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NEIC Developments: Updates on the U.S. Geological Survey National Earthquake Information Center’s Earthquake Monitoring Systems

Description: 

The U.S. Geological Survey (USGS) National Earthquake Information Center’s (NEIC) real-time global monitoring systems support the NEIC missions to detect earthquakes globally, estimate source parameters, and produce a reviewed earthquake bulletin. The NEIC continually develops and deploys seismic algorithms, systems, and interfaces to meet the NEIC’s evolving needs. NEIC analysts, developers, and researchers have made several significant algorithmic advancements to improve the NEIC’s ability to characterize earthquakes, including the updates to its W-Phase moment tensor and location algorithms, new web-based services and seismic analysis interfaces, and improvements to machine learning characterization of seismic phases.

The NEIC has rewritten its W-Phase moment-tensor software to improve its computational speed and the stability of its automatic solutions and is now working towards transforming this software into a web service. The NEIC retired its legacy Fortran locator in favor of a new Java version which is implemented as a web service. This new locator has additional algorithmic improvements including slab models to help constrain event depths, updated statistics of phase-distance specific uncertainties, new strategies to better compute locations with mixed local-to-teleseismic observations, and more robust uncertainty estimates. The NEIC continues to develop a new seismic analysis system, Motus, and has largely completed the back-end web data notification services and a new web-based interface for real-time and research analysis of W-Phase moment magnitude (Mww). The NEIC is currently developing new web-based repicking and modernized Catalog Review interfaces, both extensions of the completed Quick-Look interface. The NEIC has developed the AI-Driven Earthquake Monitor (AIDEM), a python package that provides a framework to test and implement machine learning models in our operational settings. AIDEM takes continuous waveform data as an input and produces phase detections along with characterization labels provided by single-station ML models, which are utilized by the NEIC associator to improve detections.

Session: Network Seismology: Recent Developments, Challenges and Lessons Learned - IV

Type: Oral

Date: 5/2/2024

Presentation Time: 11:00 AM (local time)

Presenting Author: John

Student Presenter: No

Invited Presentation: 

Authors