Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization

Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh
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Abstract

Joint inversion of geophysical datasets is instrumental in subsurface characterization and has garnered significant popularity, leveraging information from multiple geophysical methods. In this study, we implemented the joint inversion of DC resistivity with MT data using the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal non-dominated solutions, eliminating the need for weighting parameters in the objective functions. This set of non-dominated predictions also facilitates the understanding of uncertainty in the predicted model parameters. Through a field case study in the region around Broken Hill in South Central Australia, the paper showcases MOGWO's capabilities in joint inversion, providing confident estimates of the model parameters (resistivity profiles), as indicated by a narrow spread in the suite of solutions. The obtained results are comparable to well established methodologies and highlight the efficacy of MOGWO as a reliable tool in geophysical exploration.
利用多目标灰狼优化法联合反演直流电阻率和 MT 数据
地球物理数据集的联合反演在地下特征描述中非常重要,它充分利用了多种地球物理方法的信息,因而大受欢迎。在本研究中,我们使用多目标灰狼优化(MOGWO)算法实现了直流电阻率与 MT 数据的联合反演。作为广泛使用的灰狼优化算法的扩展,MOGWO 提供了一套帕累托最优非支配解,省去了目标函数中的权重参数。这套非主导预测还有助于理解预测模型参数的不确定性。通过对澳大利亚中南部布罗肯希尔周边地区的实地案例研究,论文展示了 MOGWO 在联合反演方面的能力,提供了对模型参数(电阻率剖面)的可靠估计,这体现在整套解决方案的窄幅分布上。所获得的结果可与成熟的方法相媲美,凸显了 MOGWO 作为地球物理勘探可靠工具的功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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