# 尼泊尔地震的NOAA卫星数据震前异常分析

1. 福建师范大学数学与信息学院,福州 350000
2. 福建农林大学计算机与信息学院,福州 350000
3. 中国地震局地震预测研究所,北京 100036

# Pre-earthquake Anomaly Data Mining of Remote Sensing OLR in Nepal Earthquake

LIN Ling1*, KONG Xiangzeng1, LI Nan2, XIONG Pan3

1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350000, China
2. College of Computer and Information Sciences, Fujian Agriculture and Forestry University,Fuzhou 350000, China
3. Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China

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.

Keywords： OLR ; Martingale theory ; data mining ; Nepal earthquake ; anomaly detection

0

LIN Ling, KONG Xiangzeng, LI Nan, XIONG Pan. Pre-earthquake Anomaly Data Mining of Remote Sensing OLR in Nepal Earthquake[J]. Journal of Geo-information Science, 2018, 20(8): 1169-1177 https://doi.org/10.12082/dqxxkx.2018.170567

## 2 数据源与处理

Fig. 1   Diagram of earth's Outgoing Longwave Radiation (OLR) before earthquake

Tab. 1   List of Nepal earthquakes from May 2015 to November 2014

12014-12-1815:32:1229 km SSE of Zuobude,中国27.74 °N, 86.37° E5.033.6
22015-04-2506:11:2536 km E of Khudi,尼泊尔28.23 °N, 84.73 °E7.88.2
32015-05-1207:05:1919 km SE of Kodari,尼泊尔27.81 °N, 86.07 °E7.315

Fig. 2   Diagram of grid map

## 3 算法分析

$di=dP,oi=oi-m$（1）

$pˆi,kPn,θn=⋕j|dj>di+θi,k⋕j|dj=dii$（2）

$θi,k=0.01×k-1+0.0001×100-k,k≤501-0.01×k-50-1+0.0001×100-k-50,50（3）

$Mn=∏i-1n(εpˆi,kε-1)$（4）

$CDn(ε)=∑k=1100∏i-1n(εpˆi,kε-1)100$（5）

Fig. 3   Flowchart for the algorithm

## 4 结果与分析

OLR数据在尼泊尔地区的时间2014年9月28日到2015年7月25日的波动原始数据图,如图4（a）所示。从温度变化数据曲线的振动频率上可以看到,2月25日之前的信号波动频率相对比较低,从2月25日开始振动频率逐渐变大,总的振幅也有所增高。通过采用基于鞅理论的算法对原始信号分析与处理后得到的CD值波动曲线图（图4（b）,曲线走势更清晰地展示了异常信息的变化情况。图4（b）中的3条竖线,分别表示在此期间的3次地震的时间,容易看出在3次地震的时间或附近均出现了震前异常信号增加的情况,与地震发生的时间相吻合。

Fig. 4   The original NOAA data graph and the wave diagram of the $CD$ values

Fig. 5   The original NOAA data and $CD$ values in Wenchuan and Lushan area

### 4.2 地域相关性分析

Fig. 6   Abnormal analysis of pre-earthquake and post-earthquake

## 5 结论与讨论

The authors have declared that no competing interests exist.

