Skip to main content

Vector-Sensor MUSIC for Polarized Seismic Sources Localization

Abstract

This paper addresses the problem of high-resolution polarized source detection and introduces a new eigenstructure-based algorithm that yields direction of arrival (DOA) and polarization estimates using a vector-sensor (or multicomponent-sensor) array. This method is based on separation of the observation space into signal and noise subspaces using fourth-order tensor decomposition. In geophysics, in particular for reservoir acquisition and monitoring, a set of-multicomponent sensors is laid on the ground with constant distance between them. Such a data acquisition scheme has intrinsically three modes: time, distance, and components. The proposed method needs multilinear algebra in order to preserve data structure and avoid reorganization. The data is thus stored in tridimensional arrays rather than matrices. Higher-order eigenvalue decomposition (HOEVD) for fourth-order tensors is considered to achieve subspaces estimation and to compute the eigenelements. We propose a tensorial version of the MUSIC algorithm for a vector-sensor array allowing a joint estimation of DOA and signal polarization estimation. Performances of the proposed algorithm are evaluated.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sebastian Miron.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Miron, S., Le Bihan, N. & Mars, J.I. Vector-Sensor MUSIC for Polarized Seismic Sources Localization. EURASIP J. Adv. Signal Process. 2005, 280527 (2005). https://doi.org/10.1155/ASP.2005.74

Download citation

Keywords and phrases

  • vector-sensor array
  • vector MUSIC
  • interspectral tensor
  • higher-order eigenvalue decomposition for 4th-order tensors