Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon

被引:0
|
作者
Zhuang, Kai [1 ]
Trad, Daniel [1 ]
Ibrahim, Amr [2 ]
机构
[1] Univ Calgary, Fac Sci, Dept Earth Energy & Environm, Calgary, AB T2N 1N4, Canada
[2] Beni Suef Univ, Fac Sci, Dept Phys, Bani Suwayf 62521, Egypt
基金
加拿大自然科学与工程研究理事会;
关键词
deblending; sparse radon; inversion; SPARSE;
D O I
10.3390/a17080344
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dipping layers are centred around zero offsets. As a result, CMP gathers exhibit a simpler structure compared to common receiver gathers (CRGs), where these reflections are apex-shifted. Consequently, we can employ a zero-offset hyperbolic Radon operator to process CMP gathers. This operator is a computationally more efficient alternative to the apex-shifted hyperbolic Radon required for processing CRG gathers. Sparse transforms, such as the Radon transform, can stack reflections and produce sparse models capable of separating blended sources. We utilize the Radon operator to develop an inversion-based deblending framework that incorporates a sparse model constraint. The inclusion of a sparsity constraint in the inversion process enhances the focusing of the transform and improves data recovery. Inversion-based deblending enables us to account for all observed data by incorporating the blending operator into the cost function. Our synthetic and field data examples demonstrate that inversion-based deblending in the CMP domain can effectively separate blended sources.
引用
收藏
页数:16
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