolutional network as the input for waveform migration. The proposed workflow is first applied to the continuous data of the Dawson-Septimus area. Compared with a reference catalog generated by the SeisComP3 software, our method automatically locates 57 additional seismic events (accounting for 43% of the events in the obtained catalog). We further evaluate the performance of DMLoc by applying it to a 35-day continuous microseismic dataset from the Tony Creek Dual Microseismic Experiment. The spatiotemporal distribution of our detected events is consistent with results reported in prior catalogs, demonstrating the effectiveness of our method. In contrast to using raw microseismic records for stacking, DMLoc addresses the issue of inaccurate locating caused by low signal-to-noise ratios and polarity changes. The use of GPUs has substantially accelerated the calculations and enabled DMLoc to output locating results in minutes. This fast and efficient metric could be easily extended to any microseismic monitoring scenario that requires (near) real-time locations and assists in site-based risk mitigation and industrial operation optimization.
{"title":"DMLoc: Automatic Microseismic Locating Workflow Based on Deep Learning and Waveform Migration","authors":"Yizhuo Liu, Jing Zheng, Ruijia Wang, Suping Peng, Shuaishuai Shen","doi":"10.1785/0220230391","DOIUrl":"https://doi.org/10.1785/0220230391","url":null,"abstract":"\u0000 During hydraulic fracturing, real-time acquisition of the spatiotemporal distribution of microseismic in the reservoir is essential in evaluating the risk of induced seismicity and optimizing injection parameters. By integrating deep learning with migration-based location methods, we develop an automatic microseismic locating workflow (named DMLoc). DMLoc applies deep learning to automate phase picking and leverage the phase arrival probability function generated by a convolutional network as the input for waveform migration. The proposed workflow is first applied to the continuous data of the Dawson-Septimus area. Compared with a reference catalog generated by the SeisComP3 software, our method automatically locates 57 additional seismic events (accounting for 43% of the events in the obtained catalog). We further evaluate the performance of DMLoc by applying it to a 35-day continuous microseismic dataset from the Tony Creek Dual Microseismic Experiment. The spatiotemporal distribution of our detected events is consistent with results reported in prior catalogs, demonstrating the effectiveness of our method. In contrast to using raw microseismic records for stacking, DMLoc addresses the issue of inaccurate locating caused by low signal-to-noise ratios and polarity changes. The use of GPUs has substantially accelerated the calculations and enabled DMLoc to output locating results in minutes. This fast and efficient metric could be easily extended to any microseismic monitoring scenario that requires (near) real-time locations and assists in site-based risk mitigation and industrial operation optimization.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"114 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.
{"title":"Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging","authors":"Yuhang Lei, Baoshan Wang","doi":"10.1785/0220240050","DOIUrl":"https://doi.org/10.1785/0220240050","url":null,"abstract":"\u0000 Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"26 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Much of what is known about the effects of the 1886 Charleston, South Carolina, earthquake throughout the epicentral region can be attributed to meticulous field investigations by an individual with training in geology and engineering, Earle Sloan (Clendenin, 1926). In a recent study, Bilham and Hough (2024) undertook a detailed analysis of the effects of the earthquake on railroads in the Charleston region, drawing heavily from Sloan’s reports. This exercise identified several inconsistencies in Sloan’s field reports, including understandable measurement imprecision, inferred data entry mistakes, and transcription errors. The study also begged the question, where was Sloan at the time of the mainshock and over the following week? And to what extent did he draw from secondhand information in compiling his reports? On this question Sloan’s reports were sometimes enigmatic, lending themselves to misinterpretation in contemporaneous as well as modern interpretations. Beyond the details that were germane for, and briefly summarized by, the studies of Bilham and Hough (2023, 2024), in this report we don our historical seismologist caps to chronicle Sloan’s activities following the earthquake. We summarize our inferences here for the benefit of future scholars who might attempt to retrace either Sloan’s footsteps or our own. This study also serves to highlight Sloan’s singular contributions to earthquake science, which were never published separately.
