Hierarchical exploration of continuous seismograms with unsupervised learning

Autor: Léonard Seydoux, René Steinmann, Eric Beaucé, Michel Campillo
Přispěvatelé: Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Massachusetts Institute of Technology (MIT), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), Projet Européen ERC F-IMAGE, European Project: 742335,F-IMAGE
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Geophysical Research: Solid Earth
Journal of Geophysical Research : Solid Earth
Journal of Geophysical Research : Solid Earth, 2021, ⟨10.1002/essoar.10507113.1⟩
ISSN: 2169-9313
2169-9356
Popis: International audience; 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.
Databáze: OpenAIRE
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