LAUSR

Predictive filtering methods are widely used in industry to remove random noise, owing to their stability and efficiency. Traditional predictive filtering methods use a fixed autoregressive order (filtering factor) in the spatial direction. However, denoising spatially varying seismic data have thus far been ineffective. In this study, the effect of nonstationary seismic data variations is overcome using local windowing or nonstationary denoising methods. We propose a nonstationary predictive filtering method in which an autoregressive model is constructed using spatially varying filtering factors. First, we evaluate the structural complexity of the global data based on a local window selection using plane-wave destruction. Then, adaptive filtering factors are proposed depending on the structural complexity of the seismic data. Finally, the autoregressive model containing the adaptive filtering factors is solved. We use three quality measures to evaluate the denoising performance: the signal-to-noise ratio ( $S/N$ ), the peak signal-to-noise ratio (PSNR), and local similarity (LS). The proposed method acts on both synthetic and field data, including both pre- and post-stack seismic data. Compared with prior nonstationary filtering methods, experimental results demonstrate that structural complexity-guided (CG) predictive filtering enables efficient random noise attenuation and reduces signal leakage.