{"title":"On the Provenance of Field Reports of the 1886 Charleston, South Carolina, Earthquake: A Seismo-Historical Whodunnit","authors":"Susan E. Hough, R. Bilham","doi":"10.1785/0220240055","DOIUrl":"https://doi.org/10.1785/0220240055","url":null,"abstract":"\u0000 Much of what is known about the effects of the 1886 Charleston, South Carolina, earthquake throughout the epicentral region can be attributed to meticulous field investigations by an individual with training in geology and engineering, Earle Sloan (Clendenin, 1926). In a recent study, Bilham and Hough (2024) undertook a detailed analysis of the effects of the earthquake on railroads in the Charleston region, drawing heavily from Sloan’s reports. This exercise identified several inconsistencies in Sloan’s field reports, including understandable measurement imprecision, inferred data entry mistakes, and transcription errors. The study also begged the question, where was Sloan at the time of the mainshock and over the following week? And to what extent did he draw from secondhand information in compiling his reports? On this question Sloan’s reports were sometimes enigmatic, lending themselves to misinterpretation in contemporaneous as well as modern interpretations. Beyond the details that were germane for, and briefly summarized by, the studies of Bilham and Hough (2023, 2024), in this report we don our historical seismologist caps to chronicle Sloan’s activities following the earthquake. We summarize our inferences here for the benefit of future scholars who might attempt to retrace either Sloan’s footsteps or our own. This study also serves to highlight Sloan’s singular contributions to earthquake science, which were never published separately.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yashan Feng, Neng Xiong, Bin Shan, Rongjiang Wang, Xiong Xiong
The rate–state frictional law, coupled with the Coulomb failure stress changes (ΔCFS), is one of the most popular physics-based models to forecast seismicity rate changes following a major earthquake. However, its effectiveness is hampered by parameter uncertainties. To seek possible solutions for such uncertainties, this article carried out retrospective forecasts of the decade-long seismicity in the Longmenshan region, China, after the 2008 Mw 7.9 Wenchuan earthquake, and proposed methods to constrain parameter uncertainties. First, we derived spatially variable ta and Aσ from fault-slip rates. This method not only provides observational constraints for these two parameters but also reflects spatial variations of fault rate–state properties. Second, although both complete and declustered background catalogs are common in Coulomb rate–state forecasts, this study demonstrated that declustering avoids false alerts of seismicity rate increase that resulted from temporary seismicity fluctuations in the background catalog. Finally, we extended the model from its typical application based on a stress step (the coseismic stress change) to a calculation that allows a more complex stress evolution (the postseismic viscoelastic stress change). With these methods to constrain parameter uncertainties, we are able to obtain more reliable forecasts.
速率状态摩擦定律与库仑破坏应力变化（ΔCFS）相结合，是预报大地震后地震活动率变化的最常用物理模型之一。然而，其有效性受到参数不确定性的影响。为了寻求解决这种不确定性的可能方案，本文对 2008 年汶川 7.9 级地震后中国龙门山地区十年的地震活动进行了回顾性预报，并提出了约束参数不确定性的方法。首先，我们从断层滑动率推导出空间可变的 ta 和 Aσ。这种方法不仅为这两个参数提供了观测约束，而且反映了断层速率态属性的空间变化。其次，虽然库仑率态预报中常见的是完整和去簇背景目录，但本研究证明，去簇可避免因背景目录中的临时地震波动而导致的地震率上升的错误警报。最后，我们将模型从基于应力阶跃（共震应力变化）的典型应用扩展到允许更复杂应力演变（震后粘弹应力变化）的计算。有了这些限制参数不确定性的方法，我们就能获得更可靠的预测。
