A computer-implemented method comprising: receiving labeled measured seismic data for one or more real geologic regions that comprise features, wherein the labeled seismic data includes labels for said features of the one or more real geologic regions and the received data further includes acquisition data for the one or more geologic regions, processing data for the one or more geologic regions and geology data for the one or more geologic regions; selecting an algorithm related to the generation of synthetic seismic data; determine and output , based on the received data including the acquisition data for the one or more geologic regions, the processing data for the one or more geologic regions and geology data for the one or more geologic regions, ranges of acquisition parameters, ranges of processing parameters and ranges of geology parameters; generating labeled synthetic seismic data for a set of structural features, the set of structural features including structural features of the one or more real geologic regions, based at least in part on the algorithm and based at least in part on at least a portion of the labeled measured seismic data , wherein the labeled synthetic seismic data are generated for the ranges of acquisition parameters, the ranges of processing parameters and the ranges of geology parameters; training a deep learning framework to recognize structural features, and/or to classify structural features and/or to more accurately determine that some type of structural feature exists that is not recognized in the seismic data, based at least in part on the labeled synthetic seismic data to generate a trained deep learning framework ; receiving acquired seismic data for a geologic environment (1734; implementing the trained deep learning framework to generate interpretation results for the acquired seismic data; and outputting the interpretation results . receiving labeled measured seismic data for one or more real geologic regions that comprise features, wherein the labeled seismic data includes labels for said features of the one or more real geologic regions and the received data further includes acquisition data for the one or more geologic regions, processing data for the one or more geologic regions and geology data for the one or more geologic regions; selecting an algorithm related to the generation of synthetic seismic data; determine and output , based on the received data including the acquisition data for the one or more geologic regions, the processing data for the one or more geologic regions and geology data for the one or more geologic regions, ranges of acquisition parameters, ranges of processing parameters and ranges of geology parameters; generating labeled synthetic seismic data for a set of structural features, the set of structural features including structural features of the one or more real geologic regions, based at least in part on the algorithm and based at least in part on at least a portion of the labeled measured seismic data , wherein the labeled synthetic seismic data are generated for the ranges of acquisition parameters, the ranges of processing parameters and the ranges of geology parameters; training a deep learning framework to recognize structural features, and/or to classify structural features and/or to more accurately determine that some type of structural feature exists that is not recognized in the seismic data, based at least in part on the labeled synthetic seismic data to generate a trained deep learning framework ; receiving acquired seismic data for a geologic environment (1734; implementing the trained deep learning framework to generate interpretation results for the acquired seismic data; and outputting the interpretation results . <EOS>