Hao Lv, Xiangfang Zeng, Gongbo Zhang, Zhenghong Song
Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber-optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends on heavy computational cost and diverse template events. We developed a program named HD-TMA (high-efficiency DAS template matching algorithm), which accelerates computation by 40 times on the central processing unit platform and 2 times on the graphic processing unit platform. For linear DAS array data, we introduced a fast arrival-picking algorithm based on the Hough transform to pick the time window of template waveform. The HD-TMA was successfully applied to the 2022 Ms 6.9 Menyuan earthquake aftershock sequence recorded by a DAS array, and the DAS data result was compared with a collocated short-period seismometer data’s result. Two optimization strategies were discussed based on this data set. (1) Using signal-to-noise ratio in choosing the location and aperture of the subarray and the time window of the template waveform. (2) Considering the decrease in template events’ marginal utility, we proposed applying a neural network to build a template event library, followed by the HD-TMA scanning. Such strategies can effectively reduce computational cost and improve detection capability.
分布式声学传感（DAS）技术与现有的电信光缆相结合，在地震监测方面显示出巨大的潜力。模板匹配算法（TMA）显示出良好的检测能力，但依赖于沉重的计算成本和多样化的模板事件。我们开发了一种名为 HD-TMA（高效 DAS 模板匹配算法）的程序，在中央处理器平台上可将计算速度提高 40 倍，在图形处理器平台上提高 2 倍。对于线性 DAS 阵列数据，我们引入了基于 Hough 变换的快速到达选取算法，以选取模板波形的时间窗口。将 HD-TMA 成功应用于 DAS 阵列记录的 2022 年门源 6.9 级地震余震序列，并将 DAS 数据结果与同轴短周期地震仪数据结果进行了比较。基于该数据集，讨论了两种优化策略。(1) 利用信噪比选择子阵列的位置和孔径以及模板波形的时间窗。(2) 考虑到模板事件的边际效用下降，我们建议应用神经网络建立模板事件库，然后进行 HD-TMA 扫描。这种策略可以有效降低计算成本，提高检测能力。
{"title":"HD-TMA: A New Fast Template Matching Algorithm Implementation for Linear DAS Array Data and Its Optimization Strategies","authors":"Hao Lv, Xiangfang Zeng, Gongbo Zhang, Zhenghong Song","doi":"10.1785/0220240019","DOIUrl":"https://doi.org/10.1785/0220240019","url":null,"abstract":"\u0000 Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber-optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends on heavy computational cost and diverse template events. We developed a program named HD-TMA (high-efficiency DAS template matching algorithm), which accelerates computation by 40 times on the central processing unit platform and 2 times on the graphic processing unit platform. For linear DAS array data, we introduced a fast arrival-picking algorithm based on the Hough transform to pick the time window of template waveform. The HD-TMA was successfully applied to the 2022 Ms 6.9 Menyuan earthquake aftershock sequence recorded by a DAS array, and the DAS data result was compared with a collocated short-period seismometer data’s result. Two optimization strategies were discussed based on this data set. (1) Using signal-to-noise ratio in choosing the location and aperture of the subarray and the time window of the template waveform. (2) Considering the decrease in template events’ marginal utility, we proposed applying a neural network to build a template event library, followed by the HD-TMA scanning. Such strategies can effectively reduce computational cost and improve detection capability.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"6 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748093","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}
This article introduces the first version of SSA2py (v.1.0)—an open-source package designed to implement the source-scanning algorithm (SSA). SSA2py is a Python-based, high-performance-oriented package that incorporates the SSA method, which has been effectively applied to numerous earthquakes for imaging the spatiotemporal behavior of the seismic source. The software supports a wide range of data and metadata resources. These include the International Federation of Digital Seismograph Networks Web Services, the SeedLink protocol, and others, ensuring optimal access to waveforms and station metadata. Furthermore, the code may evaluate the quality of accessible waveforms using signal analysis methods, allowing for the most appropriate data selection. The SSA method has been computationally optimized using multiprocessing techniques for efficient central processing unit and graphic processing units executions, enabling considerably accelerated computational processes even for large-scale grid searches. The program is also designed to provide statistical and methodological uncertainties for the executed cases through jackknife, bootstrap, and backprojection array response function tests. After appropriate tuning by the user, SSA2py can be used for detailed earthquake source studies that backprojection technique typically serves as a complementary output to the source inversion result or as a near-real-time tool for successful and quick identification of the style and complexity of the earthquake rupture. With a wide and flexible configuration, the user has complete control over all calculating aspects of SSA2py. This article provides a detailed description of the structure and capabilities of this new package, and its reliability is demonstrated through targeted applications to the 2004 Mw 6.0 Parkfield and 2019 Mw 7.1 Ridgecrest earthquakes. Furthermore, the computational efficiency of SSA2py is validated through rigorous performance tests.
