GPU Accelerated 3D P-wave Source Free Adaptive Wavefield Reconstruction Inversion with an application to experimental VSP physical modeling data

TL;DR

This paper presents a GPU-accelerated 3D wavefield reconstruction inversion method that significantly improves computational efficiency and robustness for realistic seismic data applications, including VSP physical modeling.

Contribution

The study introduces a novel GPU-accelerated 3D source-free adaptive WRI method with adaptive accuracy control and on-the-fly source estimation, enabling practical large-scale seismic inversions.

Findings

Achieved 195-fold speedup over CPU-based methods

Maintained inversion accuracy with adaptive accuracy and regularization

Successfully applied to real VSP data, demonstrating practical effectiveness

Abstract

Wavefield reconstruction inversion (WRI) has been considered a potential solution to the issue of local minima inherent in conventional full waveform inversion (FWI) methods. However, most current WRI research has been confined to 2D problems due to the computational challenges posed by solving augmented systems for optimal data-fitting wavefields. This constraint limits WRI applicability to realistic 3D scenarios. This study introduces a GPU-accelerated 3D source-free adaptive WRI (GPU-SF-AWRI) method that overcomes these computational barriers by adaptively controlling the computational accuracy of wavefield simulation and optimizing GPU utilization, thus enhancing its suitability for 3D applications. The inclusion of an on-the-fly source estimation technique further boosts its performance on realistic problems. Numerical experiments reveal that the proposed GPU-accelerated method achieves a 195-fold speedup compared to CPU-based approaches. By incorporating adaptive accuracy and total variation regularization, we attain a 2-fold speedup while maintaining inversion accuracy. We applied the GPU-SF-AWRI method to numerical and actual Vertical Seismic Profiling (VSP) physical modeling P-wave data, confirming its efficacy in addressing real data challenges and mitigating local minima associated with conventional FWI.