ROBUST FULL WAVEFORM INVERSION: A SOURCE WAVELET MANIPULATION PERSPECTIVE

Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface parameter reconstruction. Due to the existence of local minimum traps, the success of the inversion process usually requires a good initial model. Our study primarily focuses on understanding the impact of source wavelets on the landscape of the corresponding optimization problem. We thus introduce a decomposition scheme that divides the inverse problem into two parts. The first step transforms the measured data into data associated with the desired source wavelet. Here, we consider inversions with known and unknown sources to mimic real scenarios. The second subproblem is the conventional FWI, which is much less dependent on an accurate initial model since the previous step improves the misfit landscape. A regularized deconvolution method and a convolutional neural network are employed to solve the source transformation problem. Numerical experiments on the benchmark models demonstrate that our approach improves the gradient's quality in the subsequent FWI and provides a better inversion performance.

Automated summary

Full waveform inversion is a powerful tool for high-resolution subsurface parameter reconstruction, but it usually requires a good initial model. This study focuses on the impact of source wavelets on the optimization problem and proposes a decomposition scheme. Numerical experiments show that our approach improves the gradient quality in subsequent FWI and provides better inversion performance.