T. Godoladze, R. Gök, Tuna Onur, I. Gunia, Manana Dzmanashvili, Giorgi Boichenko, A. Buzaladze, István Bondár, Lana Ratiani, T. Rostomashvili, J. Nábělek, Z. Javakhishvili, G. Yetirmishli, E. Sandvol, F. T. Kadirioğlu, Andrea Chiang
Instrumental seismic monitoring has a long history in the Caucasus and started in 1899 when the first seismograph was installed in Tbilisi, Georgia. Much of the analog paper records from this time period are preserved in the Tbilisi archives because Georgia served as the regional data center. In the 1990s, due to the collapse of the Soviet Union and the political turmoil in the region, the analog networks and the communication between the newly formed national networks deteriorated. In Georgia, for the next 13 yr, the seismic network coverage was poor until the 2002 Tbilisi earthquake. Following this earthquake, the first permanent digital seismic station in Georgia was established in Tbilisi in 2003. The digital era progressively improved the ability to collect and archive data and today more than a hundred broadband seismic stations (including temporary arrays) are operating in the southern Caucasus. Until recently, the region lacked a coordinated effort to catalog all analog and digital era data collected by different countries into a single repository. As a result of collaboration between Lawrence Livermore National Laboratory, the Ilia State University, and the Republican Seismic Survey Center of Azerbaijan, a comprehensive earthquake catalog was compiled for the Caucasus and neighboring areas as part of a broader probabilistic seismic hazard assessment project. This project digitized Soviet-era paper bulletins, compiled a unified earthquake catalog from regional bulletins, developed 1D reference velocity model, and used it to relocate the events. The final catalog contains 16,963 events with magnitudes 3.7 and above, bringing together all the available data sets in the Caucasus region from 1900 to 2015, significantly improving locations, and generating the most complete earthquake catalog in the region, temporally and geographically.
{"title":"Compilation of a Comprehensive Earthquake Catalog and Relocations in the Caucasus Region","authors":"T. Godoladze, R. Gök, Tuna Onur, I. Gunia, Manana Dzmanashvili, Giorgi Boichenko, A. Buzaladze, István Bondár, Lana Ratiani, T. Rostomashvili, J. Nábělek, Z. Javakhishvili, G. Yetirmishli, E. Sandvol, F. T. Kadirioğlu, Andrea Chiang","doi":"10.1785/0220230206","DOIUrl":"https://doi.org/10.1785/0220230206","url":null,"abstract":"\u0000 Instrumental seismic monitoring has a long history in the Caucasus and started in 1899 when the first seismograph was installed in Tbilisi, Georgia. Much of the analog paper records from this time period are preserved in the Tbilisi archives because Georgia served as the regional data center. In the 1990s, due to the collapse of the Soviet Union and the political turmoil in the region, the analog networks and the communication between the newly formed national networks deteriorated. In Georgia, for the next 13 yr, the seismic network coverage was poor until the 2002 Tbilisi earthquake. Following this earthquake, the first permanent digital seismic station in Georgia was established in Tbilisi in 2003. The digital era progressively improved the ability to collect and archive data and today more than a hundred broadband seismic stations (including temporary arrays) are operating in the southern Caucasus. Until recently, the region lacked a coordinated effort to catalog all analog and digital era data collected by different countries into a single repository. As a result of collaboration between Lawrence Livermore National Laboratory, the Ilia State University, and the Republican Seismic Survey Center of Azerbaijan, a comprehensive earthquake catalog was compiled for the Caucasus and neighboring areas as part of a broader probabilistic seismic hazard assessment project. This project digitized Soviet-era paper bulletins, compiled a unified earthquake catalog from regional bulletins, developed 1D reference velocity model, and used it to relocate the events. The final catalog contains 16,963 events with magnitudes 3.7 and above, bringing together all the available data sets in the Caucasus region from 1900 to 2015, significantly improving locations, and generating the most complete earthquake catalog in the region, temporally and geographically.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"10 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451112","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}
Distributed acoustic sensing (DAS) data become important for seismic monitoring of subsurface structures in urban areas. Different from the previous studies that only focused on Rayleigh waves, we report successful observation and analysis of both Rayleigh and Love waves extracted from ambient-noise interferometry, using orthogonal segments of fiber-optic cables in San Jose, California. Theoretical angular responses of DAS ambient-noise cross correlation, together with numerical experiments, help identify DAS channel pairs expected to record stronger Love waves than Rayleigh waves. Based on these waveforms, we further obtain clear Rayleigh- and Love-wave dispersion maps, including both phase and group velocities, with various channel pair orientations. Finally, we perform a joint inversion of Rayleigh- and Love-wave dispersion curves to obtain depth-dependent subsurface velocity structures of the top 100 m. Our inversion result is consistent with the model from the previous study based on Rayleigh-wave dispersion and horizontal-to-vertical spectral ratio. In addition, the joint inversion of Love and Rayleigh is more robust than that of the independent inversion of either type of wave. Our new study demonstrates the potential of surface-wave analysis on fiber-optic cables with complex geometry, which can further advance the seismic monitoring of urban areas.
