| 基于大数据挖掘的地震前兆台网观测数据跟踪分析 |
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| 引用本文： | 尹晶飞, 张明, 沈钰, 严俊峰, 徐梦林. 基于决策树算法的水位观测干扰识别模型[J]. 国际地震动态 , 2019, (11): 27-34. DOI: 10.3969/j.issn.0253-4975.2019.11.005 |
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| 作者姓名： | 尹晶飞 张明 沈钰 严俊峰 徐梦林 |
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| 作者单位： | 浙江省地震局，浙江 310013 |
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| 基金项目： | 浙江省地震局局科技项目(2018zjj07)资助。 |
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| 摘 要： | 为提高地下水位观测数据中干扰事件的识别效率，利用决策树算法对宝坻等5个台站近5年的水位观测数据进行样本训练和数据验证。结果表明，决策树算法对观测系统干扰和场地环境干扰事件的分类准确率在80%以上。在大量准确的训练样本基础上，决策树算法对于各种水位干扰事件具有良好的识别效果。
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| 关 键 词： | 水位观测 决策树 干扰识别 |
| 收稿时间： | 2018-09-18 |
| 修稿时间： | 2018-12-04 |
Convolutional neural network for earthquake detection and location |
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| Jingfei Yin, Ming Zhang, Yu Shen, Junfeng Yan, Menglin Xu. Groundwater observation interference recognition model based on decision tree algorithm[J]. Progress in Earthquake Sciences, 2019, (11): 27-34. DOI: 10.3969/j.issn.0253-4975.2019.11.005 |
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| Authors: | Jingfei Yin Ming Zhang Yu Shen Junfeng Yan Menglin Xu |
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| Affiliation: | Zhejiang Earthquake Agency，Hangzhou 310013，China |
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| Abstract: | To improve the identification efficiency of disturbance events in groundwater observation data, decision tree algorithm is used to perform sample training and data verification for groundwater data of Baodi and other four stations in recent five years. The results show that the classification accuracy of the decision tree algorithm for observing system interference and environmental interference events is above 80%. Based on a large number of accurate training samples, the decision tree algorithm can identify various water level interference events efficiently. |
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| Keywords: | groundwater observation decision tree interference identification |
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