| PSO-LIBSVM在钾盐矿层识别中的应用研究 |
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| 引用本文： | 杨福强,陈科贵,黄长兵,陈愿愿,李进,马小林.PSO-LIBSVM在钾盐矿层识别中的应用研究[J].地球科学进展,2019(7):757-764. |
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| 作者姓名： | 杨福强 陈科贵 黄长兵 陈愿愿 李进 马小林 |
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| 作者单位： | 西南石油大学地球科学与技术学院;中国石油化工集团中原油田;川庆钻探工程有限公司地球物理勘探公司 |
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| 基金项目： | 国家自然科学基金项目“四川盆地油钾兼探的地球物理评价方法研究”(编号：41372103)资助~~ |
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| 摘 要： | 钾盐的紧缺严重制约了中国农业的发展,加大钾盐的勘探开发力度有助于提高我国钾盐的自给自足能力。四川盆地钾盐资源丰富,是我国目前重要的钾盐勘探开发研究区域之一。杂卤石作为四川盆地最重要的固态钾盐矿物,常夹杂在硬石膏、岩盐和白云岩等岩层中。针对常规测井解释方法难以精确识别杂卤石的问题,因此,提出一种新的基于粒子群算法(PSO)优化的支持向量机(SVM)杂卤石识别方法开展四川盆地杂卤石的分类识别研究。以PSO和SVM理论为基础,结合测井解释方法,选择对杂卤石测井响应灵敏的有效数据作为输入样本,随机产生训练集和测试集,并采用PSO优选出径向基核函数参数,建立杂卤石分类预测模型。与录井结果对比,基于PSO的SVM模型识别准确率达到了97.5758%,在识别精度和速度上明显优于交叉验证方法优化的SVM模型。结果表明,该模型在四川盆地钾盐勘探中具有广阔的应用前景。
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| 关 键 词： | 四川盆地 测井数据 粒子群算法 支持向量机 杂卤石识别 |
Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits |
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| Yang Fuqiang,Chen Kegui,Huang Changbing,Chen Yuanyuan,Li Jin,Ma Xiaolin.Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits[J].Advance in Earth Sciences,2019(7):757-764. |
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| Authors: | Yang Fuqiang Chen Kegui Huang Changbing Chen Yuanyuan Li Jin Ma Xiaolin |
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| Institution: | (School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;Sinopec Group Zhongyuan Oilfield, Henan Puyarjg 457000, China;Geophysical Exploration Company, Chuanqing Drilling Engineering Company Limited, Chengdu 610213, China) |
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| Abstract: | The shortage of potassium salt seriously restricts the development of China’s agriculture.Increasing the exploration and development of potash will help improve the self-sufficiency of potassium in China. With rich potassium salt resources,Sichuan basin is one of the most important research areas for potash exploration and development in China. Polyhalite is an important solid potassium salt mineral in Sichuan basin,often intercalated in rock minerals such as anhydrite,rock salt and dolomite. Aiming at the problem that conventional log interpretation methods are difficult to accurately identify polyhalites,this paper proposed a new Support Vector Machine(SVM)recognition method based on Particle Swarm Optimization(PSO)to classify polyhalites in Sichuan basin. Based on particle swarm optimization and support vector machine theory,combined with logging interpretation theory,the effective data sensitive to polyhalite logging response were selected as input samples to generate training sets and test sets randomly. The Radial Basis Function(RBF)parameters were optimized by particle swarm optimization,and the classification and prediction model of polyhalite was established. Compared with mud logging results,the recognition accuracy of SVM model based on particle swarm optimization reached 97.5758%,which is obviously better than that of SVM model optimized by cross validation method in recognition accuracy and speed. The results show that the model has broad application prospects in potash exploration in Sichuan basin. |
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| Keywords: | Sichuan Basin Well logging data Particle swarm optimization Support vector machine The identification of Polyhalite |
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