探地雷达(GPR)信号反演的目的是提高资料解释的准确性。针对GPR 信号在反演解释的过程中传统反演方法面临的反演流程耗时和反演结果受专业人员主观经验影响较大的问题,文中提出了一种基于数据驱动的融合多尺度卷积和空间注意力机制的深度学习反演GPR 信号的神经网络GInet。首先通过B-Scan 数据维度变换单元实现对GPR 图像数据的压缩和特征提取,然后再经过多尺度UNet 单元实现从GPR 图像到对应地下介电常数分布图像的转换。实验采用基于时域有限差分法构建的仿真数据集进行网络训练和测试,GInet 反演结果的均方误差值降为了0. 005 54,结构相异性值为0. 000 026 7。使用砂槽实测数据对GInet 进行测试,反演得到的相对介电常数图像与真实模型接近,表明该方法可以准确高效地完成GPR 图像反演任务。

The purpose of ground penetrating radar signal inversion is to improve the accuracy of data interpretation. In the process of GPR signal inversion interpretation, the traditional inversion method faces the problems of time-consuming inversion process and the inversion results are greatly affected by the subjective experience of professionals. A data-driven deep learning inversion network, GInet, is proposed in this paper, which integrates multi-scale convolution and spatial attention mechanisms to invert GPR signals. Firstly, the B-Scan data dimension transformation unit is used to compress and extract features of GPR image data, and then the multi-scale UNet unit is used to convert the GPR image to the corresponding underground dielectric constant distribution image. The simulation data set built based on the finite-difference time domain method is used for network training and testing. The mean square error of GInet inversion results is reduced to 0. 005 54, and the structural dissimilarity value is 0. 000 026 7. The measured data of sand tank are used to test GInet, and the relative permittivity image obtained by inversion is close to the real model, indicating that this method can accurately and efficiently complete the GPR image inversion task.

TN959

国家自然科学基金资助项目(42074171)