Generating Reliable Initial Velocity Models for Full-waveform Inversion with Well and Structural Constraints

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. This study introduces a well interpolation algorithm based on a generative diffusion model (GDM) to generate initial models for FWI, addressing the cycle-skipping problem. Existing convolutional neural network (CNN)-based methods face difficulties in handling complex feature distributions and lack effective uncertainty quantification, limiting the reliability of their outputs. The proposed GDM-based approach overcomes these challenges by providing geologically consistent well interpolation while incorporating uncertainty assessment. Numerical experiments demonstrate that the method produces accurate and reliable initial models, enhancing FWI performance and mitigating cycle-skipping issues.