Half-space born approximation modeling and inversion for cross-well radar sensing of contaminants in soil

Detection of dense non-aqueous phase liquids (DNAPLs) is a practical interesting problem in geophysics. This problem involves forward modeling, subsurface sensing and object reconstruction. An analytical forward model - HSBA - in the frequency domain containing dyadic Green's function solution to describe the cross-well radar sensing in infinite soil media is developed. First order Born approximation is employed to linearize this inverse scattering problem. The forward model is validated by both frequency domain computational models and laboratory experiments that are collected using cross-well radar method that uses broadband antennas in subsurface to illuminate the inhomogeneous field and receive scattered electromagnetic (EM) waves. A shape-based inversion algorithm is proposed for contaminant pool localization and reconstruction, in which the shape of the object is assumed priori and represented by a low- order differentiable parametric function. The characteristics of the object - location, size and dielectric contrast to the background medium - are obtained by an iterative non-linear optimization. The inversion method using synthesized data by numerical experiments gives promising preliminary results.