Seismic　coherence　is　one　of　the　seismic　attributes　that　can　be　used　to　aid　in　seismic　fault　and　stratigraphic　interpretation.　Traditionally,　seismic　coherence　is　computed　by　measuring　the　similarity　between　waveforms　of　2D　seismic　section　or　3D　seismic　volumes.　More　recently,　the　computation　of　a　seismic　attribute　that　may　be　significant　in　seismic　fault　interpretation　or　a　seismic　fault　attribute　is　treated　as　an　image　segmentation　problem　and　using　different　deep　learning　(DL)　architectures.　For　doing　this,　the　researchers　mainly　concentrate　on　applying　cutting-edge　DL　architectures　in　computing　seismic　fault　attribute.　The　available　literature　on　the　topic　discusses　exploring　factors,　which　can　be　classified　into　data　and　method　categories,　that　affect　the　performance　of　seismic　image　segmentation　by　using　DL　methods.　To　explore　such　factors　in　computing　a　seismic　fault　attribute,　we　compare　the　computed　fault　probability　using　DL　architectures　under　different　scenarios.　The　designed　scenarios　aim　to　highlight　the　leading　factor　that　may　affect　the　accuracy　and　resolution　of　seismic　image　segmentation.　The　proposed　comparisons　are　applied　to　one　marine　seismic　survey　from　New　Zealand　and　one　land　seismic　survey　from　China.　The　results　demonstrate　that　properly　preparing　training　data　is　far　more　important　than　choosing　a　cutting-edge　DL　architecture　in　computing　seismic　fault　attribute.　We　also　propose　a　practical　workflow　that　can　include　real　seismic　data　and　corresponding　labels　in　training　data　for　a　specific　seismic　survey.　©　2022　Society　of　Exploration　Geophysicists　and　the　American　Association　of　Petroleum　Geologists.