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Machine Learning in Seismology

Date: 4/24/2019

Time: 8:30 AM to 3:30 PM

Room: Elliott Bay

Recent advances in computer science and data analytics have brought machine-learning (ML) techniques, including deep learning, to the forefront of seismological research. While ML methods continue to produce impressive successes in conventional artificial intelligence (AI) tasks, they also start to show powerful applicability in augmenting big data analysis in seismology by improving accuracy and efficiency compared to the traditional methods. Successful ML applications in seismology include seismic event detection, seismic signal classification, earthquake parameter estimation, signal denoising, ground motion prediction, subsurface tomography, aftershock pattern recognition and efficient visualization. However, challenges remain in terms of discovering new ML methods that can be applied to seismic and other geophysical data to learn about Earth’s subsurface structure and the underlying processes of Earth such as earthquakes. Furthermore, instead of considering ML models as “black boxes”, developing human-interpretable ML models and learning about their decision-making process also remain as grand challenges in ML field. The goal of this session is to highlight some of most recent ML results in our seismology community to motivate discussions of new ML research directions in seismology and beyond.
This session is jointly organized by the Seismological Society of Japan and SSA.

Conveners

Youzuo Lin, Los Alamos National Laboratory (ylin@lanl.gov)
Sepideh Karimi, Nanometrics Incorporated (sepidehkarimi@nanometrics.ca)
Takahiko Uchide, Geological Survey of Japan, AIST (t.uchide@aist.go.jp)
Qingkai Kong, University of California, Berkeley (kongqk@berkeley.edu)
Dario Baturan, Nanometrics Incorporated (dariobaturan@nanometrics.ca)
Zhigang Peng, Georgia Institute of Technology (zpeng@gatech.edu)
Ting Chen, Los Alamos National Laboratory (tchen@lanl.gov)
Andrew Delorey, Los Alamos National Laboratory (andrew.delorey@lanl.gov)
Min Chen, Michigan State University (chenmi22@msu.edu)
Chengping Chai, Oak Ridge National Laboratory (chaic@ornl.gov)
Paul Johnson, Los Alamos National Laboratory (paj@lanl.gov)

Oral Presentations

Participant RoleDetailsStart TimeMinutesAction
SubmissionA Probabilistic Framework for Vs3008:30 AM15View
SubmissionUsing Machine Learning to Improve Ground Motion Prediction Equations08:45 AM15View
SubmissionReal-Time Earthquake Detection and Phase Picking Using Temporal Convolutional Networks09:00 AM15View
SubmissionSmart Phone Based Bridge Seismic Monitoring and Vibration Status Realization by Time Domain Convolutional Neural Network09:15 AM15View
SubmissionRobust Arrival Time Uncertainty Estimation Using Gaussian Blurring09:30 AM15View
Other TimePosters and Break09:45 AM60
SubmissionA Deep Neural Network to Identify Foreshocks in Real Time10:45 AM15View
SubmissionRealistic Synthetic Broadband Ground Motions by Machine Learning11:00 AM15View
SubmissionSeismic Signal Clustering Using Deep-Self-Supervised Networks11:15 AM15View
SubmissionRapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network11:30 AM15View
SubmissionSequencing Seismic Data and Models11:45 AM15View
Other TimeLuncheon12:00 PM135
SubmissionNon-Negative Tensor Factorization for Interpretable Unsupervised Signal Discovery in Continuous Seismic Data02:15 PM15View
SubmissionHigh-Resolution Seismic Tomography of Long Beach, CA, Using Machine Learning02:30 PM15View
SubmissionConvolutional Neural Networks & Deep Learning on Spectrograms for Earthquake Detection02:45 PM15View
SubmissionEvent and Noise Discrimination Using Deep Learning03:00 PM15View
SubmissionA Convolutional-Neural-Network-Based Damage Detection Method and Its Application to a Shake Table Test of an 18-Story Steel Frame Building Structure03:15 PM15View
Total:420 Minute(s)
 

Machine Learning in Seismology

Description