Weak Seismic Signal Enhancement for Low Signal-to-Noise Ratio Data Using Adaptive Nonstationary Signal Decomposition

Enhancing weak seismic signals in seismic data processing with a low signal-to-noise ratio (SNR) is a critical task, and it is imperative to attenuate random noise without damaging effective signals. One effective approach to achieving this is through the application of multichannel singular spectrum analysis (MSSA). However, the inherent non-stationary nature of weak signals poses a challenge for MSSA, as it struggles to completely attenuate random noise via the truncating singular value decomposition (TSVD). This study introduces a novel method referred to as adaptive non-stationary signal decomposition (ANSSD) to significantly improve the attenuation ability of seismic random noise of MSSA and enhance weak signals. Recognizing the non-stationary, non-Gaussian, and nonlinear random noise, our proposed method begins by decomposing the prestack data using singular value decomposition (SVD). Subsequently, each column of the left singular vector matrix is subjected to adaptive non-stationary signal decomposition. Finally, the data undergoes processing through truncated singular value decomposition in the frequency domain. For the synthetic data experiment, the SNR of the raw data is -14.03 dB, -4.38 dB after MSSA processing, and 0.29 dB after ANSSD processing. Meanwhile, the filed data processing results also prove that the ANSSD method is superior to the MSSA method in suppressing random noise and enhancing weak signals.