| 整点气温缺测的插补方法研究及其初步应用 |
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| 引用本文： | 闫丽莉,温少妍,高文晶,刘传军,杨甜.整点气温缺测的插补方法研究及其初步应用[J].震灾防御技术,2019,14(2):446-455. |
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| 作者姓名： | 闫丽莉 温少妍 高文晶 刘传军 杨甜 |
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| 作者单位： | 1.天津市地震局, 天津 300201 |
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| 基金项目： | 中国地震局三结合课题“气温在地震监测中的初步应用”（2018008），天津市地震局青年基金“缺测整点气温的插补方法研究及初步应用”（20141021），新疆地震局基金项目（201802） |
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| 摘 要： | 长期连续完整的历史气温资料是震前气温异常判别研究的重要数据基础。本文考虑了参考站与缺测站之间的距离，建立改进的线性回归模型。利用该模型插补缺测和错误的气温整点值数据，在一定程度上解决了长期连续观测数据缺测的情况。通过对收集的唐山观测站气温整点值数据进行插补，并应用插补完整的数据分析研究了2012年5月28日唐山4.8级地震前兆异常。结果表明:①插补值与其前后观测值衔接吻合，插补后完整连续数据符合夏高冬低的年变规律；②插补误差在±0.5℃范围内的比例为60.2%，在±0.8℃范围内的比例为80.3%，其误差绝对值大于1.0℃的比例为9.6%，平均绝对误差为0.84℃，插补值与观测值的相关系数大部分在0.9以上；③从3月27日起出现增温异常，特别是震前2天增温幅度约8℃。
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| 关 键 词： | 整点气温 插补 线性回归 地震 |
| 收稿时间： | 2018/9/17 0:00:00 |
Interpolating Method for Missing Data of Integral Point Temperature and Its Preliminary Application |
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| Yan Lili,Wen Shaoyan,Gao Wenjing,Liu Chuanjun and Yang Tian.Interpolating Method for Missing Data of Integral Point Temperature and Its Preliminary Application[J].Technology for Earthquake Disaster Prevention,2019,14(2):446-455. |
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| Authors: | Yan Lili Wen Shaoyan Gao Wenjing Liu Chuanjun and Yang Tian |
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| Institution: | 1.Tianjin Earthquake Agency, Tianjin 300201, China2.Earthquake Agency of Xinjiang Uygur Autonomous Region, Urumqi 830011, China3.Daixian Central Seismological Station of Shanxi Earthquake Agency, Xinzhou 034200, Shanxi, China |
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| Abstract: | The analysis of the temperature anomaly before earthquakes is based on the continuously historical air temperature data. The improved linear regression model is established in this paper in consideration of the distance of reference sites and missing observation sites. This model is used to interpolate hourly temperature data of missing and incorrect observations, which can partially help to solve the problem of long-term continuous observations data missing. The observation data from 15 sites were interpolated with the improved linear regression method and the interpolated data were applied to the Tangshan MS 4.8 earthquake that occurred on May 28, 2012. Our results suggest that: ①The interpolated data are consistent with its pre and post observation and the complete temperature data have the annual variation characteristic of higher in summer and lower in winter; ②The probability of error in temperature range ±0.5℃ is about 60.2%, and the error in ±0.8℃ is about 80.3%. The absolute error over 1.0℃ is 9.6% and the mean absolute error is 0.84℃. The correlation coefficients between interpolated and observed data are generally greater than 0.9; ③The complete interpolated temperature data of Tangshan site were applied to study the Tangshan earthquake, 2012. The results show that the temperature increasing anomaly was found on May 10, 2012, when the temperature increased 8℃ two days before the quake. |
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| Keywords: | Integral point temperature Interpolating Linear regression model Earthquake |
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