## 参考文献 原文顺序 文献年度倒序 文中引用次数倒序 被引期刊影响因子

 [1] Bouchon M, Karabulut H, Aktar M, et al.Extended nucleation of the 1999 Mw 7.6 Izmit earthquake[J]. Science, 2011,331(6019):877-880. [2] Cervone G, Kafatos M, Napoletani D, et al.An early warning system for coastal earthquakes[J]. Advances in Space Research, 2006,37(4):636-642. Earthquakes are very common natural hazards, and every year a few damaging events occur throughout the globe. Satellite remote sensing data are found to be useful in providing information about changes in land, ocean, atmosphere and ionosphere. Recent studies have shown that some of the parameters observed from the remote sensing data are associated with impending coastal earthquakes. An automatic system “CQuake” has been developed to carry out spatial and temporal data mining analysis in real time. CQuake performs retrospective analysis of earthquakes, and performs forecast for predefined regions of the world based on the analysis of Surface latent heat flux (SLHF) or any other geophysical parameters. Details of CQuake, and an example of the Colima earthquake of January 22, 2003, are discussed. [3] Kong X Z, Bi Y X, Glass D, Detecting seismic anomalies in outgoing Long-Wave Radiation data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,8(2):649-660. In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and postoccurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data. [4] 郭晓,张元生,魏从信,等.汶川8.0级和仲巴6.8级地震中波红外热辐射异常[J].地球学报,2014,35(3):338-344. 本文以静止卫星中波红外亮温为数据源，应用小波变换和计算功率谱方法对汶川8.0级地震和仲 巴6.8级地震进行了分析研究。结果表明，这两次地震震前均出现了明显的短临热异常现象。这与长波辐射通量和热红外亮温资料的研究结果基本一致。在时间演 化过程中热异常现象在震前存在一次明显的变化，这种变化有短临预测意义。地震前后热异常分布可能反映了区域应力集中和调整变化，根据异常的演化方向和异常 消失区域可以估计发震的可能区域范围。 [ Guo X, Zhang Y S, Wei C X, et al.Medium wave infrared brightness anomalies of Wenchuan 8.0 and Zhongba 6.8 earthquakes[J]. Acta Geoscientica Sinica, 2014,35(3):338-344. ] 本文以静止卫星中波红外亮温为数据源，应用小波变换和计算功率谱方法对汶川8.0级地震和仲 巴6.8级地震进行了分析研究。结果表明，这两次地震震前均出现了明显的短临热异常现象。这与长波辐射通量和热红外亮温资料的研究结果基本一致。在时间演 化过程中热异常现象在震前存在一次明显的变化，这种变化有短临预测意义。地震前后热异常分布可能反映了区域应力集中和调整变化，根据异常的演化方向和异常 消失区域可以估计发震的可能区域范围。 [5] 张元生,郭晓,钟美娇,等.汶川地震卫星热红外亮温变化[J].科学通报,2010,55(10):900-906. [ Zhang Y S, Guo X, Zhong M J, et al.Wenchuan earthquake: Brightness temperature changes from satellite infrared information[J]. Chinese Science Bulletin, 2010,55(10):900-906. ] [6] Saraf A K, Choudhury S.Cover: NOAA-AVHRR detects thermal anomaly associated with the 26 January 2001 Bhuj earthquake, Gujarat, India[J]. International Journal of Remote Sensing, 2005,26(6):1065-1074. [7] Ouzounov D, Bryant N, Logan T, et al.Satellite thermal IR phenomena associated with some of the major earthquakes in 1999-2003[J]. Physics and Chemistry of the Earth, 2006,31(4-9):154-163. Satellite thermal infrared (TIR) imaging data have recorded short-lived anomalies prior to major earthquakes and associations with fault systems. Others have proposed that these signals originate from electromagnetic phenomena associated with pre-seismic processes, causing enhanced IR emissions, that we are calling TIR anomalies. The purpose of this exploratory study is to verify if TIR anomalies can be found in association with known earthquakes by systematically applying satellite data analysis techniques to imagery recorded prior-to and immediately after large earthquakes. Our approach utilizes both a mapping of surface TIR transient fields from polar orbiting satellites and co-registering geosynchronous weather satellites images. The significance of these observations was explored using data sets of recent worldwide strong earthquakes (1999 2003) and the techniques used to capture the trace of TIR anomalies. [8] Tramutoli V, Cuomo V, Filizzola C, et al.Assessing the potential of thermal infrared satellite surveys for monitoring seismically active areas: The case of Kocaeli (İzmit) earthquake, August 17, 1999[J]. Remote Sensing of Environment, 2005,96(3-4):409-426. [9] Selva J, Marzocchi W, Papale P, et al.Operational eruption