Multicomponent Microseismic Data Denoising by 3D Shearlet Transform

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We propose an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data as it captures details of signals at different scales and orientations, which benefits signal and noise separation. We propose a novel processing strategy based on a signal- detection operator which can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of three-component microseismic signals into account. We perform tests on synthetic and real datasets demonstrate that the proposed method can effectively remove random noise and preserve weak signals.