التفاصيل البيبلوغرافية
العنوان: |
Fast pre-stack multi-channel inversion constrained by seismic reflection features. |
المؤلفون: |
Ya-Ming Yang, Xing-Yao Yin, Kun Li, Feng Zhang, Jian-Hu Gao |
المصدر: |
Petroleum Science (KeAi Communications Co.); Aug2023, Vol. 20 Issue 4, p2060-2074, 15p |
مستخلص: |
Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set. [ABSTRACT FROM AUTHOR] |
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قاعدة البيانات: |
Complementary Index |