Earthquake-explosion discrimination using diffusion maps

N. Rabin, Y. Bregman, O. Lindenbaum, Y. Ben-Horin, A. Averbuch

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Discrimination between earthquakes and explosions is an essential component of nuclear test monitoring and it is also important for maintaining the quality of earthquake catalogues. Currently used discrimination methods provide a partial solution to the problem. In this work, wem apply advanced machine learning methods and in particular diffusion maps for modelling and discriminating between seismic signals. Diffusion maps enable us to construct a geometric representation that capture the intrinsic structure of the seismograms. The diffusion maps are applied after a pre-processing step, in which seismograms are converted to normalized sonograms. The constructed low-dimensional model is used for automatic earthquake-explosion discrimination of data that are collected in single seismic stations. We demonstrate our approach on a data set comprising seismic events from the Dead Sea area. The diffusion-based algorithm provides correct discrimination rate that is higher than 90 per cent.

Original languageEnglish
Article numberggw348
Pages (from-to)1484-1492
Number of pages9
JournalGeophysical Journal International
Volume207
Issue number3
DOIs
StatePublished - 1 Dec 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Authors 2016.

Keywords

  • Computational seismology
  • Seismic monitoring and test-ban treaty verification
  • Time-series analysis

Fingerprint

Dive into the research topics of 'Earthquake-explosion discrimination using diffusion maps'. Together they form a unique fingerprint.

Cite this