Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data
M. Rosas-Carbajal, N. Linde, T. Kalscheuer, J. A. Vrugt
Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a two-dimensional (2D) pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are a necessity to stabilize the MCMC inversion results of a highly-discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.
A Study of the Correlation Between Electrical Resistivity and Matric Suction for Unsaturated Ash-Fall Pyroclastic Soils in the Campania Region (Southern Italy)
3-D Magnetotelluric Investigations for geothermal exploration in Martinique (Lesser Antilles). Characteristic Deep Resistivity Structures, and Shallow Resistivity Distribution Matching Heliborne TEM Results
在一项面向可再生能源开发的全球法国计划框架内，通过综合多种方法对马提尼克岛（法国小安的列斯群岛）进行了广泛调查（2012年至2013年），目的是精确确定先前强调的潜在地热资源（Sanjuan等人，2003年）。在所采用的常用调查方法中，我们在马提尼克岛三个最有前途的地热田上方进行了三次大地电磁（MT）调查，即Anses d 'Arlet、Montagne Pel{é}e和Pitons du Carbet远景区。共采集了约100个MT台站，表现出单维或多维行为和静态偏移效应。在处理数据与远程参考，三维MT反演的四个复杂的元素MT阻抗张量没有预静位移校正，已被执行为每个扇区，提供三个三维电阻率模型下降到约12至30公里的深度。通过生成三维电阻率模型，包括马提尼克岛周围从海岸到200公里距离的水深测量，在三维反演中考虑了海岸效应。模型的正演响应用于计算海岸效应系数，该系数在3-D反演过程中应用于计算的MT响应，以与观测数据进行比较。每个区段的三维电阻率模型都继承了不同地质历史，反映了其三维电阻率分布和与火山历史有关的特殊性。特别是，与Montagne Pel{é}e层状火山有关的地热田，其特征是准普遍存在的导电层和相当单调的典型电阻率分布，这使得地热目标的解释变得困难。相反，Anse d 'Arlet地区的电阻率分布完全不同，地热目标被认为与沿主结构轴延伸的不太深的电阻性侵入体相连。除了…
Deep Learning Inversion of Electrical Resistivity Data
School of Qilu Transportation, Shandong University（山东大学齐鲁交通学院）；Geotechnical and Structural Engineering Research Center, Shandong University（山东大学岩土与结构工程研究中心）；Data Science Institute, Shandong University（山东大学数据科学研究院）
Focusing inversion techniques applied to electrical resistance tomography in an experimental tank
提出了一种电阻率层析成像（ERT）数据聚焦反演的算法。ERT是一个典型的不适定问题。正则化是处理这类问题最常用的方法;它基本上包括使用关于目标的先验信息来减少解的模糊性和不稳定性。通过使用最小梯度支持（MGS）稳定泛函，我们在重建过程中引入了以下几何先验信息：异常具有尖锐的边界。所提出的工作是嵌入在一个项目（L.A.R.A.）其目的是从地球物理调查中估计水文地质性质。洛杉矶设施包括一个模拟槽（4 m x 8 m x 1.35 m）; 160个电极位于槽的周围并用于3-D ERT。由于大量的电极和它们的尺寸，重要的是要模拟它们的影响，以正确评估电气系统的响应。所提出的算法中的正向建模是基于所谓的完整电极模型，该模型考虑了电极的存在及其接触阻抗。在本文中，我们比较了不同的正则化泛函上的合成模型所获得的结果。
Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
Software Competence Center Hagenberg (SCCH)（哈根贝格软件能力中心（SCCH））；Euskampus Fundazioa（巴斯克校园基金会）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Ikerbasque (Basque Foundation for Sciences)（伊克巴斯克（巴斯克科学基金会））；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Tecnalia（泰克纳利亚）；Basque Research & Technology Alliance（巴斯克研究与技术联盟）；Pontificia Universidad Católica de Valparaíso（瓦尔帕莱索天主教大学）；Curtin University（科廷大学）
