The energy of micro-seismic data is weak and the signal-to-noise ratio is also very low. However, the quality of the monitoring data has a significant impact on effective signal recognition in Micro-seismic. It is one of the key factors in improving the signal-to-noise ratio when processing micro-seismic data. Due to high frequency, short duration, and low energy of the micro-seismic events, the effective signal can be overwhelmed by the random noise. So it is essential to preprocess the data before determining the location of the source, the origin time, and the strength of the source. But conventional filtering methods may not be fully qualified to eliminate noise and improve SNR. In this paper, we propose a method to remove the noise of the micro-seismic data, which is mainly based on using differences in geometrical features between the effective signal and the random noise in the reconstructed phase space to separate one from the other, so that we can acquire a better and more accurate effective signal.


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