| 基于支持向量机的水库诱发地震分析 |
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| 引用本文： | 魏海,陶开云,王新,陈丹蕾,廖敏.基于支持向量机的水库诱发地震分析[J].水文地质工程地质,2017,0(6):135-135. |
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| 作者姓名： | 魏海 陶开云 王新 陈丹蕾 廖敏 |
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| 作者单位： | 昆明理工大学电力工程学院，云南 昆明650500 |
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| 基金项目： | 云南省应用基础研究基金资助项目（KKSY20140426） |
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| 摘 要： | 将诱发水库地震的主要因素（岩性、岩体完整性、断层性质、库区区域应力状态、库区地震活动背景）划分为11个因子，并进行定量化；再根据每个样本到所属类内超平面的距离计算每个样本点的模糊因子，确定其对分类超平面影响大小；然后建立水库地震的支持向量机（SVM）和模糊支持向量机（FSVM）模型，并应用于水库诱发地震等级预测。实例分析表明，两种模型均可用于水库诱发地震等级预测，具有预测精度较高、考虑因素全面的特点，相比之下SVM模型预测结果略优于FSVM模型。另外，在应用SVM和FSVM进行分类时，如果样本离散性较高，则SVM模型优于FSVM模型；相反，如果样本离散性较低，则FSVM模型优于SVM模型。
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| 关 键 词： | 水库诱发地震 支持向量机 定量化 地震等级预测 |
| 收稿时间： | 2016-12-25 |
| 修稿时间： | 2017-03-17 |
An analysis of the reservoir induced earthquake based on Support Vector Machines |
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| WEI Hai,TAO Kaiyun,WANG Xin,CHEN Danlei,LIAO Min.An analysis of the reservoir induced earthquake based on Support Vector Machines[J].Hydrogeology and Engineering Geology,2017,0(6):135-135. |
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| Authors: | WEI Hai TAO Kaiyun WANG Xin CHEN Danlei LIAO Min |
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| Affiliation: | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, Yunnan650500, China |
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| Abstract: | The main factors inducing reservoir earthquakes, including lithology, rock mass integrity, fault property, tectonic stress state and seismic activity background in reservoir area, are divided into 11 factors and are quantified. Fuzzy factor of each sample reflecting the effect of the sample on this hyperplane was calculated based on the distance to the hyperplane of each class samples. The Fuzzy Support Vector Machines (FSVM) and Support Vector Machines (SVM) are used to establish the classifier of the induced earthquake, and to predict the magnitude of the reservoir induced earthquake (RIE). The cases analysis shows that FSVM and SVM models can be employed to predict the magnitude of RIE with high precision and over-all consideration. The SVM model are slightly superior to FSVM in the field of RIE prediction. Furthermore, when SVM and FSVM model are applied to classify samples, the SVM model is superior to FSVM if samples are with high discreteness. On the contrary, the FSVM model is superior to SVM if samples are with low discreteness. |
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