Sea　level　rise,　caused　by　accelerated　melting　of　glaciers　in　Greenland　and　Antarctica　in　recent　decades,　has　become　a　major　concern　in　scientific,　environmental,　and　political　areas.　A　comprehensive　study　of　subglacial　conditions　and　processes　is　particularly　important　for　reliable　analysis　about　their　future　evolution.　Basal　units—visibly　distinct　englacial　structures　near　ice-bedrock　interface,　could　provide　substantial　insight　into　subglacial　processes,　ice-sheet　dynamic　history　and　dramatically　influence　the　flow　of　surrounding　ice.　In　order　to　enable　improved　characterization　of　these　features,　we　develop　and　apply　an　algorithm　that　allows　for　automatic　detection　of　basal　units,　therefore,　an　algorithm　based　on　deep　learning　is　proposed.　Compared　with　traditional　method　based　on　manual　feature　extraction,　proposed　algorithm　can　achieve　completely　automatic　recognition　of　basal　units　in　radargram.　The　network　is　mainly　composed　of　ResNet　and　ASPP　module,　which　can　achieve　high　accuracy　in　very　small　dataset.　We　use　radar　data　collected　by　Polar　Geophysics　Group　(PGG)　of　The　Earth　Institute　at　Columbia　University　in　Antarctica　in　2008–2009　and　2009–2010　for　experiments,　results　confirm　the　effectiveness　and　robustness　of　proposed　algorithm.　At　the　same　time,　a　more　rapid　deep　learning　method　is　tried,　which　uses　the　lightweight　network　MobileNet　V3　as　backbone,　obtaining　a　network　structure　that　can　save　81.8%　of　training　time　and　92.2%　of　processing　time　with　high　accuracy.　It　provides　a　possibility　for　rapid　network　training,　application　on　mobile　devices　and　real-time　processing　of　radargram.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.