Leveraging Advanced Detection, Association and Source Characterization in Network Seismology
In a classic seismic monitoring framework, automatic pickers detect earthquakes, individual detections are associated into events and events are further characterized using routine methods (e.g., single-event locators, magnitude estimators). While this processing structure underlies the operations of the majority of seismic networks, researchers continue to develop novel ways to extract additional earthquake data from continuous waveforms. Template matching is routinely applied to lower detection thresholds. Machine learning algorithms detect earthquake signals and further classify key seismic characteristics (e.g., phase-type). Multiple-event relocation algorithms retrospectively enhance earthquake hypocenter estimates. While many such techniques have vastly improved our understanding of cataloged seismicity, hurdles remain when applying these techniques to real-time systems and therefore they have not been routinely adopted. In this session, we invite submissions that investigate novel earthquake detection and characterization techniques, particularly with a focus on how these could be applied in a real-time environment to regional and global seismic networks.
Conveners
William L. Yeck, U.S. Geological Survey (wyeck@usgs.gov); Kris Pankow, University of Utah (pankowseis2@gmail.com); Gavin Hayes, U.S. Geological Survey (ghayes@usgs.gov); Paul Earle, U.S. Geological Survey (pearle@usgs.gov); Harley Benz, U.S. Geological Survey (benz@usgs.gov)
Oral Presentations
Participant Role | Details | Start Time | Minutes | Action |
---|---|---|---|---|
Submission | Developing Convolutional Neural Networks as Efficient Tools for Earthquake Detection, Localization and Source Characterization - Work in Progress and Key Challenges | 03:45 PM | 15 | View |
Submission | Source-Scanning based on Navigated Automatic Phase-Picking (S-SNAP) for Delineating the Spatiotemporal Distribution of Earthquake Sequence in Real Time: Application to the 2019 Ridgecrest, California Sequence | 04:00 PM | 15 | View |
Submission | Towards an Improved Earthquake Catalog for Northern California Using Deep-Learning-Based Arrival Time Picking and Graph-Based Phase Association | 04:15 PM | 15 | View |
Submission | A Matched Filtering-Based Workflow for Characterizing Swarm and Aftershock Sequences | 04:30 PM | 15 | View |
Submission | Source Parameters and Moment Magnitudes From Seismogram Envelopes – The Coda Calibration Tool | 04:45 PM | 15 | View |
Total: | 75 Minute(s) |
Leveraging Advanced Detection, Association and Source Characterization in Network Seismology
Description