| 尼泊尔地震的NOAA卫星数据震前异常分析 |
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| 引用本文： | 林岭,孔祥增,李南,熊攀.尼泊尔地震的NOAA卫星数据震前异常分析[J].地球信息科学,2018,20(8):1169-1177. |
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| 作者姓名： | 林岭 孔祥增 李南 熊攀 |
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| 作者单位： | 1. 福建师范大学数学与信息学院,福州 3500002. 福建农林大学计算机与信息学院,福州 3500003. 中国地震局地震预测研究所,北京 100036 |
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| 基金项目： | 福建省引导性项目（2015Y0054）;国家自然科学基金青年项目（41601477）;国家自然科学基金项目（61361136002、61772004） |
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| 摘 要： | 发生地震时常伴随有地热辐射增强现象,这些异常信息隐藏于遥感卫星NOAA所捕获的地球射出长波辐射（OLR）数据中,目前多数研究还停留在对源数据的解读,缺乏有效的信息处理技术,致使大部分的OLR数据没有得到充分利用。因此,本文提出一种基于鞅理论的概率统计方法来识别异常特征算法,从有噪声的非结构化的源数据中提取出震前OLR数据异常变化特征序列,从时间序列和地域范围进行震前异常分析。本算法实验是以尼泊尔的在2014年9月至2015年7月期间（包括2015年4月25日Ms7.8大地震）发生的3次地震的OLR数据为例,实验结果显示震中区域的OLR数据在震前的2~3个月开始有显著的异常数据变化,通过分析发现数据显示数据异常在震前出现的时间长短与地震的大小相关,异常的发生区域与震区周围的地貌特征相关。这说明基于鞅理论的算法所提取的OLR数据异常点序列的确与地震发生的时间地点是有关系的。
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| 关 键 词： | 射出长波辐射 鞅理论 数据挖掘 尼泊尔地震 异常提取 |
| 收稿时间： | 2017-11-28 |
Pre-earthquake Anomaly Data Mining of Remote Sensing OLR in Nepal Earthquake |
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| LIN Ling,KONG Xiangzeng,LI Nan,XIONG Pan.Pre-earthquake Anomaly Data Mining of Remote Sensing OLR in Nepal Earthquake[J].Geo-information Science,2018,20(8):1169-1177. |
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| Authors: | LIN Ling KONG Xiangzeng LI Nan XIONG Pan |
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| Institution: | 1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350000, China2. College of Computer and Information Sciences, Fujian Agriculture and Forestry University,Fuzhou 350000, China3. Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China |
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| Abstract: | A number of researches have shown that the occurrence of earthquakes is often accompanied by abnormal warming of infrared radiation data, which is hidden in the Outgoing Long-Wave Radiation (OLR) data, which has been captured by the NOAA remote sensing satellite. These abnormal signals are embedded in a large amount of normal information and cannot be recognized by human eyes or some common methods. Many scholars utilized different means to analyze the anomaly of infrared remote sensing data. However, there were still lack of any effective processing techniques and algorithms, and most of the thermal infrared satellite remote sensing data weren't fully utilized. In this work, we propose a data mining algorithm, which is based on the anomaly features of the martingale theory. The algorithm first calculates the distance between a sample point and the cluster, and then determines whether the measured point is abnormal according to a comprehensive operation of the number of abnormal points nearest to each point, and calculates the whole event sequence data changing trend based on the martingale theory. The martingale value (i.e. CD value) corresponding to each original point is obtained, so that the original data is stripped out, the noise and the normal data are obtained, and the anomaly is analyzed before the earthquake. The OLR data sources used in the experiments on this algorithm were from three earthquakes happened in Nepal between September 2014 and July 2015 (including the Ms7.8 earthquake in April 25, 2015). We found that the CD value of the OLR data about the epicenter region began to have significant temporal correlation characteristics of anomalous data changes as early as 2 or 3 months before the earthquake. The results of this research were similar to the comparison of the OLR original and CD values of the Wenchuan and Lushan earthquakes. In this paper, we analyzed the anomaly of the three earthquakes one month before and some two weeks after the earthquakes. The experimental results show that when the earthquake is larger, and the anomaly CD value occurs earlier. In conclusion, the more obvious the anomaly is, the closer the region is to the epicenter or fault zone, the farther from the epicenter, the weaker and appeared later the abnormal signal. |
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| Keywords: | OLR Martingale theory data mining Nepal earthquake anomaly detection |
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