Leveraging Advanced Detection, Association and Source Characterization in Network Seismology [Poster]
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)
Poster Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Detection and Location of Small Seismic Events Surrounding Yellowstone Lake, WY | View |
| Submission | Expanding the Value of Confidence Estimates from Neural Network Classifiers | View |
| Submission | A Frequency-Domain-Based Algorithm for Detecting Induced Seismicity Using Dense Surface N-Arrays | View |
| Submission | White Box Comparison of Different Algorithmic Approaches to Event Detection and Association | View |
| Submission | Using Waveform Correlation to Reduce Analyst Workload Due to Repeating Mining Blasts | View |
| Submission | Local Earthquake Detection and Location From Continuous Seismic Waveforms in Xichang Seismic Array With U-Net | View |
| Submission | A Neural Network Based Small Seismic Event Detector and Locator | View |
| Submission | Global Earthquake Detection with Machine Learning: Exploring Array and Network Based Detection | View |
| Submission | Considerations for Regional-Scale Earthquake Assessment Using Seismic Arrays | View |
| Submission | High-Precision Delineation of Fault Geometry and Stress Using Next-Generation Seismic Monitoring: West Texas Case Study | View |
| Submission | Denoising of Seismic Signals Recorded at Local to Near-Regional Distances Using Deep Convolutional Neural Networks | View |
Leveraging Advanced Detection, Association and Source Characterization in Network Seismology [Poster]
Description