{"title":"Constraining Parameter Uncertainties in the Coulomb Rate–State Approach: A Case Study of Seismicity in the Longmenshan Region after the 2008 Mw 7.9 Wenchuan Earthquake, China","authors":"Yashan Feng, Neng Xiong, Bin Shan, Rongjiang Wang, Xiong Xiong","doi":"10.1785/0220230414","DOIUrl":"https://doi.org/10.1785/0220230414","url":null,"abstract":"\u0000 The rate–state frictional law, coupled with the Coulomb failure stress changes (ΔCFS), is one of the most popular physics-based models to forecast seismicity rate changes following a major earthquake. However, its effectiveness is hampered by parameter uncertainties. To seek possible solutions for such uncertainties, this article carried out retrospective forecasts of the decade-long seismicity in the Longmenshan region, China, after the 2008 Mw 7.9 Wenchuan earthquake, and proposed methods to constrain parameter uncertainties. First, we derived spatially variable ta and Aσ from fault-slip rates. This method not only provides observational constraints for these two parameters but also reflects spatial variations of fault rate–state properties. Second, although both complete and declustered background catalogs are common in Coulomb rate–state forecasts, this study demonstrated that declustering avoids false alerts of seismicity rate increase that resulted from temporary seismicity fluctuations in the background catalog. Finally, we extended the model from its typical application based on a stress step (the coseismic stress change) to a calculation that allows a more complex stress evolution (the postseismic viscoelastic stress change). With these methods to constrain parameter uncertainties, we are able to obtain more reliable forecasts.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"354 11‐12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We proposed a deep learning (DL) method to derive VS models from joint inversion of Rayleigh-wave dispersions and receiver functions, which is based on multilabel convolutional neural network and recurrent neural network. We used a spline-based approach to generate synthetic models instead of directly using existing models to build the training data set, which improves the generalization of the method. Unlike the traditional methods, which usually set a fixed VP/VS ratio, our method makes full use of the powerful data mining ability of DL to invert the VS models assuming different VP/VS ratios. A loss function is specially designed that focuses on key features of the model space, for example, the shape and depth of Moho. Synthetic tests demonstrate that the proposed method is accurate and fast. Application to the southeast margin of the Tibetan Plateau shows results consistent with the previous joint inversion with P constraints, indicating the proposed method is reliable and robust.
我们提出了一种基于多标签卷积神经网络和递归神经网络的深度学习（DL）方法，以从射线波色散和接收函数的联合反演中推导出 VS 模型。我们使用基于样条线的方法生成合成模型，而不是直接使用现有模型来建立训练数据集，从而提高了该方法的泛化能力。与通常设定固定 VP/VS 比值的传统方法不同，我们的方法充分利用了 DL 强大的数据挖掘能力，在假设不同 VP/VS 比值的情况下反演 VS 模型。我们专门设计了一个损失函数，该函数关注模型空间的关键特征，例如莫霍的形状和深度。合成测试表明，所提出的方法准确、快速。在青藏高原东南边缘的应用显示，其结果与之前的 P 约束联合反演结果一致，表明所提出的方法是可靠和稳健的。
{"title":"Joint Inversion of Surface-Wave Dispersions and Receiver Functions Based on Deep Learning","authors":"Feiyi Wang, Xiaodong Song, Jiangtao Li","doi":"10.1785/0220240040","DOIUrl":"https://doi.org/10.1785/0220240040","url":null,"abstract":"\u0000 We proposed a deep learning (DL) method to derive VS models from joint inversion of Rayleigh-wave dispersions and receiver functions, which is based on multilabel convolutional neural network and recurrent neural network. We used a spline-based approach to generate synthetic models instead of directly using existing models to build the training data set, which improves the generalization of the method. Unlike the traditional methods, which usually set a fixed VP/VS ratio, our method makes full use of the powerful data mining ability of DL to invert the VS models assuming different VP/VS ratios. A loss function is specially designed that focuses on key features of the model space, for example, the shape and depth of Moho. Synthetic tests demonstrate that the proposed method is accurate and fast. Application to the southeast margin of the Tibetan Plateau shows results consistent with the previous joint inversion with P constraints, indicating the proposed method is reliable and robust.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"88 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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