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

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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.