Tensor decomposition exploiting diversity of propagation velocities: Application to localization of icequake events

The problem of direction of arrival (DoA) estimation of seismic plane waves impinging on an array of sensors is considered from a new deterministic perspective using tensor decomposition techniques. In addition to temporal and spatial sampling, further information is taken into account, based on the different propagation speed of body waves (P and S) through solid media. Performances are evaluated through simulated data in terms of the Cramér-Rao bounds and compared to other reference methods such as ESPRIT and MUSIC, in the presence of additive Gaussian circular noise. The proposed approach is then applied to real seismic data recorded at the Argentière glacier, occurring at the interface between the ice mass and the underlying bedrock. MUSIC and ESPRIT rely on the estimation of the covariance matrix of received data, thus requiring a large number of time samples. Moreover, information about propagation speed diversity is not taken into account by existing models in array processing. The discovered advantage in terms of the average error in estimating the direction of arrival of body waves is noteworthy, especially for a low number of sensors, and in separating closely located sources. Additionally, an improvement of precision in processing real seismic data is observed.