{"title":"Poroelastic full-waveform inversion as training a neural network","authors":"","doi":"10.1016/j.jappgeo.2024.105479","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we investigate the full-waveform inversion (FWI) for recovering three media parameters of the poroelastic wave equations as training a neural network. We recast the poroelastic wave simulation in the time domain by the staggered-grid schemes into a process of recurrent neural networks (RNNs). Furthermore, the parameters of RNNs coincide with the inverted parameters in FWI. The algorithm of FWI with a stochastic gradient optimizer named Adam is proposed. The gradients of the objective function with respect to the media parameters are computed by the automatic differentiation. FWI is implemented numerically for three media parameters, i.e., solid density, Lamé parameter of of saturated matrix and shear modulus of dry porous matrix. The numerical computations with two designed models show the good imaging ability of the described method in this paper. It can be applied to invert more media parameters of the poroelastic wave equations.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985124001952","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we investigate the full-waveform inversion (FWI) for recovering three media parameters of the poroelastic wave equations as training a neural network. We recast the poroelastic wave simulation in the time domain by the staggered-grid schemes into a process of recurrent neural networks (RNNs). Furthermore, the parameters of RNNs coincide with the inverted parameters in FWI. The algorithm of FWI with a stochastic gradient optimizer named Adam is proposed. The gradients of the objective function with respect to the media parameters are computed by the automatic differentiation. FWI is implemented numerically for three media parameters, i.e., solid density, Lamé parameter of of saturated matrix and shear modulus of dry porous matrix. The numerical computations with two designed models show the good imaging ability of the described method in this paper. It can be applied to invert more media parameters of the poroelastic wave equations.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.