Seismic Event Discrimination Using Deep CCA

Ofir Lindenbaum*, Neta Rabin, Yuri Bregman, Amir Averbuch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Detection and discrimination of seismic events have important implications. Precise detection of earthquakes may help prevent collateral damage and even save lives. On the other hand, the ability to identify explosions reliably not only helps prevent false alarms but also is crucial for monitoring nuclear experiments. In this work, we present a method for automatic discrimination of seismic events. A neural network is trained to fuse information from multiple seismic channels into a correlated space. Then, we augment the minority class to avoid class imbalance. Finally, a tree-based classifier is used to estimate the nature of the suspected event. We apply the proposed approach to 1609 events collected in Israel and Jordan. Our framework demonstrates improved precision and recall scores.

Original languageEnglish
Article number8946693
Pages (from-to)1856-1860
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume17
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Classification
  • data augmentation
  • deep canonical correlation analysis (DCCA)
  • seismic discrimination

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