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However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi\u2010continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.<\/jats:p>","DOI":"10.1029\/2019gl085870","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T05:20:12Z","timestamp":1579843212000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Probing Slow Earthquakes With Deep Learning"],"prefix":"10.1029","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2791-7949","authenticated-orcid":false,"given":"Bertrand","family":"Rouet\u2010Leduc","sequence":"first","affiliation":[{"name":"Los Alamos National Laboratory, Geophysics Group  Los Alamos NM USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3483-2257","authenticated-orcid":false,"given":"Claudia","family":"Hulbert","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, Geophysics Group  Los Alamos NM USA"},{"name":"Laboratoire de G\u00e9ologie, D\u00e9partement de G\u00e9osciences, \u00c9cole Normale Sup\u00e9rieure, PSL Research University, CNRS UMR  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian W.","family":"McBrearty","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, Geophysics Group  Los Alamos NM USA"},{"name":"Department of Geophysics Stanford University  Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0927-4003","authenticated-orcid":false,"given":"Paul A.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, Geophysics Group  Los Alamos NM USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13","published-online":{"date-parts":[[2020,2,24]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"704","volume-title":"Quantitative seismology","author":"Aki K.","year":"2002"},{"key":"e_1_2_7_3_1","first-page":"1803","article-title":"How to explain individual classification decisions","volume":"11","author":"Baehrens D.","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1017\/CCOL0521824737.025"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1785\/gssrl.81.3.530"},{"key":"e_1_2_7_6_1","unstructured":"Canadian National Seismograph Network(1989).Geological Survey of Canada.https:\/\/doi.org\/10.7914\/SN\/CN"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1029\/2011GL046866"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/2016GL069537"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aaw9386"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/2014JB011457"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-246X.2006.02865.x"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aao2929"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1029\/2011JB008840"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1880-5981-66-66"},{"key":"e_1_2_7_15_1","unstructured":"Ioffe S. &Szegedy C.(2015).Batch normalization: Accelerating deep network training by reducing internal covariate shift.arXiv preprint arXiv:1502.03167. 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