Machine learning inversion using bayesian inference and sampling

Abstract

Methods may include obtaining a preprocessed observed geophysical dataset based on an observed geophysical dataset of the subterranean region of interest and forming a training dataset composed of a geophysical training models and corresponding simulated geophysical training datasets. The methods may further include iteratively determining a simulated geophysical dataset from a current geophysical model, determining a data loss function between the preprocessed observed geophysical dataset and the simulated geophysical dataset, and training a machine learning (ML) network, using the training dataset, to predict a predicted geophysical model. The methods may still further include determining a model loss function between the current and predicted geophysical models and updating the current geophysical model based on an inversion using the data loss and model loss functions.