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| Prestack seismic hybrid inversion based on simulated annealing algorithm with adaptive optimization parameters |
| GUO Qiang1,2, LUO Cong2, LIU Hongda3, HUANG Jian4 |
1. School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, Jiangshu 221116, China;
2. School of Earth Sciences and Engineering, Hohai University, Nanjing, Jiangshu 211100, China;
3. No.1 Geological Team of Shandong Provincial Bureau Institute of Geology and Mineral Resources, Jinan, Shandong 250100, China;
4. College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China |
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Abstract Prestack seismic inversion is an important technique for the quantitative processing and interpretation of seismic data, and the global optimization algorithm is an effective approach in prestack seismic inversion to estimate the elastic parameters. As a main global optimization algorithm, the simulated annealing algorithm has been widely used in inverting seismic data. However, the algorithm involves multiple optimization parameters (e.g., initial temperature, disturbance range, etc.), which have great effects on the inversion results. The parameter optimization by simulated annealing is mainly based on model trial calculation, but such an empirical method is easy to introduce errors and lacks promotion. To this end, this paper proposes a prestack seismic hybrid inversion method based on the simulated annealing algorithm with adaptive optimization parameters. Specifically, the Bayesian linear inversion and simulated-annealing nonlinear inversion are combined. The linear inversion results are used to estimate subsequent optimization parameters and construct prior models; according to the data differences of different seismic tracks, the applicable initial temperature and disturbance range are calculated track by track, which effectively improves the applicability and stability of the algorithm. The synthetic data tests and the application of measured data show that compared with those of the conventional simulated annealing inversion method, the inversion results of elastic parameters by the proposed method have a higher correlation coefficient with the logging data.
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Received: 31 July 2022
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2023, Vol. 58 
