Relevant Training Data for ML - Leveraging the Existing Image

Summary We propose a method to generate high-detail, pixel sharp velocity attributes through combined inversion of stacked seismic amplitude data and existing velocity models. The resulting models can be used to obtain highly realistic and geologically relevant synthetic data. The main use case presented is the modelling of training data for ML applications towards imaging and velocity model building workflows, with example applications.