Colored Noise in DOA Estimation from Seismic Data: An Empirical Study

Neta Zimerman, Jonathan D. Rosenblatt, Tirza Routtenberg

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

Abstract

Estimation of the direction of arrival (DOA) of a seismic signal is required for accurate localization of seismic events, such as earthquakes and human-made explosions. Currently, seismic DOA estimation algorithms are based on the assumption that the additive seismic noise is uncorrelated between sensors. However, in this paper we show by analyzing real data sets that seismic sensors exhibit noise correlation. We calculate a robust estimator of the noise covariance matrix from off-line real data. Then, we present three estimators: 1) the seismic-wave DOA maximum likelihood estimator (MLE) that acknowledges the correlated noise between sensors; 2) the MLE for uncorrelated noise with spherical covariance matrix; and 3) the beamforming Bartlett estimator, which is the method used in seismic applications. We show by numerical simulations on real-data statistics that DOA estimates that do not consider these correlations depart from the true direction and have significantly higher values of mean-squared-error and bias.

Original languageEnglish
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1240-1244
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - 1 Nov 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: 1 Nov 20205 Nov 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period1/11/205/11/20

Keywords

  • Array signal processing
  • Covariance matrices
  • Direction of arrival (DOA) estimation
  • Geophysical signals processing
  • Seismic signal
  • White noise

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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