Spatiotemporal Optimization of GPR Full Waveform Inversion Based on Super-Resolution Technology

Theoretical advancements in full waveform inversion (FWI) of ground-penetrating radar (GPR) data have shown promising potential for enhancing the accuracy of GPR data interpretation. However, the widespread implementation of FWI faces significant challenges due to its low-computational efficiency and high memory consumption, primarily attributed to the gradient operation stage. To address these issues, we propose a spatiotemporal optimization approach for GPR FWI based on super-resolution (SR) technology. The proposed method focuses on three optimization directions: adopting a storage strategy that only preserves the forward wavefield while synchronizing the gradient operation and adjoint wavefield operation, compressing the time dimension of the GPR wavefield based on the Nyquist sampling law, and obtaining a fuzzy gradient in the spatial dimension by sampling the wavefield at each moment and restoring it using an SR network to complete the FWI. Experimental results demonstrate that the proposed optimization method achieves a nearly 50% acceleration in computational efficiency without compromising the original inversion architecture. Moreover, it reduces the memory usage to approximately 4.17% of the original memory, while maintaining the effectiveness of the inversion process. This method exhibits practicality and effectiveness through several numerical and measured data experiments, providing a solid foundation for the widespread application of FWI on commonly available microcomputers.