Ground Penetrating Radar (GPR) is a geophysical method developed for subsurface imaging. The main components of a GPR system are the antenna, the control unit, and the encoder. The GPR system moves along the area of interest and a set of data, called radargram, is obtained. In a single-short offset GPR acquisition, the distance between the transmitter and receiver antennas is very short. The collected data over an area can be used to construct images of the dielectric permittivity and the electric conductivity of the subsurface, which allows identifying objects such as anti-personnel mines, pipes, people in an earthquake, fossils, among others. Full waveform inversion (FWI) of GPR data can be used to estimate such electromagnetic parameters based on the minimization of a misfit function. However, FWI of GPR data has several problems: cross-talk between parameters, the estimation of the amplitude and signature of the electromagnetic source, the presence of noise in the data, the non-uniqueness of the solution and the high computational cost. In this paper, an alternative cost function for FWI is proposed to mitigate the problem of the estimation of the amplitude of the electromagnetic source. We show that the proposed alternative cost function for FWI is more robust than traditional norm in noisy environments. The proposed cost function is tested over a synthetic model with complex structures called SEAM and over collected data in a region of Colombia. In order to deal with the high computational complexity of FWI, all the experiments are computed in a cluster of GPUs.
Ground Penetrating Radar is a geophysical method based on the propagation of electromagnetic waves for the prospecting of subsurface layers. The characterization of the subsurface from data obtained at the surface is an inverse process. Full Waveform Inversion (FWI) is an iterative method that requires: a cost function to measure the misfit between observed and modeled data; a wave propagator to compute the modeled data; and an initial parameters model that is updated, at each iteration step, until reaches an acceptable decrement of the cost function. FWI has a high computational cost because use the electromagnetic wave equation is discretized using Finite Difference in Time Domain. Although shielded antenna can be configured in multi-channel mode to allow large wavenumbers, this increases the budgets of the acquisitions, the processing time and the human resource in the data collection in the field. Therefore, in this paper a methodology to obtain a simultaneous inversion of permittivity, permeability, and conductivity from 2D GPR data using FWI in time domain to short-offset is proposed. The proposed methodology takes advantage of Graphical Processing Units (GPUs), through the programming language CUDA-C developed by NVIDIA, to reduce the execution time. The FWI reaches the best result (measured as the lowest cycle skipping values % CS) when there is no conductivity in the numerical experiments. This paper shows that the conductivity effect has a bigger negative impact during the inversion process than the Full Waveform inversion of noisy data.
Full waveform inversion of Ground Penetrating Radar (GPR) data is a promising strategy to estimate quantitative characteristics of the subsurface such as permittivity and conductivity. In this paper, we propose a methodology that uses Full Waveform Inversion (FWI) in time domain of 2D GPR data to obtain highly resolved images of the permittivity and conductivity parameters of the subsurface. FWI is an iterative method that requires a cost function to measure the misfit between observed and modeled data, a wave propagator to compute the modeled data and an initial velocity model that is updated at each iteration until an acceptable decrease of the cost function is reached. The use of FWI with GPR are expensive computationally because it is based on the computation of the electromagnetic full wave propagation. Also, the commercially available acquisition systems use only one transmitter and one receiver antenna at zero offset, requiring a large number of shots to scan a single line.