| 基于粒子群优化的细胞神经网络油气重力异常信息提取 |
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| 引用本文： | 杜润林,刘展. 基于粒子群优化的细胞神经网络油气重力异常信息提取[J]. 吉林大学学报(地球科学版), 2015, 45(3): 926-933. DOI: 10.13278/j.cnki.jjuese.201503301 |
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| 作者姓名： | 杜润林 刘展 |
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| 作者单位： | 1. 中国石油大学(华东)地球科学与技术学院, 山东 青岛 266555;2. 青岛海洋地质研究所国土资源部海洋油气资源与环境地质重点实验室, 山东 青岛 266071 |
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| 基金项目： | 国家“863”计划项目，国土资源部地质调查项目，国家自然科学基金青年基金项目 |
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| 摘 要： | 细胞神经网络方法是一种有效的重力异常提取方法,提取出的重力异常信息具有较强的横向分辨能力,但如何训练优化模板是影响该方法提取精度的关键所在。笔者引入粒子群优化算法进行参数优化,结合细胞神经网络和粒子群方法各自的特点,通过细胞神经网络动态性能分析,对模板加入约束条件,并在粒子群算法中引入收缩因子,提出一种基于改进的粒子群优化的细胞神经网络算法。使用该方法进行了模型验证和实际资料处理,处理结果表明,该方法提高了重力异常提取的稳定性,能够更准确地提取油气藏重力异常信息。
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| 关 键 词： | 细胞神经网络 粒子群优化 重力异常 收缩因子 油气 |
| 收稿时间： | 2014-08-15 |
Gravity Anomaly Extraction for Hydrocarbon Based on Particle Swarm Optimization and Cellular Neural Network |
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| Du Runlin , Liu Zhan. Gravity Anomaly Extraction for Hydrocarbon Based on Particle Swarm Optimization and Cellular Neural Network[J]. Journal of Jilin Unviersity:Earth Science Edition, 2015, 45(3): 926-933. DOI: 10.13278/j.cnki.jjuese.201503301 |
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| Authors: | Du Runlin Liu Zhan |
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| Affiliation: | 1. School of Geosciences, China University of Petroleum(East China), Qingdao 266555, Shandong, China;
2. The Key Laboratory of Marine Hydrocarbon Resources and Environment Geology, Qingdao Institute of Marine Geology, Qingdao 266071, Shandong, China |
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| Abstract: | Cellular neural network is an effective method for extracting useful anomalies with a strong capability of horizontal resolution.Its key point is to optimize the template when using cellular neural network to extract hydrocarbon gravity anomaly information. We presented an improved algorithm based on the characteristics of the cellular neural network and the particle swarm optimization,joined constraint condition in template,and introduced the shrinkage factors in particle swarm optimization individual characteristics through the analysis of the dynamic performance of cellular neural network.Through the model testing and the actual data processing,the algorithm proved to be more stable to optimize the template for extracting reservoir gravity anomaly information. |
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| Keywords: | cellular neural network particle swarm optimization gravity anomaly shrinkage factors petroleum gas |
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