Automatic velocity model building with machine learning

Velocity model building for seismic imaging is commonly performed with tomography and full wave inversion (FWI). Both techniques are time consuming and need significant human intervention. Machine learning has been introduced into seismic imaging with the goal of reproduce the success earned in other fields. Due to the complexity of the earth, and the geological uniqueness of any one location, determining the appropriate training data can be challenging. Directly building a 3D velocity model by machine learning still has some way to go. Instead of letting machine learning do all the work, it may be more practical to only perform machine learning on the portion of model building that requires heavy human intervention. In this paper, we present a method that builds the velocity model automatically by combining novel machine learning with the mature velocity model building techniques.