{"title":"SSA2py: A High-Performance Python Implementation of the Source-Scanning Algorithm for Spatiotemporal Seismic Source Imaging","authors":"I. Fountoulakis, C. Evangelidis","doi":"10.1785/0220230335","DOIUrl":"https://doi.org/10.1785/0220230335","url":null,"abstract":"\u0000 This article introduces the first version of SSA2py (v.1.0)—an open-source package designed to implement the source-scanning algorithm (SSA). SSA2py is a Python-based, high-performance-oriented package that incorporates the SSA method, which has been effectively applied to numerous earthquakes for imaging the spatiotemporal behavior of the seismic source. The software supports a wide range of data and metadata resources. These include the International Federation of Digital Seismograph Networks Web Services, the SeedLink protocol, and others, ensuring optimal access to waveforms and station metadata. Furthermore, the code may evaluate the quality of accessible waveforms using signal analysis methods, allowing for the most appropriate data selection. The SSA method has been computationally optimized using multiprocessing techniques for efficient central processing unit and graphic processing units executions, enabling considerably accelerated computational processes even for large-scale grid searches. The program is also designed to provide statistical and methodological uncertainties for the executed cases through jackknife, bootstrap, and backprojection array response function tests. After appropriate tuning by the user, SSA2py can be used for detailed earthquake source studies that backprojection technique typically serves as a complementary output to the source inversion result or as a near-real-time tool for successful and quick identification of the style and complexity of the earthquake rupture. With a wide and flexible configuration, the user has complete control over all calculating aspects of SSA2py. This article provides a detailed description of the structure and capabilities of this new package, and its reliability is demonstrated through targeted applications to the 2004 Mw 6.0 Parkfield and 2019 Mw 7.1 Ridgecrest earthquakes. Furthermore, the computational efficiency of SSA2py is validated through rigorous performance tests.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"52 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367714","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}
Because of the harsh deployment environment of the fibers, distributed acoustic sensing (DAS) data usually suffer from the low signal-to-noise ratio issue. Many methods, whether simple but efficient or sophisticated but effective, have been proposed for dealing with noise and recovering signals from DAS data. However, no matter what methods we apply, we will inevitably damage the signals, more or less, resulting in coherent signal leakage in the removed noise. Here, we present a method (SigRecover) for minimizing signal leakage by recovering useful signals from removed noise and its open-source package (see Data and Resources). We apply a robust dictionary learning framework to retrieve the coherent signals from removed noise that can be captured by a pretrained library of atoms (features). The atoms are obtained by a fast dictionary-learning approach from the initially denoised data. The proposed framework is a self-learning methodology, which does not require additional training datasets and thus is conveniently applicable to any input data. We use three well-processed examples from the literature to demonstrate the generic performance of the proposed method. The idea behind this article is inspired by similar methods widely used in the exploration seismology community for retrieving signal leakage and is promising not only for DAS data processing, but also for all other multichannel seismological datasets.
