Well- and Structure-Constrained Initial Velocity Building for Full-Waveform Inversion via a Generative Diffusion Model

Full waveform inversion (FWI) plays an important role in velocity modeling due to its high-resolution advantages. However, its highly non-linear characteristic leads to numerous local minimums, which is known as the cycle-skipping problem. Therefore, effectively addressing the cycle-skipping issue is crucial to the success of FWI. Well-log data contain rich information about subsurface medium parameters, providing inherent advantages for velocity modeling. Traditional well-log data interpolation methods to build velocity models have limited accuracy and poor adaptability to complex geological structures. We propose a well interpolation algorithm based on a generative diffusion model (GDM) to create initial models for FWI, trying to address the cycle-skipping problem. By integrating well-log data, migration images to encode physics-based geological priors in the velocity-model building process, our approach can provide much more detailed information of the faults and stratigraphic features. Numerical experiments demonstrate that the method produces accurate and reliable initial models. Compared to the conventional purely data-driven methods, our approach significantly enhances FWI performance and effectively mitigates the cycle-skipping issues.