Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning.

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 collecte

SUBMITTER: Steinmann R 

PROVIDER: S-EPMC9285886 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature