###### Abstract  
Monte Carlo-based sampling methods (MCMC) can be used to solve inverse problems affecting ground penetrating radar (GPR) data. However, due to their high computational complexity, they have not been widely used in practical applications. This article uses neural network methods to replace the computationally complex forward problem of Monte Carlo methods. However, the neural network method is an approximation of the accurate formula method, and this may introduce model errors. In order to reduce the impact of model errors, in this study, we incorporate the Squeeze-and-Excitation (SE) attention mechanism into Convolutional Neural Networks (CNN) to further improve the accuracy of the network. Moreover, with the statistical advantages of the MCMC method, model errors can be explained during the inversion process, further reducing their impact. We apply the proposed method to solve the inversion problem of crosshole ground-penetrating radar travel time data. Compared with commonly used approximate forward models, the method proposed in this paper has better accuracy. The results of data experiments indicate that this method can effectively invert the velocity of underground media. c 2024 14 618 11 1  
[PERSON] et al. [9] introduced the extended Metropolis algorithm to sample posterior probability density; this algorithm can sample and solve nonlinear inversion problems. [PERSON] et al. applied the MCMC algorithm to the inversion of ground-penetrating radar data and conducted extensive research, including analyzing and interpreting the errors of the ray model, reducing the impact of model errors on the inversion results [10; 11; 12]. In recent years, with the rapid improvement of computer performance, methods for inverting ground-penetrating radar signals through deep learning have developed rapidly. CNNs are widely used in the image field due to their good performance. [PERSON] et al. [13] designed a self-coding convolutional neural network based on an end-to-end self-coding CNN structure to invert ground-penetrating radar data into an electromagnetic wave velocity model. [PERSON] et al. [14] designed a PINet inversion network using convolutional and fully connected algorithms to achieve tunnel detection. [PERSON] et al. designed an adaptive linear meta neural network for ground-penetrating-radar signal inversion [15]. [PERSON] et al. proposed GPRInvNet based on the DNN architecture in 2019 to map ground-penetrating-radar B-Scan data to the dielectric permittivity map of underground structures, achieving good inversion of complex tunnel lining structures underground [16]. [PERSON] applied the BP neural network to the inversion of ground-penetrating radar travel time data, attaining efficient and accurate inversion results [17]. [PERSON] et al. improved U-Net based on FCN in 2019 to establish a velocity model from seismic record data [18]. [PERSON] et al. [19] designed a GPR dimensional transform autoencoder to extract time-domain features of GPR data and then designed GPR-EInet based on a CNN to invert the time-frequency fusion data pertaining to ground-penetrating radar into an underground dielectric permittivity structure. [PERSON] et al. [20] designed a cascaded CNN network to extract features from ground-penetrating-radar B-Scan signals, first removing direct waves and then extracting feature maps using a CNN. The SE attention module (Squeeze-and-Excitation attention module) is a module used to improve the performance of convolutional neural networks by dynamically adjusting the weights of input feature maps. The core idea of the SE attention module is to recalibrate the features of each channel, enabling the model to focus more on features with important information [21]. One advantage of the SE attention module is that it can adaptively learn the dependencies between different channels, thereby improving the performance of a model.