A method comprising: ascribing one of a plurality of predetermined sub-classes to each pixel of a plurality of pixels of an image of an unknown rock sample ("unknown rock sample image"), the unknown rock samples comprising drill cuttings retrieved from a geological formation, wherein: the ascription utilizes a deep learning model that is trained with an annotated training dataset; and the annotated training dataset comprises: a plurality of multi-pixel images of known rock samples, ("known rock sample images"); the known rock samples comprising drill cuttings; and for each known rock sample image, sub-classes corresponding to at least a subset of pixels of that image; for each pixel of the unknown rock sample image having an ascribed sub-class, deriving which one of a plurality of predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel, wherein the meta-classes are each representative of a different one of a plurality of predetermined rock types; and predicting at least one property of the geological formation utilizing the ascription-derived meta-classes, wherein predicting the at least one property includes predicting which one or more of the rock types are present in the geologic formation and predicting a quantity of each of the one or more rock types predicted to be present in the geologic formation. ascribing one of a plurality of predetermined sub-classes to each pixel of a plurality of pixels of an image of an unknown rock sample ("unknown rock sample image"), the unknown rock samples comprising drill cuttings retrieved from a geological formation, wherein: the ascription utilizes a deep learning model that is trained with an annotated training dataset; and the annotated training dataset comprises: a plurality of multi-pixel images of known rock samples, ("known rock sample images"); the known rock samples comprising drill cuttings; and for each known rock sample image, sub-classes corresponding to at least a subset of pixels of that image; the ascription utilizes a deep learning model that is trained with an annotated training dataset; and the annotated training dataset comprises: a plurality of multi-pixel images of known rock samples, ("known rock sample images"); the known rock samples comprising drill cuttings; and for each known rock sample image, sub-classes corresponding to at least a subset of pixels of that image; a plurality of multi-pixel images of known rock samples, ("known rock sample images"); the known rock samples comprising drill cuttings; and for each known rock sample image, sub-classes corresponding to at least a subset of pixels of that image; for each pixel of the unknown rock sample image having an ascribed sub-class, deriving which one of a plurality of predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel, wherein the meta-classes are each representative of a different one of a plurality of predetermined rock types; and predicting at least one property of the geological formation utilizing the ascription-derived meta-classes, wherein predicting the at least one property includes predicting which one or more of the rock types are present in the geologic formation and predicting a quantity of each of the one or more rock types predicted to be present in the geologic formation. <EOS>