Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

Advances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity con...

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Veröffentlicht in:	Journal of geophysical research. Solid earth 2021-09, Vol.126 (9), p.n/a
Hauptverfasser:	Jenkins, William F., Gerstoft, Peter, Bianco, Michael J., Bromirski, Peter D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Antarctica Arrays Artificial neural networks Broadband Clustering Computer applications deep learning El Nino El Nino phenomena Environmental factors Geophysics Ice shelves Land ice Learning algorithms Machine learning Monitoring Neural networks Probabilistic models Representations Ross Ice Shelf Sea ice Sea ice concentrations Seasonal variation Seasonal variations Seismic activity Seismic arrays Seismic data Seismicity Seismological data seismology Seismometers Signal processing Spectrograms Temporal distribution unsupervised machine learning Wind speed
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