Analyzing　the　surface　and　bedrock　locations　in　radar　imagery　enables　the　computation　of　ice　sheet　thickness,　which　is　important　for　the　study　of　ice　sheets,　their　volume　and　how　they　may　contribute　to　global　climate　change.　However,　the　traditional　handcrafted　methods　cannot　quickly　provide　quantitative,　objective　and　reliable　extraction　of　information　from　radargrams.　Most　traditional　handcrafted　methods,　designed　to　detect　ice-surface　and　ice-bed　layers　from　ice　sheet　radargrams,　require　complex　human　involvement　and　are　difficult　to　apply　to　large　datasets,　while　deep　learning　methods　can　obtain　better　results　in　a　generalized　way.　In　this　study,　an　end-to-end　multi-scale　attention　network　(MsANet)　is　proposed　to　realize　the　estimation　and　reconstruction　of　layers　in　sequences　of　ice　sheet　radar　tomographic　images.　First,　we　use　an　improved　3D　convolutional　network,　C3D-M,　whose　first　full　connection　layer　is　replaced　by　a　convolution　unit　to　better　maintain　the　spatial　relativity　of　ice　layer　features,　as　the　backbone.　Then,　an　adjustable　multi-scale　module　uses　different　scale　filters　to　learn　scale　information　to　enhance　the　feature　extraction　capabilities　of　the　network.　Finally,　an　attention　module　extended　to　3D　space　removes　a　redundant　bottleneck　unit　to　better　fuse　and　refine　ice　layer　features.　Radar　sequential　images　collected　by　the　Center　of　Remote　Sensing　of　Ice　Sheets　in　2014　are　used　as　training　and　testing　data.　Compared　with　state-of-the-art　deep　learning　methods,　the　MsANet　shows　a　10%　reduction　(2.14　pixels)　on　the　measurement　of　average　mean　absolute　column-wise　error　for　detecting　the　ice-surface　and　ice-bottom　layers,　runs　faster　and　uses　approximately　12　million　fewer　parameters.