Petroleum Science >2022,??lssue 2:??- DOI: https://doi.org/10.1016/j.petsci.2021.12.002
Diffraction separation and imaging based on double sparse transforms Open?Access
文章信息
作者：Xue Chen, Jing-Jie Cao, He-Long Yang, Shao-Jian Shi, Yong-Shuai Guo,
作者單位：
投稿時(shí)間：
引用方式：Xue Chen, Jing-Jie Cao, He-Long Yang, Shao-Jian Shi, Yong-Shuai Guo, Diffraction separation and imaging based on double sparse transforms, Petroleum Science, Volume 19, Issue 2, 2022, Pages 534-542,
文章摘要
Abstract
Reflection imaging results generally reveal large-scale continuous geological information, and it is difficult to identify small-scale geological bodies such as breakpoints, pinch points, small fault blocks, caves, and fractures, etc. Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging. In the common-offset domain, reflections are mostly expressed as smooth linear events, whereas diffractions are characterized by hyperbolic events. This paper proposes a diffraction extraction method based on double sparse transforms. The linear events can be sparsely expressed by the high-resolution linear Radon transform, and the curved events can be sparsely expressed by the Curvelet transform. A sparse inversion model is built and the alternating direction method is used to solve the inversion model. Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.
Reflection imaging results generally reveal large-scale continuous geological information, and it is difficult to identify small-scale geological bodies such as breakpoints, pinch points, small fault blocks, caves, and fractures, etc. Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging. In the common-offset domain, reflections are mostly expressed as smooth linear events, whereas diffractions are characterized by hyperbolic events. This paper proposes a diffraction extraction method based on double sparse transforms. The linear events can be sparsely expressed by the high-resolution linear Radon transform, and the curved events can be sparsely expressed by the Curvelet transform. A sparse inversion model is built and the alternating direction method is used to solve the inversion model. Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.
關(guān)鍵詞
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Diffraction separation; Common-offset domain; Diffraction imaging; High-resolution linear Radon transform; Curvelet transform; Sparse inversion