Clonal selection based intelligent parameter inversion algorithm for prestack seismic data

Yan, Xuesong1; Li, Pengpeng1; Tang, Ke2; Gao, Liang3; Wang, Ling4

2020

10.1016/j.ins.2019.12.083

INFORMATION SCIENCES   影响因子和分区

0020-0255

1872-6291

Amplitude variation with offset (AVO) elastic parameter inversion is an approach of oil exploration that employs seismic information, and it is a problem of non-linear optimization. When using a quasi-linear or linear approach to solve the problem, the inversion result is unreliable or inaccurate. Metaheuristic search methods, e.g., bio-inspired optimization algorithms such as genetic algorithms, are capable of handling highly non-linear optimization problems and thus provide a promising approach for oil and gas exploration. As one of the metaheuristic search approaches, the immune clone selection algorithm exhibits the property of fast convergence and strong global search capability. In this paper, the immune clone selection algorithm is used to address the problem of AVO elastic parameter inversion. This algorithm employs the specific initialization strategy of Aki as well as the approximation equation of Rechard, which is utilized in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is improved in accordance. The results of multiple experiments demonstrate that the approach could significantly improve the inversion accuracy, and the correlation coefficient of the elastic parameters acquired via inversion is specifically high.
© 2019

prestack seismic data elastic parameter inversion clonal selection algorithm intelligent algorithms correlation coefficient

SCI ; EI

英语

其他

National Natural Science Foundation of China[] ; Fundamental Research Funds for the Central Universities[] ; National Natural Science Foundation of China[51825502] ; National Natural Science Foundation of China[61525304] ; National Natural Science Foundation of China[61672478] ; National Natural Science Foundation of China[61673354] ; National Natural Science Foundation of China[61873328] ; National Natural Science Foundation of China[U1911205]

Computer Science

Computer Science, Information Systems

WOS:000517659200006

Elsevier Inc.

20200207998282

Approximation algorithms ; Biomimetics ; Cloning ; Elasticity ; Genetic algorithms ; Geological surveys ; Nonlinear programming ; Petroleum prospecting ; Seismic prospecting ; Seismic response ; Seismic waves

Bioengineering and Biology:461 ; Geology:481.1 ; Geophysical Prospecting:481.4 ; Seismology:484 ; Petroleum Deposits : Development Operations:512.1.2 ; Mathematics:921

COMPUTER SCIENCE

Web of Science

1.School of Computer Science, China University of Geosciences, Wuhan; Hubei; 430074, China
2.Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen; Guangdong; 518055, China
3.State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan; Hubei; 430074, China
4.Department of Automation, Tsinghua University, Beijing; 100084, China

Yan, Xuesong,Li, Pengpeng,Tang, Ke,et al. Clonal selection based intelligent parameter inversion algorithm for prestack seismic data[J]. INFORMATION SCIENCES,2020,517:86-99.

Yan, Xuesong,Li, Pengpeng,Tang, Ke,Gao, Liang,&Wang, Ling.(2020).Clonal selection based intelligent parameter inversion algorithm for prestack seismic data.INFORMATION SCIENCES,517,86-99.

Yan, Xuesong,et al."Clonal selection based intelligent parameter inversion algorithm for prestack seismic data".INFORMATION SCIENCES 517(2020):86-99.