A computer-implemented method for training a model that uses probabilities of lithologies as prior information in an inversion, the method being implemented in a computer system, the computer system including one or more physical computer processors and non-transient electronic storage that stores subsurface map data sets that correspond to different subsurface volumes of interest, wherein a given subsurface map data set corresponding to a given subsurface volume of interest specifies textural attribute values as a function of position within the given subsurface volume of interest, the method comprising: obtaining, from the electronic storage, training data, the training data including (i) subsurface map data sets specifying textural attribute values as a function of position corresponding to a subsurface volume of interest, and (ii) known lithologies as a function of position within the subsurface volume of interest, such that the textural attribute values of the lithologies are known as a function of position within the subsurface volume of interest; obtaining an initial seismic mapping machine learning model from the electronic storage; generating a conditioned seismic mapping machine learning model by training, on the one or more physical computer processors, the initial seismic mapping machine learning model using the training data; storing the conditioned seismic mapping machine learning model in the electronic storage; obtaining a target subsurface map data set for the subsurface volume of interest; applying the conditioned seismic mapping machine learning model to the target subsurface map data set to generate a classified lithology map data set which estimates probabilities of lithologies as a function of position within the subsurface volume of interest; applying an inversion to the classified lithology map data set, using the estimated probabilities of lithologies as prior information in the inversion, to generate volumes of lithologies being volumes of parameters for individual lithologies as a function of position within the subsurface volume of interest; generating an image that represents the volumes of lithologies using visual effects to depict at least a portion of the parameters in the volumes of lithologies; and displaying, on a graphical user interface, the image. obtaining, from the electronic storage, training data, the training data including (i) subsurface map data sets specifying textural attribute values as a function of position corresponding to a subsurface volume of interest, and (ii) known lithologies as a function of position within the subsurface volume of interest, such that the textural attribute values of the lithologies are known as a function of position within the subsurface volume of interest; obtaining an initial seismic mapping machine learning model from the electronic storage; generating a conditioned seismic mapping machine learning model by training, on the one or more physical computer processors, the initial seismic mapping machine learning model using the training data; storing the conditioned seismic mapping machine learning model in the electronic storage; obtaining a target subsurface map data set for the subsurface volume of interest; applying the conditioned seismic mapping machine learning model to the target subsurface map data set to generate a classified lithology map data set which estimates probabilities of lithologies as a function of position within the subsurface volume of interest; applying an inversion to the classified lithology map data set, using the estimated probabilities of lithologies as prior information in the inversion, to generate volumes of lithologies being volumes of parameters for individual lithologies as a function of position within the subsurface volume of interest; generating an image that represents the volumes of lithologies using visual effects to depict at least a portion of the parameters in the volumes of lithologies; and displaying, on a graphical user interface, the image.