1D waveform inversion of GPR trace by particle swarm optimization


KAPLANVURAL İ. , PEKŞEN E. , Ozkap K.

JOURNAL OF APPLIED GEOPHYSICS, vol.181, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 181
  • Publication Date: 2020
  • Doi Number: 10.1016/j.jappgeo.2020.104157
  • Title of Journal : JOURNAL OF APPLIED GEOPHYSICS
  • Keywords: Particle Swarm Optimization, Ground-penetrating Radar, Inversion, Non-destructive Evaluation, Pipes, JOINT INVERSION, RESISTIVITY, CONVERGENCE, ALGORITHM, PSO

Abstract

This study investigates the identification of buried materials from ground-penetrating radar (GPR) signals by applying Particle Swarm Optimization (PSO) method for the inversion of individual traces. Interpretation of GPR data can be carried out either by applying some basic processing steps to raw data or by inverting GPR data. However, for obtaining more specific information about the buried object, some more advanced algorithms are required. In this study, the PSO, which has been used to solve many engineering problems, is utilized to invert the GPR traces for detecting various types of buried plastic pipe fillings. Firstly, we confirmed the applicability of PSO to GPR traces using numerical modeling. Then, the data acquired over buried pipe filling materials (air, water ice) were evaluated experimentally by the proposed method. Determination of the filling material is one of the most popular problems of shallow pipe exploration by GPR. The inversion process resulted in good data fit between observed and calculated traces for air-filled, water-filled, and ice-filled pipe. The estimated dielectric properties including relative dielectric permittivity, conductivity, and relative magnetic permeability values were in the range of the corresponding parameter values in the literature at the end of inversion for each model. These results show that the PSO method applied to GPR data can distinguish plastic pipe contents. Moreover, the results suggest that PSO can be used for the quantitative interpretation of GPR data. (C) 2020 Elsevier B.V. All rights reserved.