The　study　aimed　to　investigate　the　use　of　Magnetic　Anomaly　Detection　(MAD)　to　locate　defects　in　underground　ferromagnetic　pipelines　without　exposing　them,　which　presented　a　large　advantage　over　current　methods.　MAD　has　received　much　attention　in　recent　years　because　it　has　many　possible　applications　in　detecting　and　locating　ferromagnetic　targets,　especially　for　ferromagnetic　pipelines.　However,　the　accuracy　of　MAD　is　limited　due　to　disturbs　from　the　geomagnetic　field　and　other　sources　around.　The　current　investigation　involved　Variational　Mode　Decomposition　and　Scale-Space　Segmentation　(VMD-SSS)　to　improve　the　performance　of　MAD　for　pipeline　defects.　Variational　Mode　Decomposition　(VMD)　is　the　algorithm　decomposes　signals　into　different　modes,　which　is　a　revised　version　of　the　Empirical　Mode　Decomposition　(EMD)　for　analyzing　non-linear　and　non-stationary　signals.　Since　the　geomagnetic　signal　is　also　a　non-linear　and　non-stationary　signal,　and　pipeline　defects　will　affect　the　geomagnetic　signal,　it　is　crucial　to　study　the　bandwidth　estimation　of　instantaneous　frequency　(IF)　and　instantaneous　amplitude　(IA)　spectrum　based　on　the　mode.　In　the　present　work,　VMD　was　employed　to　reconstruct　the　magnetic　anomaly　signals　to　create　an　effective　detector,　which　can　be　used　to　decompose　the　magnetic　anomaly　signal　into　different　modes　accurately.　The　input　parameters　of　VMD　were　optimized　using　Scale　Space　Segmentation　(SSS),　and　the　anti-noise　performance　was　compared.　It　was　noticed　that　VMD-SSS　served　as　an　effective　tool　in　both　extracting　the　time-frequency　characteristics　of　magnetic　anomaly　signals　and　the　coupling　analysis　of　magnetic　field　signals　between　different　frequency　bands.　The　results　of　applying　VMD-SSS　in　experiments　showed　that　the　peak　of　the　coupling　strength　appeared　when　the　frequency　band　was　less　than　1　Hz,　and　pipeline　defects　could　be　located　by　using　the　specified　frequency　band　signal　with　an　error　of　no　more　than　5%.　The　VMD-SSS　method　presented　in　the　paper　can　be　used　to　describe　the　energy　coupling　characteristics　of　the　magnetic　anomaly　signals　at　different　time-frequency　scales　and　provides　support　for　studying　the　mechanism　and　characteristics　of　MAD　in　ferromagnetic　pipeline　defect.　©　2019　IEEE.