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
{"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}
{"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
{"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}
{"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
{"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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