TY - GEN
T1 - Colored Noise in DOA Estimation from Seismic Data
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
AU - Zimerman, Neta
AU - Rosenblatt, Jonathan D.
AU - Routtenberg, Tirza
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - Array signal processing
KW - Covariance matrices
KW - Direction of arrival (DOA) estimation
KW - Geophysical signals processing
KW - Seismic signal
KW - White noise
UR - http://www.scopus.com/inward/record.url?scp=85107821248&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443417
DO - 10.1109/IEEECONF51394.2020.9443417
M3 - Conference contribution
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1240
EP - 1244
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
Y2 - 1 November 2020 through 5 November 2020
ER -