To　improve　the　accuracy,　reduce　the　time　consumption　and　obtain　the　number　of　faults,　a　fault　detection　method　based　on　AP　(affinity　propagation)　clustering　and　PCA　(principal　component　analysis)　was　proposed.　Firstly,　discontinuous　points　in　seismic　horizons　were　searched　out　by　the　connected　component　labeling　method.　Secondly,　the　AP　clustering　algorithm　was　used　to　cluster　the　discontinuous　points　and　the　points　of　the　same　cluster　were　used　to　determine　a　fault,　meanwhile,　the　faults　existing　in　a　seismic　section　were　quantified.　Finally,　the　PCA　was　adopted　to　calculate　the　principal　direction　of　the　discontinuous　points　contained　in　the　same　cluster.　As　a　result,　the　corresponding　cluster　center　and　the　principal　direction　determined　a　straight　line,　and　the　part　that　intercepted　by　the　clustered　edge　was　the　fault　we　wanted.　In　the　proposed　method,　the　time　consumption　of　correlation　calculation　of　the　traditional　method　was　reduced;　the　computing　work　was　simplified　and　the　number　of　the　faults　in　the　seismic　section　was　obtained.　To　confirm　the　feasibility　and　advancement　of　the　proposed　method,　comparative　experiments　were　done　on　the　seismic　model　data　and　the　real　seismic　section.　The　results　show　that　the　accuracy　of　the　proposed　method　was　better　and　the　time　cost　was　greatly　reduced.