| 2020年7月12日唐山5.1级地震前高光谱多参数异常特征分析 |
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| 引用本文： | 丁志华,崔月菊,唐杰.2020年7月12日唐山5.1级地震前高光谱多参数异常特征分析[J].中国地震,2022,38(3):494-502. |
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| 作者姓名： | 丁志华 崔月菊 唐杰 |
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| 作者单位： | 河北省地震局, 石家庄 050021;中国地震局地震预测研究所, 地震预测重点实验室, 北京 100036 |
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| 基金项目： | 国家重点研发计划专题“星地联合的近地表气体背景场构建与地震异常识别”(2019YFC1509203-6)、河北省地震局科技星火计划项目(DZ202108090107、DZ20190415003、DZ20180322026、DZ20160406028)共同资助 |
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| 摘 要： | 基于高光谱遥感数据,通过RST算法提取2020年7月12日唐山5.1级地震前不同参数异常信息,发现震前CH4、CO、H2O、O3均出现了高值异常,且异常均位于震中附近,异常幅度高于2倍均方差,CH4、CO、H2O异常机理不同于O3。通过统计2018—2020年CH4、CO、H2O三种气体出现频次,发现同步异常具有可靠性,认为高光谱多参数异常出现的同步性,可提高异常性质判定的信度。通过对不同参数进行连续跟踪观测,提取多参数同步异常信息,对地震短临预报具有一定参考意义。
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| 关 键 词： | 高光谱 多参数 异常提取 RST算法 |
| 收稿时间： | 2020/12/27 0:00:00 |
| 修稿时间： | 2021/8/11 0:00:00 |
The Abnormal Characteristics of Hyperspectral Multi-parameter before the Tangshan M5.1 Earthquake on July 12,2020 |
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| Ding Zhihu,Cui Yueju,Tang Jie.The Abnormal Characteristics of Hyperspectral Multi-parameter before the Tangshan M5.1 Earthquake on July 12,2020[J].Earthquake Research in China,2022,38(3):494-502. |
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| Authors: | Ding Zhihu Cui Yueju Tang Jie |
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| Institution: | Hebei Earthquake Agency, Shijiazhuang 050021, China;Key Laboratory of Earthquake Prediction, Institute of Earthquake Forecasting, CEA, Beijing 100036, China |
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| Abstract: | By extracting the abnormal information from satellite hyperspectral data before the Tangshan M5.1 earthquake on July 12,2020,we found that overall CH4, CO, H2O, and O3 had high-value anomalies before the earthquake. The anomalies are all found near the epicenter,and the anomaly amplitudes are 2 times higher than the mean square error. The abnormal mechanism of CH4,CO and H2O is different from that of O3. Based on statistics of the frequency of occurrence of the three gases CH4,CO,and H2O from 2018 to 2020,we believe that the synchronization anomaly is reliable. The synchronization of hyperspectral multi-parameter abnormality can improve the reliability of abnormality determination. Through continuous tracking of different parameters,it has certain reference significance for short-term earthquake prediction. |
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| Keywords: | Hyperspectral Multiple parameters Anomaly extraction RST algorithm |
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