Search

Geological process models simulate a range of dynamic processes to evolve a base topography into a final 2D cross-section or 3D geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data; however, many realisations of process models that embody different conceptual models may provide similar consistency with observed data, and finding all such realisations may be infeasible due to the computational demands of the task. Alternatively, 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 distribution). However, this family seldom embodies geologically reasonable images. We show that the posterior distribution of tomographic images can be enhanced by injecting geological prior information into Bayesian inference, and we can do this near-instantaneously by trained MDNs. We invoke two geological concepts as geological prior information: a braided river system, and a set of marine parasequences, each parameterised by a GAN. Data from a target structure can then be used to infer the image parameter values using either geological concept using MDNs. Our MDN solutions closely resemble those found using expensive McMC methods, and while the use of incorrect geological conceptual models provides less accurate results the mean structures still approximate the target. We then show that a classification network can infer the correct geological conceptual model. Thus, imposing even incorrect geological prior information may improve geophysical tomographic images compared to those obtained without geological prior information, and in principle geological conceptual models can be inferred directly from geophysical travel time data.