The presence of strong random noise in surface microseismic data may decrease the utility of these data. Thus, in the monitoring of surface microseismic, the primary problem is to attenuate random noise and increase the signal-to-noise ratio. Shearlet transform is a new multiscale transform which can adaptively capture the geometrical characteristic of multidimensional signals and represent signals containing edges optimally. In this paper, we proposed a Shearlet transform modulus maxima denoising method (STMM) to suppress the random noise in surface microseiseismic data. This method removes white noises from microseismic signals by analyzing the evolution of the Shearlet transform maxima across scales. Because the modulus maxima amplitude of the noise decreases rapidly with the increase of scale, while the microseismic signals amplitude increases with the increase of scale. The experimental results of the microseismic forward modeling data and real microseismic data show that this method can effectively eliminate random noise and preserve signals under low SNR.


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