Amplitude-preserving iterative deblending of simultaneous source seismic data using high-order Radon transform

作者：Xue, Yaru; Man, Meixi^*; Zu, Shaohuan; Chang, Fanglan; Chen, Yangkang

来源：Journal of Applied Geophysics, 2017, 139: 79-90.

DOI：10.1016/j.jappgeo.2017.02.010

• EMPIRICAL-MODE DECOMPOSITION
• RANDOM NOISE ATTENUATION
• SIMULTANEOUS SOURCES SEPARATION
• REVERSE-TIME MIGRATION
• SHAPING REGULARIZATION
• SEISLET TRANSFORM
• VELOCITY ANALYSIS
• SIMILARITY
• SEMBLANCE
• AVO

The high-order Radon transform is adopted to eliminate incoherent noise that appears in common receiver gathers when simultaneous source data are acquired. An iterative scheme is designed to separate the blended seismic data. During each iteration, the blending noise is first estimated by the high-order Radon transform and then removed from the pseudo-deblended data by simple subtraction. A high-order Radon transform was proposed combining the superposition property of the Radon transform and the Amplitude versus-Offset or AVO-preserving property of the orthogonal polynomial transformation. It can effectively attenuate the noise and improve the signal-to-noise ratio (SNR) better than the conventional Radon denoising method. To demonstrate that the proposed algorithm has a good accuracy and efficiency, we compared the denoising effectiveness between the high-order Radon transform and the conventional Radon transform. Synthetic and field data examples confirm that the high-order Radon transform produces more accurate data estimates than the conventional Radon transform.

• 出版日期2017-4
• 单位中国石油大学（北京）