CONVOLUTIONAL NEURAL NETWORKS AS A METHOD TO SOLVE ESTIMATION PROBLEM OF ACOUSTIC WAVE PROPAGATION IN POROELASTIC MEDIA

Abstract

This article concerns the problem of the acoustic wave propagation in the 3 layered half-space. Thefirst and third layers are assumed to be solid, whereas the second is assumed to be poroelastic. This article considersresults of finding acoustic wave propagation with regard to its depth and time (further called solution to the forwardproblem) [1-4] and tries to estimate the initial physical properties of aforementioned three layers. The aim of thisarticle is to create a convolutional neural network that estimates said properties, namely speed of sound in each layerand porosity of the second layer. Model was built using “PyTorch”, open-source machine learning library. In order toevaluate the initial coefficients of acoustic wave propagation the convolutional neural networks were used. Duringthe procedure, 3 convolutional hidden layers and 2 fully connected linear hidden layers were used. The data for datacharacterization was simulated by iteratively solving forward problem of acoustic wave propagation described byStokes equation and continuity equation with given initial values of the acoustic model.

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