Self-organizing　mapping　(SOM)　is　one　of　the　most　famous　classification　method　in　seismic　facies　analysis.　Traditional　self-organizing　map　network　and　its　variation　methods　usually　use　the　Euclidean　distance　to　measure　the　similarity　between　input　data　and　weights　of　neuron　node.　A　Euclidean　distance　is　mainly　used　to　measure　the　global　similarity　between　the　random　variables,　while　a　correntropy　is　the　local　similarity　measure　of　random　variables　and　very　useful　for　non-Gaussian　signal.　In　general,　the　geological　sedimentation　has　its　own　regular　pattern.　The　data　of　seismic　imaging　is　a　response　of　geological　sedimentation.　If　the　geological　sedimentation　has　regularity,　the　seismic　data　basically　presents　characteristics　of　non-Gaussian　distribution.　Therefore,　we　proposed　a　classification　method,　which　introduce　the　maximum　correntropy　as　a　new　distance　measure　criterion　to　the　SOM.　The　field　data　example　demonstrates　that　the　proposed　method　can　effectively　delineate　the　distribution　and　boundary　of　channels　in　seismic　data.　Therefore,　it　is　of　great　significance　to　improve　the　accuracy　of　reservoir　interpretation.　©　2021　Society　of　Exploration　Geophysicists　First　International　Meeting　for　Applied　Geoscience　&　Energy