International audienc

No abstract

International audienceIn this paper, we use a biquaternion formalism to model vector-sensor signals carrying polarization information. This allows a concise and elegant way of handling signals with eight-dimensional (8-D) vector-valued samples. Using this model, we derive a biquaternionic version of the well-known array processing MUSIC algorithm, and we show its superiority to classically used long-vector approach. New results on biquaternion valued matrix spectral analysis are presented. Of particular interest for the biquaternion MUSIC (BQ-MUSIC) algorithm is the decomposition of the spectral matrix of the data into orthogonal subspaces. We propose an effective algorithm to compute such an orthogonal decomposition of the observation space via the eigenvalue decomposition (EVD) of a Hermitian biquaternionic matrix by means of a newly defined quantity, the quaternion adjoint matrix. The BQ-MUSIC estimator is derived and simulation results illustrate its performances compared with two other approaches in polarized antenna processing (LV-MUSIC and PSA-MUSIC). The proposed algorithm is shown to be superior in several aspects to the existing approaches. Compared with LV-MUSIC, the BQ-MUSIC algorithm is more robust to modelization errors and coherent noise while it can detect less sources. In comparaison with PSA-MUSIC, our approach exhibits more accurate estimation of direction of arrival (DOA) for a small number of sources, while keeping the polarization information accessible

International audienceClassical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first introduced as a tool to easily solve our problem and to preserve the data structure. Within this multivariate framework, we then present sparse coding methods: multivariate orthogonal matching pursuit (M-OMP), which provides sparse approximation for multivariate signals, and multivariate DLA (M-DLA), which empirically learns the characteristic patterns (or features) that are associated to a multivariate signals set, and combines shift-invariance and online learning. Once the multivariate dictionary is learned, any signal of this considered set can be approximated sparsely. This multivariate framework is introduced to simply present the 2D rotation invariant (2DRI) case. By studying 2D motions that are acquired in bivariate real signals, we want the decompositions to be independent of the orientation of the movement execution in the 2D space. The methods are thus specified for the 2DRI case to be robust to any rotation: 2DRI-OMP and 2DRI-DLA. Shift and rotation invariant cases induce a compact learned dictionary and provide robust decomposition. As validation, our methods are applied to 2D handwritten data to extract the elementary features of this signals set, and to provide rotation invariant decomposition

Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long term monitoring, manual inspection is no longer a viable option. As in many Big Data situations, classic Machine Learning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring at a larger scale. This paper focuses on integrated and operational tools dedicated to the automatic analysis of volcano-seismic signals. Namely we review (i) tools for the optimal representation of volcano-seismic signals (feature space) and the available methods for volcano-seismic events (ii) detection and (iii) classification. We then propose an architecture for the automatic classification of volcano-seismic events. Our prediction system is tested on 6 years of recordings containing 109434 volcano-seismic events acquired from Ubinas volcano (the most active volcano in Perú). Our new proposed model is build using supervised machine learning algorithms (Support Vector Machine) and reaches 92.2% of correct classification over six classes. This prediction model is then used to fully analyze the 6 years of recorded signals.

Summary Surface waves recorded by the nine broad‐band stations of the INDEPTH II experiment are analysed to study the crustal structure of southern Tibet. Their frequency range is between approximately 0.015 and 0.050 Hz (i.e. between 20 and 60 s period). Phase velocity dispersion curves are calculated for the regions north and south of the Tsangpo suture, using Wiener filtering of fundamental mode Rayleigh waves incident upon the array with different backazimuths. The two dispersion curves are inverted to obtain the S‐wave crustal models north and south of the suture. They show that a low‐velocity layer is present in the lower crust north but not south of the Tsangpo suture. To confirm these findings, variations in the amplitudes of the Rayleigh waves across the suture are interpreted by numerical simulation. The strong amplitude variations at a 30 s period are reproduced using a model with a low‐velocity layer in the lower crust north of the suture, a normal high‐velocity layer to the south, and a sharp transition between these layers.

The estimation of the impulse response (IR) of a propagation channel may be of great interest for a large number of underwater applications: underwater communications, sonar detection and localization, marine mammal monitoring, etc. It quantifies the distortions of the transmitted signal in the underwater channel and enables geoacoustic inversion. The propagating signal is usually subject to additional and undesirable distortions due to the motion of the transmitter-channel-receiver configuration. This paper shows the effects of the motion while estimating the IR by matched filtering between the transmitted and the received signals. A methodology to compare IR estimation with and without motion is presented. Based on this comparison, a method for motion effect compensation is proposed in order to reduce motion-induced distortions. The proposed methodology is applied to real data sets collected in 2007 by the Service Hydrographique et Océanographique de la Marine in a shallow water environment, proving its interest for motion effect analysis. Motion compensated estimation of IRs is computed from sources transmitting broadband linear frequency modulations moving at up to 12 knots in the shallow water environment of the Malta plateau, South of Sicilia.

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.