Traditional　optimization　algorithms　are　usually　applied　to　estimate　the　elastic　parameters　of　the　subsurface　by　using　field　seismic　data.　However,　these　optimization　algorithms　highly　depend　on　prior　knowledge　(e.g.,　the　initial　model　setup　and　sparsity),　leading　to　serious　inversion　uncertainties.　Nowadays,　with　the　rapid　development　of　neural　networks,　convolutional　neural　networks　(CNNs)　have　been　widely　imposed　on　estimating　elastic　parameters　from　field　data.　However,　the　deficiency　of　labeled　seismic　data　impedes　the　CNNs　application　in　seismic　inversion.　Moreover,　both　the　size　and　diversity　of　labeled　datasets　are　also　critical　factors　influencing　the　accuracy　and　resolution　of　predicted　parameters　when　using　the　CNNs-based　inversion　techniques.　In　this　work,　taking　the　unconventional　tight　sandstone　formation　as　an　example,　we　develop　a　geological　and　geophysical　model　driven　CNNs　(GGCNNs),　named　as　GGCNNs.　The　proposed　GGCNNs　allow　us　to　take　advantage　of　both　the　prior　geological　information　and　basic　geophysical　model　from　the　generated　synthetic　labeled　prestack　seismic　datasets,　representing　essential　characteristics　of　the　subsurface.　Moreover,　under　the　consideration　of　data　diversity,　the　GGCNNs　model　enables　us　to　make　a　tradeoff　between　the　inversion　accuracy　and　labeled　data　size.　Applications　on　both　synthetic　and　field　data　clearly　demonstrate　the　effectiveness　of　the　proposed　GGCNNs　model　for　predicting　elastic　parameters　by　using　prestack　seismic　data,　i.e.,　its　predicted　results　are　with　high　accuracy　in　the　vertical　profile　and　continuity　and　smooth　in　the　horizon　slice.　©　1980-2012　IEEE.