Tomographic GPR imaging using a linear inversion algorithm informed by FDTD modelling: a numerical case study of buried utility pipes monitoring. Issue 2 (1st August 2012)

• Record Type: Journal Article
• Authors: Millington, T.M.; Cassidy, N.J.; Crocco, L.; Soldovieri, F.
• Is Part Of: Near surface geophysics. Volume 11:Issue 2(2013)
• Page Start: 221
• Page End: 230
• Publication Date: 2012-08-01
• Subjects: Earth (Planet) -- Surface -- Periodicals; Geophysics -- Technique -- Periodicals; Engineering geology -- Periodicals; Geophysics -- Periodicals; Planets -- Surfaces; Engineering geology; Geophysics -- Technique; Geophysics; Earth (Planet); Periodicals; 550
• DOI: 10.3997/1873-0604.2012037
• Languages: English
• ISSNs: 1569-4445

Abstract

ABSTRACT: In this paper, we present the results of an on‐going study into the development of a practical strategy for the interpretation and analysis of near‐surface GPR data in complex scenarios. In particular, we consider the problem of how the knowledge of the investigated scenario can be exploited to improve the diagnostic results using an approach based on the joint use of FDTD numerical modelling and linear tomographic inversion methods. The performance of the approach is evaluated using a simulated test‐case in which GPR data are collected in a complex utility‐pipe model. Prior knowledge of the investigation scenario is captured for the inversion using a three‐dimensional, full‐field FDTD modelling scheme to calculate the incident field and the Green's functions, allowing the antenna geometry, the air‐ground interface and known subsurface a priori information to be accounted for. As input data to the inversion algorithm, we assume the raw GPR data preprocessed only by simple time‐gating, after Truncated Singular Value Decomposition (TSVD) resulting from the 'informed linear' model, are exploited to achieve a regularized solution of the problem. The results show that with just this basic assumption, the joint use of these two (forward and inverse) modelling techniques enhances tomographic imaging in very complex scenarios.