Filtering Deterministic Layer Effects in Imaging

Sensor array imaging arises in applications such as nondestructive evaluation of materials with ultrasonic waves, seismic exploration, and radar. The sensors probe a medium with signals and record the resulting echoes, which are then processed to determine the location and reflectivity of remote reflectors. These could be defects in materials such as voids, fault lines or salt bodies in the earth, and cars, buildings, or aircraft in radar applications. Imaging is relatively well understood when the medium through which the signals propagate is smooth, and therefore nonscattering. But in many problems the medium is heterogeneous, with numerous small inhomogeneities that scatter the waves. We refer to the collection of inhomogeneities as clutter, which introduces an uncertainty in imaging because it is unknown and impossible to estimate in detail. We model the clutter as a random process. The array data is measured in one realization of the random medium, and the challenge is to mitigate cumulative clutter scattering so as to obtain robust images that are statistically stable with respect to different realizations of the inhomogeneities. Scatterers that are not buried too deep in clutter can be imaged reliably with the coherent interferometric (CINT) approach. But in heavy clutter the signal-to-noise ratio (SNR) is low and CINT alone does not work. The “signal,” the echoes from the scatterers to be imaged, is overwhelmed by the “noise,” the strong clutter reverberations. There are two existing approaches for imaging at low SNR: The first operates under the premise that data are incoherent so that only the intensity of the scattered field can be used. The unknown coherent scatterers that we want to image are modeled as changes in the coefficients of diffusion or radiative transport equations satisfied by the intensities, and the problem becomes one of parameter estimation. Because the estimation is severely ill-posed, the results have poor resolution, unless very good prior information is available and large arrays are used. The second approach recognizes that if there is some residual coherence in the data, that is, some reliable phase information is available, it is worth trying to extract it and use it with well-posed coherent imaging methods to obtain images with better resolution. This paper takes the latter approach and presents a first attempt at enhancing the SNR of the array data by suppressing medium reverberations. It introduces filters, or annihilators of layer backscatter, that are designed to remove primary echoes from strong, isolated layers in a medium with additional random layering at small, subwavelength scales. These strong layers are called deterministic because they can be imaged from the data. However, our goal is not to image the layers, but to suppress them and thus enhance the echoes from compact scatterers buried deep in the medium. Surprisingly, the layer annihilators work better than intended, in the sense that they suppress not only the echoes from the deterministic layers, but also multiply scattered ones in the randomly layered structure. Following the layer annihilators presented here, other filters of general, nonlayered heavy clutter have been developed. We review these more recent developments and the challenges of imaging in heavy clutter in the introduction in order to place the research presented here in context. We then present in detail the layer annihilators and show with analysis and numerical simulations how they work

Implementação e avaliação de algoritmo para o imageamento de objetos enterrados em ambientes submersos multi-camadas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2016.Com o avanço tecnológico das últimas décadas, a sociedade vem desenvolvendo cada vez mais atividades em áreas submersas, seja na extração de recursos naturais ou na realização de obras de engenharia. Dessa forma, tornou-se importante a caracterização desses ambientes submersos não somente com relação a sua topografia como também à configuração de seu substrato. Neste trabalho será implementado um procedimento de imageamento baseado na migração de Kirchhoff e no método da fonte imagem, que tem como objetivo gerar imagens que auxiliem na interpretação de dados de investigações acústicas em ambientes submersos multicamadas. Sinais medidos por arranjos de hidrofones passam por uma série de processamentos, descritos em detalhes no trabalho, com o objetivo de gerar uma imagem que auxilie a localização de objetos enterrados no substrato de ambientes submersos. Para gerar os dados utilizados como entrada dos algoritmos implementados, ambientes submersos foram modelados com o Método das Diferenças Finitas, sendo as diversas camadas de sedimento modeladas como fluidos equivalentes. O algoritmo implementado foi então testado para diferentes dados de entrada, obtidos com a variação de diversos parâmetros construtivos do arranjo de hidrofones.Abstract : With the technological advances of the last decades, society have been developing several activities in underwater environments, from the extraction of natural resources to the construction of engineering projects. Therefore, it has become important to charactherize underwater environments, not only on what concerns it?s bathymetry but also the configuration of it?s substrate. In the following work, an imaging process based on the image source method and the Kirchhoff migration will be implemented. This process aims to facilitate the interpretation of seismic data. Signals measured by an array of hydrophones go through a processing procedure, described in detail in the document, in order to generate a pressure map that helps locating objects buried in the substrate of underwater environments. To test the implemented algorithm, simulated data was generated using the freely distributed Finite Difference software SimSonic©. The substrate was modeled as equivalent fluid. The implemented algorithm was then tested by a parametric analysis, where several measurement?s and array?s parameters were simulated