A computer-implemented method of subsurface data uncertainty analysis, comprising: a. receiving , at a computer processor, subsurface data representative of a subsurface volume of interest and gathered from the field; b. identifying key controlling parameters for the subsurface volume of interest, wherein the key controlling parameters are parameters controlling the existence of a subsurface system of interest corresponding to the subsurface volume of interest; c. determining maximum possible ranges for each of the key controlling parameters; d. applying experimental design methods to generate an initial set of model input parameters corresponding to an initial set of scenarios wherein the initial set of scenarios lie within the maximum possible ranges; e. creating models for the subsurface volume of interest corresponding to each of the input parameter combinations as given by the experimental design using physics-based modeling; f. comparing , using multiple dimension scaling, MDS, to a 2D map, each of the models with the subsurface data representative of the subsurface volume of interest and adding models that are consistent with the subsurface data to an ensemble of digital analogs for the subsurface volume of interest; the method characterized by further comprising: g. checking whether the ensemble of the digital analogs contains enough models to be statistically significant and, if there are not enough models, repeating steps d - f until the ensemble of the digital analogs contains enough models to be statistically significant by identifying locations on the 2D map where additional models are needed and using inverse MDS to generate new model input parameters; h. developing statistical predictions for any specific property for the subsurface volume of interest using the ensemble of the digital analogs; and i. displaying, on a graphical interface, the statistical predictions. a. receiving , at a computer processor, subsurface data representative of a subsurface volume of interest and gathered from the field; b. identifying key controlling parameters for the subsurface volume of interest, wherein the key controlling parameters are parameters controlling the existence of a subsurface system of interest corresponding to the subsurface volume of interest; c. determining maximum possible ranges for each of the key controlling parameters; d. applying experimental design methods to generate an initial set of model input parameters corresponding to an initial set of scenarios wherein the initial set of scenarios lie within the maximum possible ranges; e. creating models for the subsurface volume of interest corresponding to each of the input parameter combinations as given by the experimental design using physics-based modeling; f. comparing , using multiple dimension scaling, MDS, to a 2D map, each of the models with the subsurface data representative of the subsurface volume of interest and adding models that are consistent with the subsurface data to an ensemble of digital analogs for the subsurface volume of interest; g. checking whether the ensemble of the digital analogs contains enough models to be statistically significant and, if there are not enough models, repeating steps d - f until the ensemble of the digital analogs contains enough models to be statistically significant by identifying locations on the 2D map where additional models are needed and using inverse MDS to generate new model input parameters; h. developing statistical predictions for any specific property for the subsurface volume of interest using the ensemble of the digital analogs; and i. displaying, on a graphical interface, the statistical predictions. <EOS>