Multivariate model for surface microseismic denoising in the shearlet domain

Abstract

Because of the strong random noise in surface microseismic data, the utility of the data may be decreased. Therefore, it is necessary to use some efficient techniques for denoising the microseismic data. Shearlet transform is a new multiscale transform which can adaptively capture the geometrical characteristic of multidimensional signals and represent signals containing edges optimally. Most traditional nonlinear thresholding methods based on Shearlet transform denoising presume that shearlet coefficients are independent. However, the shearlet coefficients of surface microseismic signals have significant dependencies. For this reason, these denoising schemes suppress too many coefficients that might contain useful microseismic signals information. We proposed a new multivariate model based on shearlet transform to improve this problem. This new multivariate model can not only adaptively capture the inter-scale dependency according to the anisotropic property of variances of shearlet coefficients in different sub-bands, but also take the inter-direction dependency into account. We perform tests on synthetic and field desert seismic data and the denoising results show that the proposed method can effectively preserve effective signals and remove random noise.