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Frequency–wavenumber modelling and migration of 2D GPR data in moderately heterogeneous dispersive media[Note 1. Received February 1997, revision accepted December 1997. ...]
Author(s) -
Bitri Adnand,
Grandjean Gilles
Publication year - 1998
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1046/j.1365-2478.1998.00091.x
Subject(s) - wavenumber , ground penetrating radar , extrapolation , attenuation , synthetic data , parametrization (atmospheric modeling) , wave propagation , radar , discretization , dispersion (optics) , computational physics , mathematical analysis , geology , algorithm , physics , mathematics , computer science , optics , telecommunications , radiative transfer
An algorithm for modelling and migrating ground penetrating radar (GPR) data in moderately heterogeneous dispersive media is presented. The method is based on wavefield extrapolation in the frequency–wavenumber ( f – k ) domain, from the solution of the 2D Maxwell's equations. The wavefield is extrapolated by a phase‐shift technique using a constant relative permittivity K and a quality factor Q . It is then modified by a correction term to handle the lateral K and Q variations. The spatial distribution of the K and Q ‐factor values, representing the given model parameters, is introduced into the algorithm by a regular grid parametrization. The radar wave dispersion and attenuation, induced by relaxation processes, are taken into account by a linear frequency‐dependent Q model, and expressed by a complex wavenumber in the propagation equation. A synthetic case and a field data set illustrate the potential of the method for frequencies of 300, 500 and 900 MHz. In the first case, a typical civil engineering problem is considered. The frequency dependence of the wave velocity and attenuation is well illustrated. The synthetic data are afterwards migrated using the initial model parameters. The results show the importance of using spatially varying model parameters in the migration processes. The second case concerns an application of the method to a real data set. In order to adjust the model parameters, a forward modelling sequence is performed until the best match between the measured and the synthetic data is achieved. A depth migration is then applied to the data, and the result is compared with the initial model parameters. In conclusion, we assess the contributions of the method to industrial applications, by discussing the performance of the algorithm compared with its limitations.