分布式声学传感（DAS）数据对于城市地区地下结构的地震监测非常重要。与以往只关注瑞利波的研究不同，我们报告了在加利福尼亚州圣何塞使用正交光缆段，成功观测和分析了从环境噪声干涉测量中提取的瑞利波和爱波。DAS 环境噪声交叉相关的理论角度响应与数值实验相结合，有助于确定预计会记录到比瑞利波更强的爱波的 DAS 信道对。在这些波形的基础上，我们进一步获得了清晰的瑞利波和爱波频散图，包括相位和群速度，以及不同的信道对方向。最后，我们对雷波和爱波频散曲线进行了联合反演，得到了顶部 100 米随深度变化的地下速度结构。此外，洛夫波和雷利波的联合反演比任何一种波的独立反演都更加稳健。我们的新研究展示了对具有复杂几何形状的光缆进行面波分析的潜力，这将进一步推动城市地区的地震监测工作。
{"title":"Exploiting the Potential of Urban DAS Grids: Ambient-Noise Subsurface Imaging Using Joint Rayleigh and Love Waves","authors":"Qing Ji, Bin Luo, B. Biondi","doi":"10.1785/0220230104","DOIUrl":"https://doi.org/10.1785/0220230104","url":null,"abstract":"\u0000 Distributed acoustic sensing (DAS) data become important for seismic monitoring of subsurface structures in urban areas. Different from the previous studies that only focused on Rayleigh waves, we report successful observation and analysis of both Rayleigh and Love waves extracted from ambient-noise interferometry, using orthogonal segments of fiber-optic cables in San Jose, California. Theoretical angular responses of DAS ambient-noise cross correlation, together with numerical experiments, help identify DAS channel pairs expected to record stronger Love waves than Rayleigh waves. Based on these waveforms, we further obtain clear Rayleigh- and Love-wave dispersion maps, including both phase and group velocities, with various channel pair orientations. Finally, we perform a joint inversion of Rayleigh- and Love-wave dispersion curves to obtain depth-dependent subsurface velocity structures of the top 100 m. Our inversion result is consistent with the model from the previous study based on Rayleigh-wave dispersion and horizontal-to-vertical spectral ratio. In addition, the joint inversion of Love and Rayleigh is more robust than that of the independent inversion of either type of wave. Our new study demonstrates the potential of surface-wave analysis on fiber-optic cables with complex geometry, which can further advance the seismic monitoring of urban areas.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"54 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451816","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}
Louisa Kinzel, Tanja Fromm, Vera Schlindwein, Peter Maass
Unsupervised machine learning methods are gaining attention in the seismological community as more and larger datasets of continuous waveforms are collected. Recently, contrastive learning for unsupervised feature learning has shown great success in the field of computer vision and other domains, and we aim to transfer these methods to the domain of seismology. Contrastive learning algorithms use data augmentation to implement an instance-level discrimination task: The feature representations of two augmented versions of the same data example are trained to be similar, when at the same time dissimilar to other data examples. In particular, we use the popular contrastive learning method SimCLR. We test data augmentation strategies varying amplitude and frequency of seismological signals, and apply contrastive learning methods to automatically learn features. We use a dataset containing various mostly cryogenic waveforms detected by an STA/LTA short-term average/long-term average algorithm on continuous waveform recordings from the geophysical observatory at Neumayer station, Antarctica. The quality of the features is evaluated on a hand-labeled dataset that includes icequakes, earthquakes, and spikes, and on a larger unlabeled dataset using a classical clustering method, k-means. Results show that the approach separates the different hand-labeled groups with an accuracy of up to 88% and separates meaningful groups within the unlabeled data. Thus, we provide an effective tool for the unsupervised exploration of large seismological datasets and the automated compilation of event catalogs.
