Simultaneous Suppression of Random Noise and Ground-Roll with a Fuzzy Logic-Guided Deep Network

Seismic data are frequently contaminated by incoherent random noise and coherent surface waves; both severely degrade subsurface data quality. While random noise is statistically irregular and uncorrelated, surface waves exhibit structured regularity in both temporal and spatial domains. Their coherent low-frequency propagation makes them fundamentally distinct from random noise, posing significant challenges to simultaneous suppression. This letter presents a Deep Adaptive Signal Denoising network (DASDNet) designed for random and ground roll simultaneous suppression using weakly supervised training on synthetic clean-noisy pairs. The architecture incorporates short-time Fourier transform (STFT) and discrete wavelet transform (DWT) for multiscale time-frequency decomposition. A fuzzy logic-based encoder is employed to mitigate feature uncertainty, complemented by a time-frequency regularization module that preserves structural coherence. A unified loss function across time, frequency, and spatial domains guides robust training. DASDNet demonstrates remarkable generalization capability to both synthetic and field-recorded datasets, achieving robust performance without reliance on labeled field data or manual post-hoc parameter tuning. Experimental results show that DASDNet improves signal-to-noise ratio (SNR) by 6.86 dB, reduces mean squared error (MSE) by 4433, and increases structural similarity index (SSIM) by 0.22, outperforming other methods. Qualitative analysis further demonstrates its advantage in preserving primary fidelity while suppressing noises.