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Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks

Journal Article · · Bulletin of the Seismological Society of America
DOI:https://doi.org/10.1785/0120170200· OSTI ID:1469633
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Defense Threat Reduction Agency, Albuquerque, NM (United States)
  3. Hess Corporation, Houston, TX (United States)
  4. Google Inc., Mountain View, CA (United States)
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The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. In this paper, we present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. Finally, with ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
NA0003525
OSTI ID:
1469633
Report Number(s):
SAND-2018-9741J; 667625
Journal Information:
Bulletin of the Seismological Society of America, Vol. 108, Issue 3A; ISSN 0037-1106
Publisher:
Seismological Society of AmericaCopyright Statement
Country of Publication:
United States
Language:
English

Cited By (1)

Machine learning for data-driven discovery in solid Earth geoscience journal March 2019

Figures / Tables (11)

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