由于光纤部署环境恶劣，分布式声学传感（DAS）数据通常存在信噪比低的问题。人们提出了许多方法来处理噪声并从 DAS 数据中恢复信号，这些方法有的简单而高效，有的复杂而有效。然而，无论我们采用哪种方法，都不可避免地会或多或少地损坏信号，从而导致相干信号在去除的噪声中泄漏。在此，我们介绍一种通过从去除的噪声中恢复有用信号来最大限度减少信号泄漏的方法（SigRecover）及其开源软件包（见数据和资源）。我们采用稳健字典学习框架，从去除的噪声中检索相干信号，这些信号可通过预训练的原子（特征）库捕获。原子通过快速字典学习方法从初始去噪数据中获取。所提出的框架是一种自学习方法，不需要额外的训练数据集，因此可方便地适用于任何输入数据。我们使用文献中三个处理良好的示例来展示所提方法的通用性能。本文背后的想法受到了勘探地震学界广泛用于检索信号泄漏的类似方法的启发，不仅在 DAS 数据处理方面大有可为，而且在所有其他多道地震学数据集方面也大有可为。
{"title":"SigRecover: Recovering Signal from Noise in Distributed Acoustic Sensing Data Processing","authors":"Yangkang Chen","doi":"10.1785/0220230370","DOIUrl":"https://doi.org/10.1785/0220230370","url":null,"abstract":"\u0000 Because of the harsh deployment environment of the fibers, distributed acoustic sensing (DAS) data usually suffer from the low signal-to-noise ratio issue. Many methods, whether simple but efficient or sophisticated but effective, have been proposed for dealing with noise and recovering signals from DAS data. However, no matter what methods we apply, we will inevitably damage the signals, more or less, resulting in coherent signal leakage in the removed noise. Here, we present a method (SigRecover) for minimizing signal leakage by recovering useful signals from removed noise and its open-source package (see Data and Resources). We apply a robust dictionary learning framework to retrieve the coherent signals from removed noise that can be captured by a pretrained library of atoms (features). The atoms are obtained by a fast dictionary-learning approach from the initially denoised data. The proposed framework is a self-learning methodology, which does not require additional training datasets and thus is conveniently applicable to any input data. We use three well-processed examples from the literature to demonstrate the generic performance of the proposed method. The idea behind this article is inspired by similar methods widely used in the exploration seismology community for retrieving signal leakage and is promising not only for DAS data processing, but also for all other multichannel seismological datasets.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"15 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140374692","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}
M. Vassallo, G. Cultrera, Alessandro Esposito, A. Mercuri, A. Bobbio, G. Di Giulio, G. Milana, P. Bordoni, M. Ciaccio, F. Cara
This study presents data and preliminary analysis from a temporary seismic network (SPQR), which was deployed in the urban area of Rome (Italy) for three months in early 2021. The network was designed to investigate the city’s subsurface while evaluating the feasibility of a permanent urban seismic network, and consisted of 24 seismic stations. Despite significant anthropogenic noise, the SPQR network well recorded earthquake signals, revealing clear spatial variability referable to site effects. In addition, the network’s continuous recordings allowed the use of seismic noise and earthquake signals to derive spectral ratios at sites located in different geological and lithological settings. During the experiment, there were periods of activity restrictions imposed on citizens to limit the spread of COVID-19. Although the observed power spectral density levels at stations may not show visible noise reductions, they do cause variations in calculated spectral ratios across measurement sites. Finally, a statistical noise analysis was conducted on continuous seismic station data to evaluate their performance in terms of detection threshold for earthquakes. The results indicate that all network stations can effectively record earthquakes with a good signal-to-noise ratio (≥5 for P and S phases) in the magnitude range of 1.9–3.3 at distances of 10 km and 80 km, respectively. In addition, the network has the potential to record earthquakes of magnitude 4 up to 200 km, covering areas in Central Italy that are far from the city. This analysis shows that it is possible to establish urban observatories in noisy cities such as Rome, where hazard studies are of particular importance due to the high vulnerability (inherent fragility of its monumental heritage) and exposure.
