Multi-channel fusion for seismic event detection and classification

Ofir Lindenbaum, Neta Rabin, Yuri Bregman, Amir Averbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automatic detection and identification of seismic events is an important task that is carried out constantly for seismic monitoring. This monitoring process results in a seismic event bulletin that contains information about the detected events, their locations and, magnitudes and type (natural or man made event). Current automatic seismic bulletins comprise a large number of false alarms, which have to be manually corrected by and analysts The progress in machine learning methods and the availability of a big historic seismic archives emerge the template based seismic detection methods. We propose a two stage processes for detection and classification of seismic events. First an energy detector is applied to every channel. Then, we fuse data from multiple channels by applying a multiview kernel based construction. The framework produces a reduced mapping in which every seismic waveform is classified as related to seismic noise, explosion or earthquake.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

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