Delineating　seismic　faults　is　one　of　the　main　steps　in　seismic　structure　interpretation.　Recently,　deep　learning　(DL)　models　are　used　to　automatic　seismic　fault　interpretation.　For　the　DL-based　models,　there　are　two　widely　used　techniques,　which　can　enhance　the　model　performance,　that　is,　data　augmentation　(DA)　and　ensemble　learning　(EL).　Qualitatively　and　quantificationally　analyzing　the　performances　of　these　two　techniques　is　a　rarely　studied　domain.　In　this　study,　we　make　detailed　comparisons　between　the　DL　models　using　DA　and　EL.　For　the　DL　model　with　DA,　we　first　build　a　holistically　nested　Unet　(HUnet)　model　by　adopting　the　holistically　nested　module　to　the　widely　used　Unet　model.　Then,　we　train　a　HUnet　model　by　using　the　original　and　its　augmented　synthetic　datasets　(HUnet-D　model　for　short).　Besides,　we　train　a　Unet　model　in　the　same　way　as　a　comparison　(Unet-D　model　for　short).　On　the　other　hand,　for　the　DL　model　with　EL,　we　first　obtain　several　individual　HUnet　models　separately　trained　by　only　using　a　type　of　the　augmented　datasets　for　each　time.　Next,　we　propose　a　data-driven　EL　model　to　integrate　these　HUnet　models.　Specially,　we　propose　an　adjoint-net　module　for　the　EL　model　to　extract　the　multi-scale　features　from　seismic　data,　which　benefits　for　checking　and　fine-tuning　the　fusing　results.　Finally,　we　qualitatively　and　quantificationally　evaluate　these　DL　models　(Unet-D,　HUnet-D,　and　EL-HUnet)　using　the　synthetic　validation　dataset.　Moreover,　we　apply　these　models　to　3-D　field　data　volumes　for　automatic　fault　interpretation.　Compared　with　the　coherence　attribute,　Unet-D　and　HUnet-D　models,　we　find　that　the　EL-HUnet　model　achieves　the　comparable　model　performance　for　effectively　enhancing　the　precision　and　continuity　of　the　detected　faults.