S051
Extracting Information from Geophysical Signals with Machine Learning I

Tuesday, 15 December 2020: 04:00-05:00
Virtual
Primary Convener:  Ting Chen, Los Alamos National Laboratory, Los Alamos, NM, United States
Conveners:  Laura J Pyrak-Nolte, Purdue University, Department of Physics and Astronomy; Department of Earth, Atomospheric and Planetary Sciences; Lyles School of Civil Engineering, West Lafayette, IN, United States, Paul A Johnson, Los Alamos National Laboratory, Earth and Environmental Sciences: Geophysics (EES-17), Los Alamos, NM, United States and Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States
Primary Liaison:  Ting Chen, Los Alamos National Laboratory, Los Alamos, NM, United States
Chairs:  Paul A Johnson, Los Alamos National Laboratory, Earth and Environmental Sciences: Geophysics (EES-17), Los Alamos, NM, United States and Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States
OSPA Liaison:  Bertrand Rouet-Leduc, Los Alamos National Laboratory, Los Alamos, NM, United States
04:00
Introductory Remarks
04:02
S051-01
Interpretation and evaluation of machine learning-based earthquake monitoring (740610)
Karianne J. Bergen, Brown University, Department of Earth, Environmental and Planetary Sciences, Providence, RI, United States; Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, United States
04:06
S051-02
The National Earthquake Information Center’s Next Steps in Leveraging Machine Learning for Global Earthquake Detection (739331)
William L Yeck, John Patton, Harley Benz and Paul S Earle, USGS National Earthquake Information Center Golden, Golden, CO, United States
04:10
S051-03
A little data goes a long way: automating phase arrival picking at Nabro volcano, Eritrea, using transfer learning and a limited seismic catalog (733137)
Sacha Lapins1, Berhe Goitom2, Michael Kendall3, Maximilian J Werner1, Katharine V Cashman2 and James O. S. Hammond4, (1)University of Bristol, School of Earth Sciences, Bristol, BS8, United Kingdom, (2)University of Bristol, School of Earth Sciences, Bristol, United Kingdom, (3)University of Oxford, Department of Earth Sciences, Oxford, United Kingdom, (4)Birkbeck, University of London, London, United Kingdom
04:14
S051-04
A U-Net for Weak Earthquake Detection (730856)
Hao Mai and Pascal Audet, University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada
04:18
S051-05
Extending near-fault seismic catalog from single-station waveforms using deep learning (683058)
Josipa Majstorovic, Sophie Giffard-Roisin and Piero Poli, Université Grenoble Alpes, ISTerre, Grenoble, France
04:22
S051-06
An unsupervised automatic classification for continuous seismic records: introducing an anomaly detection algorithm to solve the imbalanced data problem (719775)
Yuki Kodera, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan and Shin'ichi Sakai, Univ Tokyo, Bunkyo-Ku Tokyo, Japan
04:26
S051-07
Earthquake Source Characterization with Graphical Deep Learning (717826)
Xitong Zhang1, Miao Zhang2 and Youzuo Lin1, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada
04:30
S051-08
Real-time earthquake location via convolutional neural network and data fusion (766030)
Chao Guo, Pennsylvania State University Main Campus, Department of Geosciences, University Park, PA, United States; University of Science and Technology Beijing, School of Civil and Resource Engineering, Beijing, China and Tieyuan Zhu, Pennsylvania State University, Department of Geosciences, University Park, PA, United States
04:34
Discussion
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