Identifying　if　a　subsurface　target　is　salt　or　not　automatically　and　accurately　is　of　vital　importance　to　oil　drilling.　But　unfortunately,　obtaining　the　precise　position　of　large　salt　deposits　is　very　difficult.　Professional　seismic　imaging　still　requires　the　interpretation　of　salt　bodies　by　experts.　This　leads　to　very　subjective,　highly　variable　renderings.　More　alarmingly,　it　leads　to　potentially　dangerous　situations　for　drillers　in　oil　and　gas　companies.　In　this　paper,　a　Squeeze-Extraction　Feature　Pyramid　Networks　(referred　to　as　Se-FPN)　was　proposed　to　tackle　the　task　of　image　segmentation　of　salt　deposits.　Specifically,　we　utilized　SeNet　as　backbone　so　as　to　implicitly　learn　to　suppress　irrelevant　regions　in　an　input　image　while　highlighting　salient　features　useful　for　the　task.　Considering　the　importance　of　multi-scales　information,　we　proposed　an　improved　FPN　to　integrate　information　of　different　scales.　In　order　to　further　fuse　the　information　from　multiple　scales,　the　Hypercolumns　module　was　inserted　at　the　end　of　the　network.　The　proposed　Se-FPN　has　been　applied　to　the　TGS　Salt　Identification　Challenge　and　achieved　high　quality　segmentation　effect.　The　Mean　Intersection　over　Union　value　can　reach　0.86.　©　2019　IEEE.