A denoising framework for microseismic and reflection seismic data based on block matching

Abstract

Microseismic and seismic data with a low signal-to-noise ratio affect the accuracy and reliability of processing results and their subsequent interpretation. Thus, denoising is of great importance. We have developed an effective denoising framework for surface (micro)-seismic data using block matching. The novel idea of the proposed framework is to enhance coherent features by grouping similar 2D data blocks into 3D data arrays. The high similarities in the 3D data arrays benefit any filtering strategy suitable for multidimensional noise suppression. We test the performance of this framework on synthetic and field data with different noise levels. The results demonstrate that the block-matching-based framework achieves state-of-the-art denoising performance in terms of incoherent-noise attenuation and signal preservation.