Noise　pollution　is　an　important　issue　to　be　solved　in　the　application　of　coal　or　rock　dynamic　disasters　electromagnetic　monitoring.　Denoising　effect　directly　affect　the　disaster　prediction　accuracy.　Empirical　Mode　Decomposition(EMD)　is　the　most　widely　method　used　in　electromagnetic　signal　denoising.　But　when　the　frequency　characteristics　of　the　signal　and　noise　are　similar,　the　algorithm　exists　serious　noise　aliasing　of　intrinsic　mode　function(IMF).　Some　IMFs　are　the　combination　of　signal　and　noise.　To　solve　this　problem,　this　paper　proposes　a　denoising　method　based　on　EMD　and　frequency　domain　constrained　independent　component　analysis.　Firstly,　the　noisy　signal　is　decomposed　to　a　series　of　IMFs　by　EMD.　Secondly,　calculate　the　correlation　coefficients　of　each　IMF　and　the　original　signal,　identify　the　transition　IMF　between　noise　and　signal.　Then　the　high　frequency　noise　IMFs　above　the　transition　IMF　are　removed.　Thirdly,　independent　component　analysis　of　the　transition　IMF　based　on　frequency-domain　constraints(the　frequency　domain　of　transition　IMF　follow-up　component)　to　remove　noise.　Finally,　the　denoised　transition　IMF　and　its　subsequent　IMFs　are　reconstructed　to　the　denoised　signal.　Take　noisy　Ricker　wavelet　and　field　electromagnetic　signals　as　the　example,　use　signal-noise　ratio　quantitatively　verify　the　validity　of　the　denoising　method　described　above　to　process　electromagnetic　signal　mode　aliasing　problem.　Frequency　domain　constrained　independent　component　analysis　has　the　advantages　of　denoising　fast　convergence　and　high　efficiency.　The　denoising　method　is　suitable　for　mass　rapid　real-time　monitoring　signals.　©　2017,　Editorial　Office　of　Journal　of　China　Coal　Society.　All　right　reserved.