A computer-implemented method (200; 400) for refining estimated parameter values within subsurface map data sets, the method (200; 400) being implemented in a computer system, the computer system including one or more physical computer processors and non-transitory 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 estimated parameter values of different parameters as a function of position within the given subsurface volume of interest, the method (200; 400) comprising: obtaining (210; 402) an initial target subsurface map data set; obtaining (208; 404), from the electronic storage, a conditioned seismic mapping model, the conditioned seismic mapping model having been conditioned by training an initial seismic mapping model using training data, wherein the initial seismic mapping model is a machine learning model and wherein the training data includes (i) unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, and (ii) refined subsurface map data sets specifying refined estimations of parameter values of the first parameter as a function of position within the corresponding subsurface volumes of interest; and applying (212; 406) the conditioned seismic mapping model to the initial target subsurface map data set to generate a refined target subsurface map data set, wherein: the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values that are filtered to have a lower sampling density and the refined subsurface map data sets include corresponding parameter values that have a higher sampling density than the filtered parameter values, and a sampling density of parameter values within the subsurface volume of interest is higher for the refined target subsurface map data set than for the initial target subsurface map data set; and/or the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values with noise introduced and the refined subsurface map data sets include corresponding noise-free parameter values, and the parameter values in the refined target subsurface map data set have been denoised with respect to parameter values in the initial target subsurface map data set. obtaining (210; 402) an initial target subsurface map data set; obtaining (208; 404), from the electronic storage, a conditioned seismic mapping model, the conditioned seismic mapping model having been conditioned by training an initial seismic mapping model using training data, wherein the initial seismic mapping model is a machine learning model and wherein the training data includes (i) unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, and (ii) refined subsurface map data sets specifying refined estimations of parameter values of the first parameter as a function of position within the corresponding subsurface volumes of interest; and applying (212; 406) the conditioned seismic mapping model to the initial target subsurface map data set to generate a refined target subsurface map data set, wherein: the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values that are filtered to have a lower sampling density and the refined subsurface map data sets include corresponding parameter values that have a higher sampling density than the filtered parameter values, and a sampling density of parameter values within the subsurface volume of interest is higher for the refined target subsurface map data set than for the initial target subsurface map data set; and/or the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values with noise introduced and the refined subsurface map data sets include corresponding noise-free parameter values, and the parameter values in the refined target subsurface map data set have been denoised with respect to parameter values in the initial target subsurface map data set. the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values that are filtered to have a lower sampling density and the refined subsurface map data sets include corresponding parameter values that have a higher sampling density than the filtered parameter values, and a sampling density of parameter values within the subsurface volume of interest is higher for the refined target subsurface map data set than for the initial target subsurface map data set; and/or the training data comprises data pairs of subsurface map data, wherein the unrefined subsurface map data sets include parameter values with noise introduced and the refined subsurface map data sets include corresponding noise-free parameter values, and the parameter values in the refined target subsurface map data set have been denoised with respect to parameter values in the initial target subsurface map data set.