Minimum entropy constrained cooperative inversion of electrical resistivity, seismic and magnetic data

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-08-20 DOI:10.1016/j.jappgeo.2024.105490
Anton H. Ziegon, Marc S. Boxberg, Florian M. Wagner
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Abstract

Geophysical methods are widely used to gather information about the subsurface as they are non-intrusive and comparably cheap. However, the solution to the geophysical inverse problem is inherently non-unique, which introduces considerable uncertainties. As a partial remedy to this problem, independently acquired geophysical data sets can be jointly inverted to reduce ambiguities in the resulting multi-physical subsurface images. A novel cooperative inversion approach with joint minimum entropy constraints is used to create more consistent multi-physical images with sharper boundaries with respect to the single-method inversions. Here, this approach is implemented in an open-source software and its applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and magnetic data is investigated. A synthetic 2D ERT and SRT data study is used to demonstrate the approach and to investigate the influence of the governing parameters. The findings showcase the advantage of the joint minimum entropy (JME) stabilizer over separate, conventional smoothness-constrained inversions. The method is then used to analyze field data from Rockeskyller Kopf, Germany. 3D ERT and magnetic data are combined and the results confirm the expected volcanic diatreme structure with improved details. The multi-physical images of both methods are consistent in some regions, as similar boundaries are produced in the resulting models. Because of its sensitivity to hydrologic conditions in the subsurface, observations suggest that the ERT method senses different structures than the magnetic method. These structures in the ERT result do not seem to be enforced on the magnetic susceptibility distribution, showcasing the flexibility of the approach. Both investigations outline the importance of a suitable parameter and reference model selection for the performance of the approach and suggest careful parameter tests prior to the joint inversion. With proper settings, the JME inversion is a promising tool for geophysical imaging, however, this work also identifies some objectives for future studies and additional research to explore and optimize the method.

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电阻率、地震和磁数据的最小熵约束合作反演
地球物理方法具有非侵入性和相对便宜的特点,因此被广泛用于收集地下信息。然而,地球物理反演问题的解本质上是非唯一的,这就带来了相当大的不确定性。作为对这一问题的部分补救,可以对独立获取的地球物理数据集进行联合反演,以减少所得到的多物理地下图像的模糊性。一种新颖的合作反演方法具有联合最小熵约束,与单一方法反演相比,这种方法能生成更一致、边界更清晰的多物理图像。在此,该方法在开源软件中得以实现,并对其在电阻率层析成像(ERT)、地震折射层析成像(SRT)和磁数据上的适用性进行了研究。使用合成的二维电阻率层析成像和 SRT 数据研究来演示该方法,并调查管理参数的影响。研究结果展示了联合最小熵(JME)稳定器相对于单独的、传统的平滑度约束反演的优势。随后,该方法被用于分析德国 Rockeskyller Kopf 的实地数据。三维 ERT 和磁数据相结合,结果证实了预期的火山二迭纪结构,并改善了细节。这两种方法的多物理图像在某些区域是一致的,因为在生成的模型中产生了类似的边界。由于 ERT 方法对地下水文条件的敏感性,观测结果表明 ERT 方法与磁力方法所感应到的结构不同。ERT 结果中的这些结构似乎并没有强制作用于磁感应强度分布,这显示了该方法的灵活性。这两项研究都概述了选择合适的参数和参考模型对该方法性能的重要性,并建议在联合反演之前进行仔细的参数测试。通过适当的设置,JME 反演是一种很有前途的地球物理成像工具,不过,这项工作也为今后的研究确定了一些目标,并为探索和优化该方法进行了更多的研究。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
期刊最新文献
Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra Deep learning-based geophysical joint inversion using partial channel drop method Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques Editorial Board
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