Machine Learning in Seismology [Poster]
Date: 4/24/2019
Time: 6:00 PM to 11:00 PM
Room: Grand Ballroom
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)
Poster Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Machine Learning Models for Classifying Variations in Emergent and Impulsive Seismic Noise | View |
| Submission | What Triggered the Tremors in Nigeria on Sept 5-7, 2018? | View |
| Submission | [Withdrawn] Detecting Low Magnitude Seismic Events Using Convolutional Neural Networks | View |
| Submission | Automatic Phase Picking by Deep Learning | View |
| Submission | An Automated Station Assessment Based on Deep Learning | View |
| Submission | Network Analysis to Characterize Seismic Ground Motion Variability | View |
| Submission | Operational Real-Time Automatic Seismic Catalog Generator Utilizing Machine Learning: Performance Review Over a One Year Period in Production | View |
| Submission | A Machine Learning Approach to Identify Landslides With Seismic Waves Using Support Vector Machine Method | View |
| Submission | Automatic Waveform Quality Control for Surface Waves Using Machine Learning Techniques | View |
| Submission | Convolutional Neural Network for Seismic Phase Picking, Performance Demonstration in the Absence of Extensive Training Data | View |
| Submission | A Neural Network Based Multi-Component Earthquake Detection Method | View |
| Submission | Inversionnet: A Real-Time and Accurate Full Waveform Inversion With CNNs and Continuous CRFs | View |
| Submission | [Withdrawn] Two Combinatorial Optimization Methods That Determine On-Fault Earthquake Magnitude Distributions | View |
Machine Learning in Seismology [Poster]
Description