The　accuracy　and　efficiency　of　fault　interpretation　greatly　affect　the　exploration　and　development　of　oil　and　gas　reservoirs.　The　traditional　manual　fault　interpretation　method　relies　on　the　experience　of　interpreters　and　takes　a　long　time;　the　conventional　automatic　fault　interpretation　method　mainly　interprets　faults　by　discontinuity　analysis　of　seismic　data　and　often　contains　multiple　parameters,　and　thus　its　accuracy　in　fault　interpretation　mostly　depends　on　the　selected　parameters.　With　the　development　of　deep　learning　in　recent　years,　the　convolutional　neural　networks　(CNNs)　with　nonlinear　properties　can　also　describe　the　discontinuous　characteristics　of　seismic　data.　Therefore,　an　edge　detection　technology　in　deep　learning,　i.e.,　the　holistically-nested　edge　detection　(HED)　network,　is　introduced　in　this　study,　and　the　network　is　improved　and　optimized　on　the　basis　of　the　cha-racteristics　of　seismic　data　and　seismic　faults,　which　leads　to　the　improved　HED　(IHED)　network　suitable　for　intelligent　seismic　fault　interpretation.　The　main　steps　are　as　follows:　The　original　two-dimensional　(2D)　HED　network　is　extended　to　a　three-dimensional　(3D)　version,　and　thus　a　3D　HED　network　is　constructed;　the　architecture　of　the　3D　HED　network　is　adjusted　considering　the　multi-scale　property　of　the　network;　the　3D　HED　network　is　trained　with　3D　synthetic　seismic　data　and　corresponding　label　data　for　a　3D　IHED　model,　and　then　the　3D　IHED　model　is　applied　to　field　data　for　seismic　fault　interpretation.　Compared　with　the　coherence　cube　algorithm　and　U-Net　model,　the　3D　IHED　model　features　higher　accuracy　in　the　prediction　of　faults　and　better　continuity.　The　proposed　model　provides　an　efficient　and　reliable　new　idea　for　intelligent　fault　interpretation.　Keywords:　©　2022,　Editorial　Department　OIL　GEOPHYSICAL　PROSPECTING.　All　right　reserved.