Random Noise Attenuation for 3D Seismic Data by Modified Multichannel Singular Spectrum Analysis

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation in seismic data, which decomposes the vector space of the Hankel matrix of the noisy signal into a signal subspace and a noise subspace by the truncated singular value decomposition (TSVD). However, this signal subspace actually still contains residual noise. In this abstract, we derive a new formula of low-rank reduction, which is more powerful in distinguishing between signal and noise compared with traditional TSVD. By introducing a trim factor, we propose a new algorithm for random noise attenuation. Application of the modified MSSA algorithm on synthetic and field seismic data demonstrates a superior performance compared with the conventional MSSA algorithm and the 2D median filtering.