{"title":"Temporary Seismic Network in the Metropolitan Area of Rome (Italy): New Insight on an Urban Seismology Experiment","authors":"M. Vassallo, G. Cultrera, Alessandro Esposito, A. Mercuri, A. Bobbio, G. Di Giulio, G. Milana, P. Bordoni, M. Ciaccio, F. Cara","doi":"10.1785/0220230290","DOIUrl":"https://doi.org/10.1785/0220230290","url":null,"abstract":"\u0000 This study presents data and preliminary analysis from a temporary seismic network (SPQR), which was deployed in the urban area of Rome (Italy) for three months in early 2021. The network was designed to investigate the city’s subsurface while evaluating the feasibility of a permanent urban seismic network, and consisted of 24 seismic stations. Despite significant anthropogenic noise, the SPQR network well recorded earthquake signals, revealing clear spatial variability referable to site effects. In addition, the network’s continuous recordings allowed the use of seismic noise and earthquake signals to derive spectral ratios at sites located in different geological and lithological settings. During the experiment, there were periods of activity restrictions imposed on citizens to limit the spread of COVID-19. Although the observed power spectral density levels at stations may not show visible noise reductions, they do cause variations in calculated spectral ratios across measurement sites. Finally, a statistical noise analysis was conducted on continuous seismic station data to evaluate their performance in terms of detection threshold for earthquakes. The results indicate that all network stations can effectively record earthquakes with a good signal-to-noise ratio (≥5 for P and S phases) in the magnitude range of 1.9–3.3 at distances of 10 km and 80 km, respectively. In addition, the network has the potential to record earthquakes of magnitude 4 up to 200 km, covering areas in Central Italy that are far from the city. This analysis shows that it is possible to establish urban observatories in noisy cities such as Rome, where hazard studies are of particular importance due to the high vulnerability (inherent fragility of its monumental heritage) and exposure.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376179","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}
R. Anthony, Nicolas Leroy, R. Mellors, A. Ringler, Joachim Saul, Martin Vallée, D. Wilson
{"title":"Preface to Focus Section on New Frontiers and Advances in Global Seismology","authors":"R. Anthony, Nicolas Leroy, R. Mellors, A. Ringler, Joachim Saul, Martin Vallée, D. Wilson","doi":"10.1785/0220240092","DOIUrl":"https://doi.org/10.1785/0220240092","url":null,"abstract":"","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"76 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376111","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}
{"title":"Strengthening the Development and Use of “Deep” Seismic Event Catalogs","authors":"Yongsoo Park, G. C. Beroza, W. Ellsworth","doi":"10.1785/0220240044","DOIUrl":"https://doi.org/10.1785/0220240044","url":null,"abstract":"","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385302","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}
Full-waveform tomography (FWT) is increasingly recognized as a pivotal technique for delineating high-resolution subsurface properties. Despite its significant potential, practical applications of FWT encounter persistent challenges, particularly in dealing with local minima and cycle-skipping problems. These difficulties often arise and are intensified by the least-squares (L2) norm’s intrinsic insensitivity to phase mismatches. To address these challenges, we have redefined the traditional L2 norm misfit function by incorporating a time shift within the synthetic waveform. This shift is determined by the temporal discrepancies between the observed and synthetic waveforms, identified through a cross-correlation technique. This approach, termed phase-sensitive FWT, integrates phase differences into the new misfit function, thus significantly mitigating the cycle-skipping problem. Numerical experiments demonstrate that PSFWT reduces dependence on the initial model and achieves more accurate inversion results compared with the traditional L2 norm method, highlighting its potential for enhancing the precision and reliability of seismic imaging.
{"title":"Novel Phase-Sensitive Full-Waveform Tomography for Seismic Imaging","authors":"Xingpeng Dong, Dinghui Yang","doi":"10.1785/0220230442","DOIUrl":"https://doi.org/10.1785/0220230442","url":null,"abstract":"\u0000 Full-waveform tomography (FWT) is increasingly recognized as a pivotal technique for delineating high-resolution subsurface properties. Despite its significant potential, practical applications of FWT encounter persistent challenges, particularly in dealing with local minima and cycle-skipping problems. These difficulties often arise and are intensified by the least-squares (L2) norm’s intrinsic insensitivity to phase mismatches. To address these challenges, we have redefined the traditional L2 norm misfit function by incorporating a time shift within the synthetic waveform. This shift is determined by the temporal discrepancies between the observed and synthetic waveforms, identified through a cross-correlation technique. This approach, termed phase-sensitive FWT, integrates phase differences into the new misfit function, thus significantly mitigating the cycle-skipping problem. Numerical experiments demonstrate that PSFWT reduces dependence on the initial model and achieves more accurate inversion results compared with the traditional L2 norm method, highlighting its potential for enhancing the precision and reliability of seismic imaging.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385181","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}
Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.
