Application of residual learning to microseismic random noise attenuation

Microseismic data which are recorded by near-surface sensors are usually drawn in strong random noise. The reliability and accuracy of arrivals picking, source localization, microseismic imaging and source mechanism inversion are often afected by the random noise. Random noise attenuation is important for microseismic data processing. We introduce a novel deep convolutional neural network-based denoising approach to attenuate random noise from 1D microseismic data. The approach predicts the noise (the diference between the noisy microseismic data and clean microseismic data) as output instead of directly outputing the denoised data that is called residual learning. With the residual learning strategy, the approach removes the clean data in the hidden layers. In other words, the approach learns from the random noise prior instead of an explicit data prior. Then, the denoised data are reconstructed via subtracting noise from noisy data. Compared with other commonly used denoising methods, the proposed method performs its efectiveness and superiority by experimental tests on synthetic and real data. The model is trained with synthetic data and applied on real data. The results show that random noise in the synthetic and real data can been removed. However, some noise still remains in the real data case. The reason for that may be the approach can only remove random noise nor the correlated noise. Other methods are needed to be applied to remove the correlated noise to obtain higher performance after that approach when the real microseismic data which contain both correlated noise and random noise.