forecasting at high-risk volcanoes: the case of Campi Flegrei, Naples[J]. Journal of Applied Volcanology, 2012,1(1):1-14. [10] Xiong P, Shen X H, Bi Y X, et al.Study of outgoing longwave radiation anomalies associated with Haiti earthquake[J]. Natural Hazards and Earth System Science, 2010(10):2169-2178. The paper presents an analysis by using the methods of Eddy field calculation mean and wavelet maxima to detect seismic anomalies within the outgoing longwave radiation (OLR) data based on time and space. The distinguishing feature of the method of Eddy field calculation mean is that we can calculate "the total sum of the difference value" of "the measured value" between adjacent points, which could highlight the singularity within data. The identified singularities are further validated by wavelet maxima, which using wavelet transformations as data mining tools by computing the maxima that can be used to identify obvious anomalies within OLR data. The two methods has been applied to carry out a comparative analysis of OLR data associated with the earthquake recently occurred in Haiti on 12 January 2010. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, the analyzed results have indicated a number of singularities associated with the possible seismic anomalies of the earthquake and from the comparative experiments and analyses by using the two methods, which follow the same time and space, we conclude that the singularities observed from 19 to 24 December 2009 could be the earthquake precursor of Haiti earthquake. [11] 刘德富,康春丽.地球长波辐射(OLR)遥感与重大自然灾害预测[J].地学前缘,2003,10(2):427-430. 针对目前重大自然灾害(如地震、洪水泛滥、火山喷发等)还难能作出短期预测预警的现实状况, 文中提出利用极轨卫星遥感所监测的"地-气"系统射出长波辐射(即OLR)信息,是可能攻克这一难关的突破口.文中不仅详细介绍了有关OLR数据的产出原 理及其载荷卫星(NOAA)运行的主要参数,而且还结合近年来发生的重大自然灾害实例,利用笔者研究的应用OLR提取灾前预测信息的3种方法,客观地给出 了OLR时空变化图像.结果显示,在重大自然灾害事件发生前,在未来可能发生重灾的区域及其附近,OLR呈现出比周围区域更显著的辐射增强变化特征.这一 特征的揭示,为利用卫星遥感技术预测未来可能发灾区域及时作出短期预测预警提供了一种新的途径.文末对目前有关强震前热红外异常成因的两种看法做了综合介 绍和简要评述. [ Liu D F, Kang L C.Predicting heavy disasters by outgoing longwave radiation (OLR) of the earth[J]. Earth Science Frontiers, 2003,10(2):427-430. ] 针对目前重大自然灾害(如地震、洪水泛滥、火山喷发等)还难能作出短期预测预警的现实状况, 文中提出利用极轨卫星遥感所监测的"地-气"系统射出长波辐射(即OLR)信息,是可能攻克这一难关的突破口.文中不仅详细介绍了有关OLR数据的产出原 理及其载荷卫星(NOAA)运行的主要参数,而且还结合近年来发生的重大自然灾害实例,利用笔者研究的应用OLR提取灾前预测信息的3种方法,客观地给出 了OLR时空变化图像.结果显示,在重大自然灾害事件发生前,在未来可能发生重灾的区域及其附近,OLR呈现出比周围区域更显著的辐射增强变化特征.这一 特征的揭示,为利用卫星遥感技术预测未来可能发灾区域及时作出短期预测预警提供了一种新的途径.文末对目前有关强震前热红外异常成因的两种看法做了综合介 绍和简要评述. [12] 张璇,张元生,郭晓,等.尼泊尔8.1级地震卫星热红外异常解析[J].地学前缘,2017,24(2):227-233. [ Zhang X, Zhang Y S, Guo X, et al.Analysis of thermal infrared anomaly in the Nepal MS 8.1 earthquake[J]. Earth Science Frontiers, 2017,24(2):227-233. ] [13] 卢显,孟庆岩,顾行发,等. 2014年玉树M_S7.1地震前后长波辐射及温度参量变化特征研究[J].地震,2016,36(3):144-151. 应用中国FY-2E气象卫星与NCEP数据,计算了玉树地震前后的长波辐射与地面温度变化,并结合玉树井水温和HJ-1B卫星地表温度的结果探讨了各参量的变化特征。研究结果表明,几项参量均在玉树地震前出现了数值增高的现象。除了玉树井水温,其他几项参量的数值增高区均在临近震中的南部区域。同时,各参量增强特征出现的时间顺序与参量的内在物理属性一致,进一步证实了地震构造运动所产生的热量传递是由陆地深部传向地表再传递到大气的过程。 [ Lu X, Meng Q Y, Gu X F, et al.Variation characteristics of long wave radiation and temperature parameters before and after the Yushu earthquake[J]. Earthquake, 2016,36(3):144-151. ] 应用中国FY-2E气象卫星与NCEP数据,计算了玉树地震前后的长波辐射与地面温度变化,并结合玉树井水温和HJ-1B卫星地表温度的结果探讨了各参量的变化特征。研究结果表明,几项参量均在玉树地震前出现了数值增高的现象。除了玉树井水温,其他几项参量的数值增高区均在临近震中的南部区域。同时,各参量增强特征出现的时间顺序与参量的内在物理属性一致,进一步证实了地震构造运动所产生的热量传递是由陆地深部传向地表再传递到大气的过程。 [14] Qin K, Wu L.X, Liu S J. A Deviation-Time-Space-Thermal (DTS-T) method for global earth observation system of systems (GEOSS)-based earthquake anomaly recognition: criterions and quantify indices[J]. Remote Sensing, 2013,5(10):5143-5151. [15] Wu L X, Qin K, Liu S J.GEOSS-Based Thermal Parameters Analysis for Earthquake Anomaly Recognition[J], Proceedings of IEEE, 2012,100(10):2891-2907. [16] 崔月菊,李静,王燕艳,等.遥感气体探测技术在地震监测中的应用[J].地球科学进展,2015,30(2):284-294.

[ Cui Y J, Li J, Wang Y Y, et al.Application of gas remote sensing technique to earthquake monitoring[J]. Advance in Earth Sciences, 2015,30(2):284-294. ]