Abstract

Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the lp norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.

Highlights

  • The magnetic method is widely used in mineral resource exploration and structure investigation

  • The gradient-based algorithms are independent of the initial guess to start the optimization process and are easy to trap into local minima for nonlinear problems [10]

  • Like other evolution algorithms (EAs), differential evolution (DE) initializes the population within a given search range in a certain way, such as uniform random initialization, chaos initialization, opposition learning initialization, clustering initialization, etc. [45,46,47,48,49]

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Summary

Introduction

The magnetic method is widely used in mineral resource exploration and structure investigation. Position, and shape of the magnetic anomaly body, the magnetic data inversion is usually needed, which mainly includes parameter inversion [1,2], imaging inversion [3,4,5], and physical property inversion [6]. Physical property inversion can recover the shape and depth of complex sources without depending on a specific model. The inversion of physical properties has become one of the most important and commonly used methods. Linear iterative methods, such as the steepest descent method, Newton’s method, and conjugate gradient method, are usually used in physical property inversion [7,8,9]. Contrary to the conventional approaches, the metaheuristic methods like differential evolution (DE) do not require good initial solutions when searching the global minimum

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