Submarine　fan　is　an　important　lithologic　reservoir.　However,　it　is　still　a　challenge　to　precisely　estimate　the　lithofacies　of　submarine　fan　in　exploration　geophysics.　Therefore,　we　propose　significance　indicators　based　on　the　depositional　model　to　solve　lack　of　well　data　in　the　exploration　period.　With　submarine　fan　facies　model　analysis,　sandstone　and　conglomerate　contents　are　selected　as　significance　indicators　to　quantify　lithofacies　of　submarine　fan　in　Dongying　Formation,　Liaodong　Depression,　China.　These　indicators　have　not　only　clear　geological　significance,　but　also　response　of　seismic　reflection.　The　fuzzy　radial　function　neural　network　maps　the　relation　between　“sandstone　and　conglomerate　contents”　and　“seismic　attributes”　to　estimate　lithofacies　of　submarine　fan,　which　improve　the　precision　of　large-scale　reservoir　characterization　in　exploration　period.　Our　objective　is　to　find　out　quantitative　indicators　between　fan　depositional　model　and　seismic　data　as　a　potential　approach　to　estimate　the　submarine　fan　lithofacies.　©,　2015,　Science　Press.　All　right　reserved.