用CUDA与PyTorch混合编译加速雷达反演，同时求解介电常数和电导率。
Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch
- 将自定义CUDA核函数融入PyTorch自动微分，兼顾速度与灵活性。
- 在合成与实测数据上实现高精度双参数反演，收敛速度快。
- 支持变分正则化与多尺度反演，适合工程与地质探测应用。
本文提出一种高性能的地面穿透雷达（GPR）双参数全波形反演（FWI）框架，通过混合编译CUDA内核函数与PyTorch实现加速。该方法结合GPU编程的计算效率与基于Python的深度学习框架灵活性，将定制化CUDA内核集成至PyTorch的自动微分机制中，实现对介电常数与电导率的精确高效反演。在合成数据与实测波场数据上的实验表明，所提方法能有效完成GPR双参数FWI，保持高精度。此外，该框架具备良好的灵活性与可扩展性，支持总变差正则化、多尺度反演等可选策略。这些特性使其成为土木工程、环境监测与地球物理勘探中快速地下成像的实用且可扩展的解决方案。
原文摘要 · Abstract (English)
This study proposes a high-performance dual-parameter full waveform inversion framework (FWI) for ground-penetrating radar (GPR), accelerated through the hybrid compilation of CUDA kernel functions and PyTorch. The method leverages the computational efficiency of GPU programming while preserving the flexibility and usability of Python-based deep learning frameworks. By integrating customized CUDA kernels into PyTorch's automatic differentiation mechanism, the framework enables accurate and efficient inversion of both dielectric permittivity and electrical conductivity. Experimental evaluations on synthetic data and real wavefield data demonstrate that the proposed method achieves dual-parameter FWI for GPR data while maintaining high accuracy. Moreover, the framework is flexible and extensible, supporting optional regularization strategies such as total variation and multi-scale inversion. These features make the proposed approach a practical and scalable framework for rapid GPR-based subsurface imaging in applications including civil engineering, environmental monitoring, and geophysical exploration.
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