Analysis of environmental electromagnetic signal using nonnegative matrix factorization minimizing quasi-L1 norm

Motoaki Mouri, Arao Funase, Andrzej Cichocki, Ichi Takumi, Hiroshi Yasukawa

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

5 Citations (Scopus)

Abstract

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. We have been measuring the Extremely Low Frequency (ELF) range all over Japan. Our goal is to predict earthquakes using EM radiation waves. The recorded data often contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. It is necessary to eliminate noises from observed signals in a preprocessing step. In previous researches, we used ISRA, an algorithm of the Nonnegative Matrix Factorization (NMF), to estimate source signal. However, ISRA is not robust for outliers because ISRA's cost function is based on square distance. In order to improve robustness, we should use lower order cost function. In this paper, we propose a nonnegative matrix factorization method using quasi-L1 norm in cost function (quasi-L1 NMF). In the experiment using ELF observed signals that include outliers, the proposed method extracts source signals more accurately than ISRA.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages1846-1849
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
Country/TerritoryCanada
CityVancouver, BC
Period24/07/1129/07/11

Keywords

  • earthquake
  • electromagnetic wave
  • l1 norm
  • nonnegative matrix factorization
  • source separation

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