S33B:
Next-Generation Seismic Source Studies I


Submit an Abstract to this Session


Session ID#: 58841

Session Description:
Seismic source studies are fundamental to seismology. Recently, correlation-based detection and location methods have dramatically improved seismic monitoring capability. For other aspects of source studies, machine learning shows promise in tasks such as phase picking and association, event detection, event location, and event classification. Correlation-based double-difference location methods can vastly improve the precision of earthquake location. Polarity, phase amplitude, S/P amplitude ratio, and full waveform techniques have led to great improvements in focal mechanism determination and reliability. There have also been important improvements in source models spanning point source to full finite-fault models. Despite this progress, opportunities remain for improvements in seismic monitoring ability, including seismic monitoring in real time, and in streamlining the components of source characterization into a seamless workflow. This session aims to highlight new methods and applications in detection, location and characterization of seismic events in different aspects of seismological studies.
Primary Convener:  Miao Zhang, Stanford University, Stanford, CA, United States
Conveners:  David R Shelly, US Geological Survey, Menlo Park, CA, United States, Francesco Grigoli, ETH Zurich, Swiss Seismological Service, Zurich, Switzerland and Kazutoshi Imanishi, Geological Survey of Japan, AIST, Tsukuba, Japan
Primary Liaison:  Miao Zhang, Stanford University, Stanford, CA, United States
Chairs:  Miao Zhang, Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada, David R Shelly, U.S. Geological Survey, Geologic Hazards Science Center, Golden, United States, Francesco Grigoli, University of Potsdam, Potsdam, Germany and Kazutoshi Imanishi, Geological Survey of Japan, AIST, Tsukuba, Japan
OSPA Liaison:  Miao Zhang, Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada
Index Terms:

7203 Body waves [SEISMOLOGY]
7215 Earthquake source observations [SEISMOLOGY]
7219 Seismic monitoring and test-ban treaty verification [SEISMOLOGY]
7230 Seismicity and tectonics [SEISMOLOGY]

Abstracts Submitted to this Session:

Generalizing Similarity-Based Earthquake Detection Using Machine Learning (Invited) (418710)
Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States, Weiqiang Zhu, University of California, Berkeley, Department of Earth and Planetary Science, Berkeley, United States and S. Mostafa Mousavi, Stanford University, Department of Geophysics, Stanford, United States
Subject-oriented finite fault inversion: a simple way to explore the uncertainty of the inverted source models (Invited) (421971)
Chen Ji1, Mareike Adams1, Tung-Cheng Ho2 and Jinglai Hao3, (1)University of California Santa Barbara, Santa Barbara, CA, United States, (2)Earthquake Research Institute, University of Tokyo, Tokyo, Japan, (3)Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Kinematic slip imaging of the Mw 3.3 earthquake in the St. Gallen 2013 geothermal reservoir, Switzerland, using isochrone back projection. (398165)
Claudio Satriano, Université Paris Cité, Institut de physique du globe de Paris, Sismologie, Paris, France, Eszter Király, ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, Pascal Bernard, Institut de Physique du Globe de Paris (IPGP), Université Paris Cité, Paris, France and Stefan Wiemer, ETH Zurich, Swiss Seismological Service, Zurich, Switzerland
The Similarity Matrix Profile, an efficient method for detecting seismic events in very long time series (457500)
Gareth Funning1, Nader Shakibay Senobari1, Zachary Zimmerman2, Yan Zhu2 and Eamonn Keogh2, (1)University of California Riverside, Riverside, CA, United States, (2)University of California Riverside, Department of Computer Science and Engineering, Riverside, United States
Probabilistic source mechanism estimation based on body-wave waveforms through shift and stack algorithm. (408230)
Frédérick Massin, ETH Swiss Federal Institute of Technology Zurich, Swiss Seismological Service (SED), Zurich, Switzerland and Alison E Malcolm, Memorial University of Newfoundland, Earth Science, St John's, NL, Canada
Time-varying moment tensor analysis (442071)
Michael Cleveland, Los Alamos National Laboratory, Los Alamos, NM, United States
New Observations of Prompt Elastogravity Signals Confirm their Sensitivities to Key Source Parameters of Large Earthquakes (381447)
Martin Vallée, Université de Paris, Institut de Physique du Globe de Paris, CNRS, Paris, France and Kévin Juhel, Institut de Physique du Globe de Paris, Paris, France

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