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Resolution-oriented weighted stacking based on global optimization algorithm

Sun Yaoguang, Chen Siyuan, Cao Siyuan

Acta Geophysica

2023

Vol. 71, no. 5

2125--2135

Stacking is one crucial seismic data processing technique that gives a composite record made by combining traces from different shot records. The quality of stacking dramatical affects the performance of many seismic data processing tasks. The conventional equal-weight stacking method is the average of all traces in the pre-stack CMP gather, improving the signal-to-noise ratio (SNR) but reducing resolution. Most weighted stacking algorithms aim to enhance image quality by the increased SNR; however, these algorithms do not consider the resolution. Therefore, we proposed a weighted stacking algorithm with resolution enhancement, which is regarded as having maximum bandwidth and dominant frequency. Based on the genetic algorithm (GA), we calculated the stacking weights in common midpoint (CMP), or common-reflection-point (CRP) gathers. Then, we presented a weighted stacking approach to obtain the resolution-enhancement stacked data. The proposed method can obtain the resolution-enhancement stacked data by the single-trace spectrum constraint without wavelet estimation. Applications to synthetic and field seismic datasets demonstrate that compared with the traditional stacking method, the proposed method can improve the stacking resolution better, which is beneficial for subsequent interpretation.

Random noise attenuation using a structure oriented adaptive singular value decomposition

Xu Yankai, Cao Siyuan, Pan Xiao, Liu Wei, Chen Hongling

Acta Geophysica

2019

Vol. 67, no. 4

1091--1106

Singular value decomposition (SVD) is an efficient method to suppress random noise in seismic data. The performance of noise attenuation is typically affected by choosing the rank of the estimated signal using SVD. That the rank is fixed limits noise attenuation especially for a low signal-to-noise ratio data. Therefore, we propose a modified approach to attenuate random noise based on structure-oriented adaptively choosing singular values. In this approach, we first estimate dominant local slopes, predict other traces from a reference trace using the plane-wave prediction and construct a 3D seismic volume which is composed of all predicted traces. Then, we remove noise from a 2D profile whose traces are predicted from different reference traces via adaptive SVD filter (ASVD), which adaptively chooses the rank of estimated signal by the singular value increments. Finally, we stack every 2D denoised profile to a stacking denoised trace and reconstruct the 2D denoised seismic data which are composed of all stacking denoised traces. Synthetic data and field data examples demonstrate that the proposed structure-oriented ASVD approach performs well in random noise suppression for the low SNR seismic data with dipping and hyperbolic events.