Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

被引：48

作者：

Lahivaara, Timo ^{[1
]}

Karkkainen, Leo ^{[2
,4
]}

Huttunen, Janne M. J. ^{[2
,4
]}

Hesthaven, Jan S. ^{[3
]}

机构：

[1] Univ Eastern Finland, Dept Appl Phys, Kuopio, Finland

[2] Nokia Technol, Espoo, Finland

[3] Ecole Polytech Fed Lausanne, Computat Math & Simulat Sci, Lausanne, Switzerland

[4] Nokia Bell Labs, Espoo, Finland

来源：

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA | 2018年 / 143卷 / 02期

基金：

芬兰科学院;

关键词：

DISCONTINUOUS GALERKIN METHOD; NONUNIFORM BASIS ORDER; WAVE-PROPAGATION; ELASTIC-WAVES; ACOUSTIC PROPAGATION; MODEL;

D O I：

10.1121/1.5024341

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, a high-order discontinuous Galerkin method is considered, while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, the material porosity and tortuosity is estimated, while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirm the feasibility and accuracy of this approach. (C) 2018 Acoustical Society of America.

引用

页码：1148 / 1158

页数：11

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