Three-dimensional inversion of controlled-source electromagnetic data using general measures to evaluate data misfits and model structures

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Geophysical Prospecting Pub Date : 2024-08-06 DOI:10.1111/1365-2478.13576
Yonghyun Chung, Soon Jee Seol, Joongmoo Byun
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Abstract

Quantification of data misfits and model structures is an important step in the non-linear iterative inverse scheme, allowing medium parameters to be iteratively refined through minimization. This study developed a new three-dimensional controlled-source electromagnetic inversion algorithm that allows general measures to be made selectively available for this evaluation. We adopt 2 $\ell _2$ , 1 $\ell _1$ , Huber, hybrid 1 $\ell _1$ / 2 $\ell _2$ , Sech, Cauchy, biweight and 0 $\ell _0$ norms as general measures. The inversion implementation is based on a regularized Gauss–Newton method, and non-quadratic measures are incorporated via the use of an iteratively reweighted least-squares scheme. To exploit current computing power, forward solutions are computed on an edge finite-element discretization using a parallel version of a direct sparse solver, while dense matrix operations in inversion are optimized using the LAPACK library. The behaviours of general measures for evaluating data misfits and model structures are examined in synthetic inversion experiments, focusing on elucidating weighting mechanisms and setting user-defined parameters. A preliminary demonstration is presented, showcasing simultaneous regularization in imaging a toy model containing both sharp and smooth property changes, alongside a field data application for imaging subsurface artificial structures. Our findings highlight the seamless integration of general measures, contributing to improved robustness against data outliers and enhanced spatial properties provided in output models.

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使用一般方法评估数据误差和模型结构的受控源电磁数据三维反演
对数据误差和模型结构进行量化是非线性迭代反演方案中的一个重要步骤,可通过最小化对介质参数进行迭代改进。本研究开发了一种新的三维受控源电磁反演算法,允许有选择地使用一般测量方法进行评估。我们采用、、Huber、混合/、Sech、Cauchy、biweight 和规范作为一般度量。反演实现基于正则化高斯-牛顿方法,并通过使用迭代重权最小二乘方案纳入非二次测量。为了利用当前的计算能力,使用并行版直接稀疏求解器在边缘有限元离散化上计算正向解,同时使用 LAPACK 库优化反演中的密集矩阵运算。在合成反演实验中,研究了评估数据不匹配和模型结构的一般措施的行为,重点是阐明加权机制和设置用户定义的参数。我们进行了初步演示,展示了在对包含尖锐和平滑属性变化的玩具模型进行成像时的同步正则化,以及对地表下人工结构进行成像的现场数据应用。我们的研究结果凸显了通用测量的无缝整合,有助于提高对数据异常值的稳健性,并增强输出模型的空间属性。
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来源期刊
Geophysical Prospecting
Geophysical Prospecting 地学-地球化学与地球物理
CiteScore
4.90
自引率
11.50%
发文量
118
审稿时长
4.5 months
期刊介绍: Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.
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Issue Information Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation A mollifier approach to seismic data representation Analytic solutions for effective elastic moduli of isotropic solids containing oblate spheroid pores with critical porosity An efficient pseudoelastic pure P-mode wave equation and the implementation of the free surface boundary condition
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