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Geological process models typically simulate a range of dynamic processes to evolve a base topography into a final two‐dimensional cross section or three‐dimensional geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data. However, it is hard to find any process model realisations that fit all observations if data sets are complex and sparse in space or time because the simulations typically depend highly non‐linearly on base topography and dynamic parameters. As an alternative, geophysical probabilistic tomographic methods may be used to estimate the family of models of a target subsurface structure that are consistent both with information obtained from previous experiments and with new data (the Bayesian posterior probability distribution). However, this family seldom embodies geologically reasonable images. Here we show that the posterior distribution of tomographic images obtained from travel time data can be fully geological by injecting geological prior information into Bayesian inference and that we can do this near‐instantaneously by using trained mixture density networks (MDNs). We invoke two geological concepts as prior information about the possible depositional environment of an imaged target structure: a braided river system and a set of marine parasequences. Each concept is parameterised by the latent parameters of a generative adversarial network. Data from a target structure can then be used to infer the family of compatible latent parameter values using either geological concept using MDNs. Our near‐instantaneous MDN solutions closely resemble those found using relatively expensive Monte Carlo methods. We show that while the use of incorrect geological conceptual models provides significantly less accurate results, a classifier neural network can infer which geological conceptual model is most consistent with the data. It is thus demonstrated that even apparently barely related geophysical data may contain information about abstract geological concepts, and that geological conceptual models are key to creating reasonable images from geophysical data.

Introducing conceptual geological information into Bayesian tomographic imaging