A matched-filter-based reverse-time migration algorithm for ground-penetrating radar data

TLDR

Ground‑penetrating radar (GPR) remotely senses subsurface features by mapping data from object space to image space, but broad‑beam antennas spread reflected energy over a large lateral aperture, requiring migration algorithms to refocus scattering events to their true locations. The goal of this paper is to present a pair of finite‑difference time‑domain reverse‑time migration algorithms for GPR data processing. Linear inverse scattering theory is used to develop a matched‑filter response, and the resulting reverse‑time migration algorithms for both bistatic and monostatic configurations are implemented via finite‑difference time‑domain simulation in object space. Several examples demonstrate the performance of the proposed algorithms.

Abstract

Ground-penetrating radar (GPR) is a remote sensing technique used to obtain information on subsurface features from data collected over the surface. The process of collecting data may be viewed as mapping from the object space to an image space. Since most GPRs use broad beam width antennas, the energy reflected from a buried structure is recorded over a large lateral aperture in the image spare, migration algorithms are used to reconstruct an accurate scattering map by refocusing the recorded scattering events to their true spatial locations through a backpropagation process. The goal of this paper is to present a pair of finite-difference time-domain (FDTD) reverse-time migration algorithms for GPR data processing. Linear inverse scattering theory is used to develop a matched-filter response for the GPR problem. The reverse-time migration algorithms, developed for both bistatic and monostatic antenna configurations, are implemented via FDTD in the object space. Several examples are presented.