{"title":"Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica","authors":"Louisa Kinzel, Tanja Fromm, Vera Schlindwein, Peter Maass","doi":"10.1785/0220230078","DOIUrl":"https://doi.org/10.1785/0220230078","url":null,"abstract":"\u0000 Unsupervised machine learning methods are gaining attention in the seismological community as more and larger datasets of continuous waveforms are collected. Recently, contrastive learning for unsupervised feature learning has shown great success in the field of computer vision and other domains, and we aim to transfer these methods to the domain of seismology. Contrastive learning algorithms use data augmentation to implement an instance-level discrimination task: The feature representations of two augmented versions of the same data example are trained to be similar, when at the same time dissimilar to other data examples. In particular, we use the popular contrastive learning method SimCLR. We test data augmentation strategies varying amplitude and frequency of seismological signals, and apply contrastive learning methods to automatically learn features. We use a dataset containing various mostly cryogenic waveforms detected by an STA/LTA short-term average/long-term average algorithm on continuous waveform recordings from the geophysical observatory at Neumayer station, Antarctica. The quality of the features is evaluated on a hand-labeled dataset that includes icequakes, earthquakes, and spikes, and on a larger unlabeled dataset using a classical clustering method, k-means. Results show that the approach separates the different hand-labeled groups with an accuracy of up to 88% and separates meaningful groups within the unlabeled data. Thus, we provide an effective tool for the unsupervised exploration of large seismological datasets and the automated compilation of event catalogs.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"128 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453461","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}
New technologies such as low-cost nodes and distributed acoustic sensing (DAS) are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, we are motivated to investigate the use of lossy compression on passive seismic array data. In particular, there is a need to not only just quantify the errors in the raw data but also the characteristics of the spectra of these errors and the extent to which these errors propagate into results such as detectability and arrival-time picks of microseismic events. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark fiber DAS experiment and a surface DAS array above a geothermal field. We find that depending on the level of compression needed and the importance of preserving large versus small seismic events, different compression schemes are preferable.
