Vector denoising of multicomponent seismic data based on quaternion weighted nuclear norm minimization
Jianjun Gao, Xiaogang Wang, Yonghu Fu · 2024
During seismic exploration, the acquired data are often affected by various random noises. Therefore, suppressing random noise and improving the Signal-to-Noise Ratio(SNR) has always been one of the important tasks in seismic data processing. Compared with traditional P wave seismic exploration, multi-component seismic exploration technology can simultaneously obtain P wave and S wave information, and has become an important tool for exploring complex oil and gas reservoirs. For the random noise suppression of multi-component seismic data, the current strategy is to denoise each component independently. This strategy affects the vector relationship between each component’s data. This vector relationship is important for the subsequent high-precision interpretation of multi-component data processing. Therefore, it is necessary to develop vector joint denoising techniques for multi-component data. In this paper, we propose a Quaternion Weighted Nuclear Norm Minimization (QWNNM) method to suppress random noise in multi-component seismic data. Compared with the scalar WNNM denoising method, the proposed vector denoising method can better protect weak effective waves and the interrelationship among components while suppressing random noise.