An Improved Weighted Projection Onto Convex Sets Method for Seismic Data Interpolation and Denoising

Abstract

Due to the environment effects, economy restrictions, and acquisition equipment limitations, observed seismic data always have several traces missing and contain some random noise, affecting the performance of surface-related multiple elimination (SRME), wave-equation-based imaging, and inversion. Projection onto convex sets (POCS) is an effective interpolation algorithm, while the performance is unsatisfactory in noisy situations. Weighted POCS (WPOCS) method can weaken the random noise effects to some extent, but the performance is still unsatisfactory. Thus, an improved WPOCS (IWPOCS) method is proposed in this paper, for seismic data interpolation and denoising simultaneously based on Curvelet transform. First, the POCS formula is derived from the iterative hard threshold (IHT) view. Then, its shortcoming is analyzed because there is an implicit assumption that the observed seismic data should have a high signal-to-noise ratio (SNR). Finally, a novel method named IWPOCS is proposed based on WPOCS method, which can achieve simultaneous interpolation and denoising. Among the above three methods, the IWPOCS method is the most effective to interpolate and denoise seismic data in terms of recovered SNR and visual view. Numerical experiments on the synthetic data and the real seismic data from the marine acquisition with towed streamers confirm the validity of the proposed IWPOCS method.