Abstract: A method for producing a synthetic model for training a backpropagation-enabled process for identifying subsurface features, includes generating synthetic subsurface models with realizations of subsurface features. The synthetic subsurface models are generated by introducing at least three distinct model variations selected from geologically realistic features simulating the outcome of a geologic process, simulations of geologic processes, simulations of noise sources, and combinations thereof. Labels are applied to one or more of the subsurface features in one or more of the synthetic subsurface models. The labels and the corresponding synthetic subsurface models are imported into the backpropagation-enabled process for training.

Abstract: A method for producing a synthetic model for training a backpropagation-enabled process for identifying subsurface features, includes generating noise-free synthetic subsurface models with realizations of subsurface features. The noise-free synthetic subsurface models are generated by introducing a model variation selected from geologically realistic features simulating the outcome of a geologic process, simulations of geologic processes, and combinations thereof. Labels are applied to one or more of the subsurface features in one or more of the synthetic subsurface models. A simulation of a noise source is applied to a copy of one or more of the noise-free synthetic subsurface models to produce a noise-augmented copy. The labels and the corresponding synthetic subsurface models are imported into the backpropagation-enabled process for training.