Controllable seismic velocity synthesis using generative diffusion models

Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards. Machine learning-based inversion algorithms have shown promising performance in regional (i.e., for exploration) and global velocity estimation, while their effectiveness hinges on access to large and diverse training datasets whose distributions generally cover the target solutions. Additionally, enhancing the precision and reliability of velocity estimation also requires incorporating prior information, e.g., geological classes, well logs, and subsurface structures, but current statistical or …

algorithms cs.lg datasets diffusion diffusion models diverse earth estimations exploration generative global hazards machine machine learning natural performance physics.geo-ph regional resources synthesis training understanding vital