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Hierarchical exploration of continuous seismograms with unsupervised learning
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  • Rene Steinmann,
  • Leonard Seydoux,
  • Eric Beaucé,
  • Michel Campillo
Rene Steinmann
Université Grenoble Alpes, Université Grenoble Alpes

Corresponding Author:[email protected]

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Leonard Seydoux
Université Grenoble Alpes, Université Grenoble Alpes
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Eric Beaucé
Massachusetts Institute of Technology, Massachusetts Institute of Technology
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Michel Campillo
Université Joseph Fourier, Grenoble, Université Joseph Fourier, Grenoble
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Continuous seismograms contain a wealth of information with a large variety of signals with different origins. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of seismic 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 in- dependent 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 crisis that includes more than 200 repeating events and high-frequent signals with correlated 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.
Jan 2022Published in Journal of Geophysical Research: Solid Earth volume 127 issue 1. 10.1029/2021JB022455