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 for damping the singular values in traditional MSSA, we propose a new algorithm for random noise attenuation. The proposed modified MSSA is named as the damped MSSA. Application of the damped MSSA algorithm on synthetic and field seismic data demonstrates a superior performance compared with the conventional MSSA algorithm and the 2D median filtering.