Conditional stochastic inversion of common-offset ground-penetrating radar reflection data

Abstract

We present a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile, or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that the proposed method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise

Conditional stochastic inversion of common-offset ground-penetrating radar reflection data

“…To this end, we consider the impedance-type inversion of surface-based common-offset GPR reflection data from a stochastic perspective, whereby we seek to match observed radargrams to spatial distributions of subsurface properties that honor, a priori, pertinent in situ information derived from borehole-type measurements as well as prescribed geostatistical constraints. This is done via conditional geostatistical simulation within a stochastic optimization procedure, the repeated application of which allows for the generation of multiple acceptable models in order to explore and quantify uncertainty (Xu et al, 2020b).…”