Abstract
Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area.
Highlights
Crosshole ground-penetrating radar is widely used in geoscientific and engineering investigations [1,2]
The paper is organized as follows: Firstly, we introduce the formula of the probabilistic inversion method and the forward method based on the waveform
For time-lapse inversion, two inversions are required; we mainly focus on the change in the target area
Summary
Crosshole ground-penetrating radar is widely used in geoscientific and engineering investigations (e.g., identify cracks, analysis moisture) [1,2]. These two inversions cost much calculation time, and at the second inversion, the non-target region will affect the accuracy of target region To solve this problem, we use the Monte Carlo inversion to achieve local sampling. We use the Monte Carlo inversion to achieve local sampling In this way, the reduction of the sampling area eliminates the impact of the non-target area and improves computational efficiency. We use MCMC method combined with double difference strategy to realize time-lapse GPR inversion. Taking the advantage of a priori information in the MCMC method, we achieve local sampling in time-lapse inversion. In this way, we can only sample the target area which eliminates the impact of the non-target area and reduce the calculations. We present an analysis of results from synthetic data using local compared with full sampling
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