eprintid: 2419 rev_number: 8 eprint_status: archive userid: 6 dir: disk0/00/00/24/19 datestamp: 2023-09-22 11:03:05 lastmod: 2023-10-27 10:14:10 status_changed: 2023-09-22 11:03:05 type: article metadata_visibility: show creators_name: Ross, Zachary E. creators_name: Meier, Men‐Andrin creators_name: Hauksson, Egill creators_name: Heaton, Thomas H. corp_creators: Seismological Laboratory, California Institute of Technology, Pasadena corp_creators: Seismological Laboratory, California Institute of Technology, Pasadena corp_creators: Seismological Laboratory, California Institute of Technology, Pasadena corp_creators: Seismological Laboratory, California Institute of Technology, Pasadena title: Generalized Seismic Phase Detection with Deep Learning full_text_status: none abstract: To optimally monitor earthquake‐generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (i.e., template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large‐magnitude events. Here, we show that with deep learning, we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand‐labeled data archives of the Southern California Seismic Network to detect seismic body‐wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases even when masked by high background noise and when the ConvNet is applied to new data that are not represented in the training set (in particular, very large‐magnitude events). This generalized phase detection framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research. date: 2018 date_type: published publication: Bulletin of the Seismological Society of America volume: 108 number: 5A pagerange: 2894-2901 id_number: doi:10.1785/0120180080 issn: 0037-1106 official_url: https://doi.org/10.1785/0120180080 access_IS-EPOS: limited software_references: P_and_S_Waves_Detection_Tool owner: Publisher citation: Ross, Zachary E. and Meier, Men‐Andrin and Hauksson, Egill and Heaton, Thomas H. (2018) Generalized Seismic Phase Detection with Deep Learning. Bulletin of the Seismological Society of America, 108 (5A). pp. 2894-2901. DOI: https://doi.org/10.1785/0120180080