Development of the Inversion Method for Transient Electromagnetic Data

Guoqiang Xue,Hai Li,Xin Wu,Yiming He,Junjie Xue

doi:10.1109/access.2020.3013626

Abstract

Transient electromagnetic method (TEM) is a geophysical tool to obtain resistivity distribution in the subsurface. Combining with the resistivity property of typical rocks, the TEM method can make inferences of the geological maps underground. The inversion method is the main technique to extract resistivity form the recorded TEM data. There are various inversion methods that have been applied to TEM data, each of which favors different model structures. It is essential to choose the optimal inversion algorithm for a TEM survey in a given geological setting. Thus, this article presents a systematic summary of recent developments of inversion methods for TEM data. We first summarize the basic concept of the TEM inversion theory. Then, the recent developments TEM inverse method are divided into deterministic inversion and stochastic inversion. For the deterministic method, we present the development of constrained inversion and joint inversion. For the stochastic method, we analyze the particle swarm optimization, Bayesian inversion, and TEM pseudo-seismic imaging. Thereafter, we prospect the future research direction of the TEM method.

Highlights

Geophysics is the main method to access information about the earth’s interior
As the earth’s interior is complex, to make inversion results as close to the real geological structure as possible, we usually use a simple model to approximate the real geological structure and modify the model parameters based on the fitness of
Li et al [19] combined an adaptive genetic algorithm with the feasible region method to calculate the longitudinal conductance of Transient electromagnetic method (TEM), which improved the accuracy of the inversion result

Summary

INTRODUCTION

Geophysics is the main method to access information about the earth’s interior. Based on physical principles, it can be used to make inferences about the parameters of underground targets by recording data sets on the earth’s surface. Since the 1990s, researchers have gradually introduced various geophysical inversion methods to invert TEM data. The deterministic and stochastic inversion are both effective ways to recover model parameters from the geophysical data sets. A brief introduction of deterministic inversion of TEM data with a multi-dimensional forward operator is given as follows: Wang and Hohmann [12] applied the conjugate gradient method to the study of TEM inversion and realized a 2D inversion of TEM data. Yang and Oldenburg [16] developed an inversion algorithm based on the Gauss-Newton method under a 3D forward modeling of the finite volume method in the time domain and successfully applied it to field data in many mining areas in Canada. Li et al [19] combined an adaptive genetic algorithm with the feasible region method to calculate the longitudinal conductance of TEM, which improved the accuracy of the inversion result. Lin et al [30] proposed a lateral constraint inversion method to invert magnetic resonance and TEM data

AN OVERVIEW OF TEM INVERSION THEORY

CONSTRAINED INVERSION

JOINT INVERSION

STOCHASTIC INVERSION OF TEM DATA

BAYESIAN INVERSION

CONCLUSION