In　most　approaches　based　on　computational　auditory　scene　analysis　(CASA),　the　ideal　binary　mask　(IBM)　is　often　used　for　noise　reduction.　However,　it　is　almost　impossible　to　obtain　the　IBM　result.　The　error　in　IBM　estimation　may　greatly　violate　smooth　evolution　nature　of　speech　because　of　the　energy　absence　in　many　speech-dominated　time-frequency　(T-F)　units.　To　reduce　the　error,　the　ideal　ratio　mask　(IRM)　via　modeling　the　spatial　dependencies　of　speech　spectrum　is　used　as　an　optimal　target　mask　because　the　predictive　ratio　mask　is　less　sensitive　to　the　error　than　the　predictive　binary　mask.　In　this　paper,　we　introduce　a　data　field　(DF)　to　model　the　spatial　dependencies　of　the　cochleagram　for　obtaining　the　ratio　mask.　Firstly,　initial　T-F　units　of　noise　and　speech　are　obtained　from　noisy　speech.　Then　we　can　calculate　the　forms　of　the　potentials　of　noise　and　speech.　Subsequently,　their　optimal　potentials　which　reflect　their　respective　distribution　of　potential　field　are　obtained　by　the　optimal　influence　factors　of　speech　and　noise.　Finally,　we　exploit　the　potentials　of　speech　and　noise　to　obtain　the　ratio　mask.　Experimental　results　show　that　the　proposed　method　can　obtain　a　better　performance　than　the　reference　methods　in　speech　quality.