The　denoising　of　microseismic　signals　is　a　prerequisite　for　subsequent　analysis　and　research.　In　this　research,　a　new　microseismic　signal　denoising　algorithm　called　the　Black　Widow　Optimization　Algorithm　(BWOA)　optimized　Variational　Mode　Decomposition　(VMD)　joint　Wavelet　Threshold　Denoising　(WTD)　algorithm　(BVW)　is　proposed.　The　BVW　algorithm　integrates　VMD　and　WTD,　both　of　which　are　optimized　by　BWOA.　Specifically,　this　algorithm　utilizes　VMD　to　decompose　the　microseismic　signal　to　be　denoised　into　several　Band-Limited　Intrinsic　Mode　Functions　(BLIMFs).　Subsequently,　these　BLIMFs　whose　correlation　coefficients　with　the　microseismic　signal　to　be　denoised　are　higher　than　a　threshold　are　selected　as　the　effective　mode　functions,　and　the　effective　mode　functions　are　denoised　using　WTD　to　filter　out　the　residual　low-　and　intermediate-frequency　noise.　Finally,　the　denoised　microseismic　signal　is　obtained　through　reconstruction.　The　ideal　values　of　VMD　parameters　and　WTD　parameters　are　acquired　by　searching　with　BWOA　to　achieve　the　best　VMD　decomposition　performance　and　solve　the　problem　of　relying　on　experience　and　requiring　a　large　workload　in　the　application　of　the　WTD　algorithm.　The　outcomes　of　simulated　experiments　indicate　that　this　algorithm　is　capable　of　achieving　good　denoising　performance　under　noise　of　different　intensities,　and　the　denoising　performance　is　significantly　better　than　the　commonly　used　VMD　and　Empirical　Mode　Decomposition　(EMD)　algorithms.　The　BVW　algorithm　is　more　efficient　in　filtering　noise,　the　waveform　after　denoising　is　smoother,　the　amplitude　of　the　waveform　is　the　closest　to　the　original　signal,　and　the　signal-to-noise　ratio　(SNR)　and　the　root　mean　square　error　after　denoising　are　more　satisfying.　The　case　based　on　Fankou　Lead-Zinc　Mine　shows　that　for　microseismic　signals　with　different　intensities　of　noise　monitored　on-site,　compared　with　VMD　and　EMD,　the　BVW　algorithm　is　more　efficient　in　filtering　noise,　and　the　SNR　after　denoising　is　higher.