低成本节点和分布式声学传感（DAS）等新技术使连续收集宽带、高密度地震监测数据变得更加容易。为了缩短将数据从野外传输到计算中心的时间、降低存档要求并加快交互式数据分析和可视化，我们开始研究在被动地震阵列数据中使用有损压缩技术。特别是，我们不仅需要量化原始数据中的误差，还需要量化这些误差的频谱特征，以及这些误差在多大程度上会传播到微地震事件的可探测性和到达时间选取等结果中。我们比较了三种类型的有损压缩：稀疏阈值小波压缩、zfp 压缩和低秩奇异值分解压缩。我们将这些技术用于比较两个公开数据集的压缩方案：一个城市暗光纤 DAS 实验和一个地热田上方的地表 DAS 阵列。我们发现，根据所需的压缩水平以及保存大地震事件与小地震事件的重要性，不同的压缩方案更可取。
{"title":"Impact of Lossy Compression Errors on Passive Seismic Data Analyses","authors":"Abdul Hafiz S. Issah, Eileen R. Martin","doi":"10.1785/0220230314","DOIUrl":"https://doi.org/10.1785/0220230314","url":null,"abstract":"\u0000 New technologies such as low-cost nodes and distributed acoustic sensing (DAS) are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, we are motivated to investigate the use of lossy compression on passive seismic array data. In particular, there is a need to not only just quantify the errors in the raw data but also the characteristics of the spectra of these errors and the extent to which these errors propagate into results such as detectability and arrival-time picks of microseismic events. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark fiber DAS experiment and a surface DAS array above a geothermal field. We find that depending on the level of compression needed and the importance of preserving large versus small seismic events, different compression schemes are preferable.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"142 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452936","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}
Hendro Nugroho, B. Hejrani, S. Mousavi, Meghan S. Miller
A sequence of earthquakes occurred on Alor Island, Nusa Tenggara Timur, Indonesia, beginning in November 2015 with the mainshock (Mw 6.2) on 4 November 2015. We calculate the centroid moment tensor (CMT) solutions for nine of the earthquakes with Mw≥3.9, which occurred between November 2015 and March 2016 using records from a temporary array of 30 broadband instruments in eastern Indonesia and Timor Leste (YS network). Our CMT results reveal an interesting pattern of ruptures in this order: (a) three foreshocks of Mw 4–5.3 all with strike-slip mechanisms that occurred with a centroid depth of ∼13 km in the three days prior to the mainshock, (b) the mainshock on 4 November 2015, with Mw 6.2 that occurred with a deeper centroid (∼25 km) and a strike-slip mechanism similar to the foreshocks, (c) followed by five aftershocks with Mw>3.9 at depth ∼3–15km. We further determine the fault plane and rupture direction of the mainshock and the largest foreshock (Mw 5.3) by relocating the hypocenter and examining its geometrical location with respect to the centroid. We find that the fault plane strikes 97°±9° from north and that the fault ruptures westward. We propose that the rupture of this sequence of events initiated at depth ∼10 km, propagating westward and triggering the mainshock to rupture at a deeper depth (within lower crust) on a similar faulting system. The aftershocks migrate back to shallower depths and occur mainly at depth <10 km.
{"title":"Rupture Pattern of the 2015 Alor Earthquake Sequence, Indonesia","authors":"Hendro Nugroho, B. Hejrani, S. Mousavi, Meghan S. Miller","doi":"10.1785/0220230185","DOIUrl":"https://doi.org/10.1785/0220230185","url":null,"abstract":"\u0000 A sequence of earthquakes occurred on Alor Island, Nusa Tenggara Timur, Indonesia, beginning in November 2015 with the mainshock (Mw 6.2) on 4 November 2015. We calculate the centroid moment tensor (CMT) solutions for nine of the earthquakes with Mw≥3.9, which occurred between November 2015 and March 2016 using records from a temporary array of 30 broadband instruments in eastern Indonesia and Timor Leste (YS network). Our CMT results reveal an interesting pattern of ruptures in this order: (a) three foreshocks of Mw 4–5.3 all with strike-slip mechanisms that occurred with a centroid depth of ∼13 km in the three days prior to the mainshock, (b) the mainshock on 4 November 2015, with Mw 6.2 that occurred with a deeper centroid (∼25 km) and a strike-slip mechanism similar to the foreshocks, (c) followed by five aftershocks with Mw>3.9 at depth ∼3–15km. We further determine the fault plane and rupture direction of the mainshock and the largest foreshock (Mw 5.3) by relocating the hypocenter and examining its geometrical location with respect to the centroid. We find that the fault plane strikes 97°±9° from north and that the fault ruptures westward. We propose that the rupture of this sequence of events initiated at depth ∼10 km, propagating westward and triggering the mainshock to rupture at a deeper depth (within lower crust) on a similar faulting system. The aftershocks migrate back to shallower depths and occur mainly at depth <10 km.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452559","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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