Decoding Geophysical Signatures with Machine Learning
Decoding Geophysical Signatures with Machine Learning
Session ID#: 121112
Session Description:
Data-driven machine learning (ML) has led to rapid advances in solving various geophysical problems with some remarkable results. By leveraging large quantities of data with advanced ML algorithms, new and existing geophysical signatures have been decoded that aid earthquake detection, seismic phase picking, source characterization, fault slip physics, ground motion displacement, signal denoising, mineral and pore identification, fluid flow characterization and subsurface imaging. As geoscientists take advantage of the spectrum of capabilities and applications that ML has to offer, we will continue to see new phenomena revealed by data. This session aims to engage diverse geophysical communities to share their applications of ML to geophysical data and to discuss improved geophysical understanding through such applications. We welcome contributions that employ ML to elucidate geophysical signatures in all aspects of solid earth geophysics.
Index Terms:
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
1910 Data assimilation, integration and fusion [INFORMATICS]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]
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
See more of: Seismology