Event‐Based Training in Label‐Limited Regimes

Linville, Lisa M.

doi:10.1029/2021JB022820

Event‐Based Training in Label‐Limited Regimes

Journal Article · Tue Jan 18 00:00:00 EST 2022 · Journal of Geophysical Research. Solid Earth

DOI:https://doi.org/10.1029/2021JB022820· OSTI ID:1840785

^[1]

Sandia National Laboratories Albuquerque NM USA

Abstract

The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

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Sponsoring Organization:: USDOE

OSTI ID:: 1840785

Alternate ID(s):: OSTI ID: 1842528

Journal Information:: Journal of Geophysical Research. Solid Earth, Journal Name: Journal of Geophysical Research. Solid Earth Journal Issue: 1 Vol. 127; ISSN 2169-9313

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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