区域地震风险或损失评估通常需要使用多种烈度测量方法对空间分布的地动进行模拟。因此，在本研究中，利用空间相关性进行地动模型估算。此前，许多研究人员利用地动记录分析了空间相关性并开发了经验模型。在本研究中，利用 2019 年至 2023 年期间发生在加利福尼亚州的地面运动，使用不同频谱周期的峰值地面加速度和伪谱加速度的半变量图分析空间相关性。根据分析结果，传统的相关模型有两个方面需要修改：（1）经验指数模型不能合理地反映距离小于 10 km 的目标空间相关性；（2）空间相关地动强度的变化不能在距离小于 1 km 时得到描述。由于这些局限性，我们修改了半变量图的拟合模型，以更好地描述空间相关性。在该模型中，另一个名为 "一致性 "的函数取代了空间相关性，以描述傅里叶相位的变化，而不是距离小于 1 千米范围内的强度变化。这项研究表明，在实际的地震风险或损失评估问题中，可以通过结合相干性和相关性函数来分析任何区域的空间变化。
{"title":"A New Spatial Variation Model for Ground-Motion Intensities Combined with Correlation and Coherency","authors":"Pan Wen, Baofeng Zhou, Guoliang Shao","doi":"10.1785/0220230249","DOIUrl":"https://doi.org/10.1785/0220230249","url":null,"abstract":"\u0000 Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382160","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}
R. Gök, William R. Walter, J. Barno, Carlos Downie, R. Mellors, K. Mayeda, Jorge Roman-Nieves, Dennise Templeton, Jonathan Ajo-Franklin
A challenge in fully using distributed acoustic sensing (DAS) data collected from fiber-optic sensors is correcting the signals to provide quantitative true ground motion. Such corrections require considering coupling and instrument response issues. In this study, we show via comparison with geophone and broadband seismometer data that we can use coda envelope calibration techniques to obtain absolute moment magnitudes and source spectra from DAS data. Here, we use DAS and nodal geophones deployed as part of a geothermal monitoring experiment at Brady Hot Springs, Nevada, and on a 20 km long dark fiber of the ESnet’s Dark Fiber Testbed–a U.S. Department of Energy user facility, in Sacramento, California. Several DAS line segments with colocated geophone stations were used to compare the amplitude variation using narrowband S-wave coda envelopes. The DAS coda envelope decay at each point showed remarkable similarity with coda envelopes from different events in each narrow frequency range examined. The coda envelopes are used to determine Mw magnitudes and source spectra from regional stations without any major scatter. Because coda waves arrive from a range of directions, the azimuthal sensitivity of DAS is somewhat ameliorated. We show that the openly available seismic coda calibration software toolkit can be used for straightforward and faster processing of large DAS datasets for source parameters and subsurface imaging.
要充分利用光纤传感器收集的分布式声学传感（DAS）数据，面临的一个挑战是校正信号，以提供定量的真实地面运动。这种校正需要考虑耦合和仪器响应问题。在本研究中，我们通过与地震检波器和宽带地震仪数据的比较表明，我们可以使用尾音包络校正技术从 DAS 数据中获得绝对矩幅和震源频谱。在这里，我们使用了部署在内华达州布雷迪温泉地热监测实验中的 DAS 和节点检波器，以及位于加利福尼亚州萨克拉门托的 ESnet 的暗光纤试验台（美国能源部用户设施）上的 20 千米长的暗光纤。使用窄带 S 波尾音包络比较了几个带有同地检波器站的 DAS 线路段的振幅变化。每个点的 DAS 尾音包络衰减与所研究的每个窄频率范围内不同事件的尾音包络具有显著的相似性。尾波包络用于确定地区台站的 Mw 震级和震源频谱，没有出现任何大的偏差。由于尾波来自不同方向，DAS 的方位敏感性在一定程度上得到了改善。我们的研究表明，公开的地震尾波校准软件工具包可用于直接、快速地处理大型 DAS 数据集，以获得震源参数和地下成像。
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