Performance of Deep Learning Pickers in Routine Network Processing Applications
14 pages, 9 figures, 3 tables
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Seismological Society of America
2022-07
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| Online Access: | http://hdl.handle.net/10261/283944 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003359 |
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dig-icm-es-10261-2839442022-11-29T02:12:32Z Performance of Deep Learning Pickers in Routine Network Processing Applications García Navarro, José Enrique Fernández-Prieto, Luis Villaseñor, Antonio Sanz, Verónica Ammirati, Jean-Baptiste Díaz Suárez, Eduardo A. García García, Carmen European Commission Generalitat Valenciana Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) 14 pages, 9 figures, 3 tables Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV). Antonio Villaseñor acknowledges funding from Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 grants CGL2017-88864-R, PID2020-114682RB-C31, and the Severo Ochoa Center of Excellence accreditation CEX2019-000928-S to ICM-CSIC Peer reviewed 2022-11-28T11:44:57Z 2022-11-28T11:44:57Z 2022-07 artículo Seismological Research Letters 93(5): 2529-2542 (2022) 0895-0695 CEX2019-000928-S http://hdl.handle.net/10261/283944 10.1785/0220210323 1938-2057 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003359 en #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2017-88864-R/ES/SISMICIDAD ASOCIADA A LA EXTRACCION Y ALMACENAMIENTO DE HIDROCARBUROS EN ESPAÑA/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114682RB-C31/ES/ESTRUCTURA DE LA CORTEZA Y MANTO EN LAS ISLAS CANARIAS INTEGRANDO OBSERVACIONES EN EL FONDO MARINO/ Preprint https://doi.org/10.1785/0220210323 Sí open Seismological Society of America |
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| description |
14 pages, 9 figures, 3 tables |
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European Commission |
| author_facet |
European Commission García Navarro, José Enrique Fernández-Prieto, Luis Villaseñor, Antonio Sanz, Verónica Ammirati, Jean-Baptiste Díaz Suárez, Eduardo A. García García, Carmen |
| format |
artículo |
| author |
García Navarro, José Enrique Fernández-Prieto, Luis Villaseñor, Antonio Sanz, Verónica Ammirati, Jean-Baptiste Díaz Suárez, Eduardo A. García García, Carmen |
| spellingShingle |
García Navarro, José Enrique Fernández-Prieto, Luis Villaseñor, Antonio Sanz, Verónica Ammirati, Jean-Baptiste Díaz Suárez, Eduardo A. García García, Carmen Performance of Deep Learning Pickers in Routine Network Processing Applications |
| author_sort |
García Navarro, José Enrique |
| title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
| title_short |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
| title_full |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
| title_fullStr |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
| title_full_unstemmed |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
| title_sort |
performance of deep learning pickers in routine network processing applications |
| publisher |
Seismological Society of America |
| publishDate |
2022-07 |
| url |
http://hdl.handle.net/10261/283944 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003359 |
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