An Adaptive High-Dimensional Progressive Denoising Method for Seismic Weak Signal Enhancement

As seismic exploration focuses on deep and ultra-deep hydrocarbon targets, seismic data are characterized by weak reflection signals and extremely low signal-to-noise ratio (SNR). Although weak reflections help delineate deep geological structures, the low SNR presents challenges for traditional denoising methods. We propose a high-dimensional adaptive progressive seismic denoising (APSD) method to enhance the SNR of deep weak reflection signals. Instead of using a global noise variance, we estimate local noise variances using a 3-D Laplacian mask based on the local characteristics of seismic data at different locations for nonstationary seismic signals. It employs local noise variance to calculate a Gaussian bilateral kernel function to estimate high-amplitude noise in the time domain and low-amplitude noise in the frequency domain. We further extend this algorithm to three dimensions by adjusting the parameters of the 3-D kernel function during the iterative process. Numerical examples of synthetic and field data demonstrate the feasibility and adaptability of the proposed method. Its comparison with the dictionary learning method and optimal damped rank reduction methods shows that the proposed method can significantly improve the SNR of deep reflection signals and is a good tool for processing deep and ultra-deep seismic data.