A Markov chain Monte Carlo approach based on CNN-LSTM for cross-hole GPR full waveform inversion

Abstract This research introduces a new method for performing the full waveform inversion on cross-hole ground-penetrating radar (GPR) data. This method relies on a Markov chain Monte Carlo (MCMC) technique that integrates a CNN-LSTM network. The cross-hole GPR technique is widely used in geophysical prospecting for its ability to obtain high-resolution subsurface information. However, traditional full waveform inversion methods for GPR data often face challenges such as high computational cost and being trapped in local minima. The CNN-LSTM network is capable of capturing spatial and temporal features, which helps to boost the inversion process’s effectiveness. By integrating the CNN-LSTM network into the MCMC method, this proposed approach aims to overcome these limitations. The CNN part of the network can effectively extract spatial features from GPR data, while the LSTM part can capture the temporal dependencies, providing a more in-depth comprehension of the data characteristics. This understanding is then utilized by the MCMC method to sample more efficiently from the model parameters’ posterior distribution. Numerical experiments demonstrate that compared with traditional methods, the CNN-LSTM network has smaller root mean square errors and can effectively perform the full waveform inversion. This study highlights the effectiveness of combining CNN-LSTM networks with MCMC methods for complex data inversion tasks.