Hierarchical exploration of continuous seismograms with unsupervised learning - Université Grenoble Alpes
Pré-Publication, Document De Travail Année : 2021

Hierarchical exploration of continuous seismograms with unsupervised learning

Résumé

We propose a strategy to identify seismic signal classes 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. We then identify signal classes from these relevant features with agglomerative clustering, which allows us to explore the data in a hierarchical way. To test our strategy, we investigate a two-day long seismogram collected in the vicinity of the North Anatolian fault in Turkey. We interpret the automatically inferred clusters by analyzing their 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 with more than 200 repeating events and high-frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy can be used to draw the focus on a certain class of signals and extract subclusters for further analysis. This is interesting, when certain types of signals such as earthquakes are under-represented in the data. The proposed method can be also used to discover new types of signals since it is entirely data-driven.
Fichier principal
Vignette du fichier
essoar.10507113.1.pdf (7.04 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03241271 , version 1 (28-05-2021)
hal-03241271 , version 2 (13-01-2022)

Identifiants

Citer

Rene Steinmann, Leonard Seydoux, Eric Beaucé, Michel Campillo. Hierarchical exploration of continuous seismograms with unsupervised learning. 2021. ⟨hal-03241271v1⟩
123 Consultations
257 Téléchargements

Altmetric

Partager

More