Abstract: Systems and methods for training a model that uses probabilities of lithologies as prior information in an inversion are disclosed. Exemplary implementations may: obtain training data, the training data including (i) subsurface map data sets, and (ii) known lithologies; obtain an initial seismic mapping model; generate a conditioned seismic mapping model by training the initial seismic mapping model; store the conditioned seismic mapping model; obtain a target subsurface map data set; apply the conditioned seismic mapping model to generate a classified lithology map data set; apply an inversion to the classified lithology map data set to generate volumes of lithologies; generate an image that represents the volumes of lithologies; display the image.

Abstract: Systems and methods for training a model that uses probabilities of lithologies as prior information in an inversion are disclosed. Exemplary implementations may: obtain training data, the training data including (i) subsurface map data sets, and (ii) known lithologies; obtain an initial seismic mapping model; generate a conditioned seismic mapping model by training the initial seismic mapping model; store the conditioned seismic mapping model; obtain a target subsurface map data set; apply the conditioned seismic mapping model to generate a classified lithology map data set; apply an inversion to the classified lithology map data set to generate volumes of lithologies; generate an image that represents the volumes of lithologies; display the image.