Multi-parameter imaging by finite difference frequency domain full waveform inversion of GPR data: A guide for sedimentary architecture modeling

Author:

Layek Mrinal Kanti1,Sengupta Probal2

Affiliation:

1. Indian Institute of Engineering Science and Technology Shibpur

2. Indian Institute of Technology Kharagpur

Abstract

AbstractThe need for reconstruction of the distribution of physical properties like dielectric permittivity and electrical conductivity of shallow subsurface sedimentary architecture leads to the development of an optimum strategy of GPR data inversion. In this paper, we present finite difference frequency domain (FDFD) full waveform inversion (FWI) method to get high-resolution subsurface model using GPR data. FWI is an optimization technique which involves in search of the minima between recorded and predicted data. The inversion process includes the quasi-Newton method and simultaneous frequency sampling strategy of irregular sampling. The Hessian term in quasi-Newton algorithm is approximated using preconditioned-LBFGS consideration and the search directions are also optimized after following the Wolfe conditions. At the end of each iteration during inversion, permittivity and conductivity models were updated and became ready to be the initial model for the next iteration. The goals of the study were to establish a suitable guideline for sedimentary-GPR data inversion and to test the effectiveness of newly proposed grid strategy during FWI. In this paper, a comparative study between existing and newly proposed methods are presented with aid of some numerical experiments performed using our own MATLAB programming. Numerical tests conducted on a benchmark from previously published article, established the fact that new grid formulation produces a faster converging rate and required less computation time. This method is very much effective for the realistic sedimentary model of the lossy medium. The proposed method is also applicable for modeling of the acoustic wave with some necessary modifications.

Publisher

Research Square Platform LLC

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