Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning
Name
JGR Solid Earth - 2021 - Steinmann - Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning.pdf
Description
Published version
Size
10.71 MB
Format
Adobe PDF
Checksum (MD5)
37e5ec7e31d7a5a2b5ec71e5ca337322
Author(s)
Steinmann, René
Seydoux, Léonard
Beaucé, Éric
Campillo, Michel
Date Issued
December 16, 2021
Journal
Journal of Geophysical Research: Solid Earth
Publisher
American Geophysical Union
Version
Final published version
Abstract
Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single‐station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two‐day‐long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high‐frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under‐represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data‐driven.
Terms of Use
Creative Commons Attribution-NonCommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2021jb022455
