Improving the imaging capabilities of modern portable loop-loop electromagnetic induction (EMI) systems using ground-penetrating radar (GPR) data

使用探地雷达 (GPR) 数据提高现代便携式环路电磁感应 (EMI) 系统的成像能力

• 批准号: 418056756
• 负责人: Dr. Julien Guillemoteau, Ph.D.
• 金额: --
• 依托单位: Institut für Geowissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/418056756?language=en
• 关键词: Improving; imaging; capabilities; modern; portable

In near-surface geophysical applications, portable loop-loop electromagnetic induction (EMI) sensors are increasingly used to rapidly image the electrical conductivity of the uppermost meters of the subsurface across rather large areas (several hectares). The resulting 3D models of electrical conductivity can serve to characterize a large selection of targets because many rocks, soil layers and anthropogenic materials show contrast of electrical conductivity. However, because electrical conductivity of subsurface materials is influenced by many different soil and rock properties, the interpretation of the EMI electrical conductivity models is often complex and non-unique; especially, in contexts where no reliable background information about the imaged subsurface is available. Moreover, even if the nature of the targets is known, EMI data are limited in terms of their structural resolution capabilities because each measurement is sensitive to an integrated volume of subsurface.The ground-penetrating radar (GPR) method is another popular near-surface imaging method. As a wave-based imaging technique, which is sensitive to dielectric permittivity contrasts, GPR is typically considered as the geophysical method providing the highest structural resolution. However, a more quantitative analysis in terms of physical property models is often limited with typical 2D/3D GPR reflection data. Although both EMI and GPR methods are used to explore similar depth ranges, they have never been combined in the framework of a quantitative integrated imaging/inversion procedure. Considering weaknesses and strengths of each method and that they can provide complementary information, we hypothesize that a quantitative integration results in an improved characterization of subsurface structures and properties. In this project, we propose to develop and evaluate approaches for quantitively combining EMI and GPR data in order to reduce the classical ambiguities and resolution limitations encountered when using the EMI method only. In doing so, we will first study the typical non-uniqueness of the EMI methods by comparing three different EMI data inversion strategies on several types of controlled targets. Then, we will focus on incorporating the structures as derived from GPR data into the inversion of EMI data, and study how such a strategy helps to reduce the non-uniqueness of the inverted EMI models. In this respect, we will develop and evaluate two constrained inversion strategies: one deterministic grid-based approach, which was recently reported in the literature for larger scale problems, and one stochastic parametric approach. Thus, we expect from this project general conclusion regarding the possibilities of combining EMI and GPR data as well as methodological innovations regarding EMI data inversion further improving the imaging capabilities and the applicability of the EMI method.

在近地表地球物理应用中，便携式环路电磁感应 (EMI) 传感器越来越多地用于快速成像相当大区域（几公顷）的地下最高米的电导率。由此产生的 3D 电导率模型可用于表征大量目标，因为许多岩石、土层和人造材料显示出电导率的对比。然而，由于地下材料的电导率受到许多不同的土壤和岩石特性的影响，因此 EMI 电导率模型的解释通常很复杂且不唯一；特别是在没有关于成像地下的可靠背景信息的情况下。此外，即使目标的性质已知，EMI 数据在结构分辨率方面也受到限制，因为每次测量都对地下的整体体积敏感。探地雷达 (GPR) 方法是另一种流行的近地表方法成像方法。作为一种对介电常数对比敏感的基于波的成像技术，探地雷达通常被认为是提供最高结构分辨率的地球物理方法。然而，物理特性模型方面的定量分析通常受限于典型的 2D/3D GPR 反射数据。尽管 EMI 和 GPR 方法都用于探索相似的深度范围，但它们从未在定量集成成像/反演程序的框架中结合起来。考虑到每种方法的弱点和优点以及它们可以提供补充信息，我们假设定量积分可以改善地下结构和特性的表征。在这个项目中，我们建议开发和评估定量组合 EMI 和 GPR 数据的方法，以减少仅使用 EMI 方法时遇到的经典模糊性和分辨率限制。为此，我们将首先通过比较几种不同类型的受控目标的三种不同 EMI 数据反演策略来研究 EMI 方法的典型非唯一性。然后，我们将重点关注将从 GPR 数据导出的结构合并到 EMI 数据的反演中，并研究这种策略如何帮助减少反演 EMI 模型的非唯一性。在这方面，我们将开发和评估两种约束反演策略：一种是基于确定性网格的方法，最近在文献中报道了针对更大规模问题的方法，另一种是随机参数方法。因此，我们期望从该项目中得出关于结合 EMI 和 GPR 数据的可能性的总体结论，以及关于 EMI 数据反演的方法创新，进一步提高 EMI 方法的成像能力和适用性。