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Modeling Seismic Network Detection Thresholds Using Production Picking Algorithms

Session: Network Seismology: Keeping the Network Running While Integrating New Technologies II

Type: Oral

Date: 4/22/2021

Presentation Time: 02:15 PM Pacific

Description: 

The ability to estimate a network’s detection threshold (the minimum magnitude earthquake that can be reliably detected) is a critical part of network design and can drive network maintenance efforts. A station’s ability to detect an earthquake has typically been determined by modeling the predicted spectral amplitude for an earthquake (including computing attenuation for a range of epicentral distances) and comparing that amplitude to the average station background noise level. This approach has significant uncertainty because of unknown regional attenuation and complications in computing small event power spectra. We develop an approach to determine network detection thresholds using a multi-band picking algorithm that is currently in use at the USGS National Earthquake Information Center (NEIC). We apply this picking algorithm to cataloged earthquakes to determine an empirical relationship of the observability of earthquakes as a function of magnitude and distance. Using these relationships, we produce maps of detection thresholds as a function of station spacing, station spatial configuration, and station noise levels. We show how this approach allows for operational modeling of network capabilities and the assessment of individual station contributions. We also show that a single quiet station is exponentially more beneficial for earthquake monitoring than several closely spaced noisy stations.

Presenting Author: David Wilson

Student Presenter: No

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