Room: Exhibit Hall
Date: 4/15/2025
Session Time: 8:00 AM to 5:45 PM (local time)
Building and Decoding High-resolution Earthquake Catalogs With Statistical and Machine-learning Tools
Recent application of advanced earthquake detection techniques such as template matching and machine learning (ML) have produced exponential growth in the quantity of earthquakes listed in next-generation high-resolution catalogs around the world. These improved catalogs can include relocated seismicity on the order of tens of thousands to a few million individual events, making it challenging to use standard analysis and modeling tools such as the Epidemic Type Aftershock Sequence (ETAS) model or the nearest neighbor algorithm for de-clustering, to extract key features and forecast seismicity. This session welcomes contributions on recent efforts to build high-resolution earthquake catalogs using waveform-based and ML methods such as transformers or foundation models. We also solicit presentations on innovative methods to decode these high-resolution catalogs using statistical analyses and advancing our understanding of earthquake interactions, swarms, fault geometries or localization processes, as well as predictive modeling approaches including neural and Bayesian point processes, deep Gaussian process models, and other generative models. We especially encourage submissions that compare new results with benchmarks, e.g. with respect to standard catalogs, or to model benchmarks such as statistical ETAS models or physics-based models such as Coulomb Rate-and-State (CRS) models to forecast seismicity.
Conveners
Xu Si, Georgia Institute of Technology (xsi33@gatech.edu)
Maximilian J. Werner, University of Bristol (max.werner@bristol.ac.uk)
Shixiang Zhu, Carnegie Mellon University (shixianz@andrew.cmu.edu)
Poster Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Developing Machine-learning-based Seismic Data Processing Tools for a Carbon Storage Site | View |
| Submission | Investigating Complex Seismogenic Structures in the Northern Longitudinal Valley, Eastern Taiwan Through an AI-based Catalog of the April 3, 2024 Mw 7.3 Hualien Earthquake | View |
| Submission | High-resolution Aftershock Catalog of the 2023 Kahramanmaraş Earthquake Sequence Reveals Detailed Fault Structures in Southeastern Türkiye | View |
| Submission | Automatic Phase Picking Model for Ocean Bottom Seismic Data: Phasenet Model Trained Using Japanese S-net Data | View |
| Submission | Earthquake Source Depth Determination Using Single Station Waveforms and Deep Learning | View |
| Submission | Improving Earthquake Detection and Localization in Hawaii With Deep Learning and High-performance Computation | View |
| Submission | Towards a Deep Learning Approach for Short-term Data-driven Spatiotemporal Seismicity Rate Forecasting Using Standard and High-resolution Earthquake Catalogues | View |
| Submission | Denoising Score Matching for Online Change Point Detection | View |
| Submission | Seasonal Variations in the Magnitude-frequency Distribution of California Earthquakes | View |
| Submission | Using Lossy Compression to Speed Up Seismic Event and Ambient Noise Analysis | View |
Building and Decoding High-resolution Earthquake Catalogs With Statistical and Machine-learning Tools [Poster]
Description