Controllable seismic velocity synthesis using generative diffusion
  models
Fu Wang Xinquan Huang Tariq Alkhalifah
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 neural
network-based methods are not flexible enough to handle such multi-modal
information. To address both challenges, we propose to use conditional
generative diffusion models for seismic velocity synthesis, in which we readily
incorporate those priors. This approach enables the generation of seismic
velocities that closely match the expected target distribution, offering
datasets informed by both expert knowledge and measured data to support
training for data-driven geophysical methods. We demonstrate the flexibility
and effectiveness of our method through training diffusion models on the
OpenFWI dataset under various conditions, including class labels, well logs,
reflectivity images, as well as the combination of these priors. The
performance of the approach under out-of-distribution conditions further
underscores its generalization ability, showcasing its potential to provide
tailored priors for velocity inverse problems and create specific training
datasets for machine learning-based geophysical applications.