neic-machine-learning
July 23, 2020
NEIC Machine Learning Applications contains various seismic machine learning algorithms developed and used by by the United States Geological Survey, National Earthquake Information Center. These algorithms apply machine learning techniques to seismic processing problems such as seismic phase classification, source-receiver distance classification, and seismic wave arrival time repicking.
Citation Information
| Publication Year | 2020 |
|---|---|
| Title | neic-machine-learning |
| DOI | 10.5066/P9ICQPUR |
| Authors | John Patton, William L Yeck |
| Product Type | Software Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | Earthquake Hazards Program |
Related
Leveraging deep learning in global 24/7 real-time earthquake monitoring at the National Earthquake Information Center
Machine‐learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real‐time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep‐learning tools into NEIC...
Authors
William L. Yeck, John Patton, Zachary E. Ross, Gavin P. Hayes, Michelle M. Guy, Nicholas Ambruz, David R. Shelly, Harley M. Benz, Paul S. Earle
Related
Leveraging deep learning in global 24/7 real-time earthquake monitoring at the National Earthquake Information Center
Machine‐learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real‐time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep‐learning tools into NEIC...
Authors
William L. Yeck, John Patton, Zachary E. Ross, Gavin P. Hayes, Michelle M. Guy, Nicholas Ambruz, David R. Shelly, Harley M. Benz, Paul S. Earle