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A Multi-Instance learning Framework for Seismic Detectors

Technical Report ·
DOI:https://doi.org/10.2172/1673169· OSTI ID:1673169
 [1];  [1];  [1]
  1. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
In this report, we construct and test a framework for fusing the predictions of a ensemble of seismic wave detectors. The framework is drawn from multi-instance learning and is meant to improve the predictive skill of the ensemble beyond that of the individual detectors. We show how the framework allows the use of multiple features derived from the seismogram to detect seismic wave arrivals, as well as how it allows only the most informative features to be retained in the ensemble. The computational cost of the "ensembling" method is linear in the size of the ensemble, allowing a scalable method for monitoring multiple features/transformations of a seismogram. The framework is tested on teleseismic and regional p-wave arrivals at the IMS (International Monitoring System) station in Warramunga, NT, Australia and the PNSU station in University of Utah's monitoring network.
Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories, Albuquerque, NM
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
DOE Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1673169
Report Number(s):
SAND-2020-10538; 691291
Country of Publication:
United States
Language:
English

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