基于量子漫步算法的地震震前异常挖掘
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摘要
地震特别是大震前会产生一些异常,但这些异常信息难以识别,导致无法充分利用这些异常信息预测地震的发生时间,减少地震带来的灾害影响.针对这个问题,提出一种基于量子漫步算法的震前异常挖掘方法,提取汶川地震和芦山地震的震前射出长波辐射(Outgoing Long-wave Radiation, OLR)异常,进而计算地震前后的P值,异常值CD等数据,通过统计分析方法,探索OLR异常与地震的关系.并且通过实验将该算法扩展到最近十年左右全球发生的8.0级及以上地震,验证该算法的有效性.实验结果表明,该算法能够有效的反映在地震前后会出现OLR异常,而且越大的地震异常越明显.因此,该算法适用于震前异常挖掘.
There are some anomalies before the earthquake, especially the large earthquake. However, such abnormal information is too difficult to identify. Therefore, we cannot make full use of the abnormal information to predict the occurrence time of the earthquake in order to reduce the impact of the earthquake. To solve this problem, an anomaly mining method before earthquake based on the quantum walk algorithm is proposed to extract seismic Outgoing Longwave Radiation(OLR) anomalies before the Wenchuan earthquake and the Lushan earthquake. Then, calculate the P value, anomaly value CD before and after the earthquake. Through statistical analysis method, the relationship between OLR anomalies and earthquake is explored. What is more, the algorithm is extended to the 8.0 magnitude and above earthquakes in the nearly last ten years. Through experiments, the effectiveness of the algorithm is verified. The experimental results show that the algorithm can effectively reflect the anomalies before and after the earthquake, and the larger the earthquake is, the more obvious anomaly is. Therefore, this algorithm is suitable for pre-earthquake anomaly excavation.
There are some anomalies before the earthquake, especially the large earthquake. However, such abnormal information is too difficult to identify. Therefore, we cannot make full use of the abnormal information to predict the occurrence time of the earthquake in order to reduce the impact of the earthquake. To solve this problem, an anomaly mining method before earthquake based on the quantum walk algorithm is proposed to extract seismic Outgoing Longwave Radiation(OLR) anomalies before the Wenchuan earthquake and the Lushan earthquake. Then, calculate the P value, anomaly value CD before and after the earthquake. Through statistical analysis method, the relationship between OLR anomalies and earthquake is explored. What is more, the algorithm is extended to the 8.0 magnitude and above earthquakes in the nearly last ten years. Through experiments, the effectiveness of the algorithm is verified. The experimental results show that the algorithm can effectively reflect the anomalies before and after the earthquake, and the larger the earthquake is, the more obvious anomaly is. Therefore, this algorithm is suitable for pre-earthquake anomaly excavation.
引文
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10李正媛,陈晶,王丽娜,等.一种基于误差和关键点的地震前兆观测数据异常挖掘算法.计算机应用研究,2011,28(8):2987-2901.[doi:10.3969/j.issn.1001-3695.2011.08.051]
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12 Wang T,Bebbington M.Identifying anomalous signals in GPS data using HMMs:An increased likelihood of earthquakes?Comptutational Statistics and Data Analysis,2013,58(1):27-44.
13 Cervone G,Kafatos M,Napoletani D,et al.Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes.Natural Hazards and Earth System Science,2004,4(3):359-374.[doi:10.5194/nhess-4-359-2004]
14 Marzocchi W,Zechar JD,Jordan TH.Bayesian forecast evaluation and ensemble earthquake forecasting.Bulletin of the Seismological Society of America,2012,102(6):2574-2584.[doi:10.1785/0120110327]
15钱国红.量子算法及其在数据挖掘中的应用[硕士学位论文].杭州:浙江工业大学,2012.15-18.
16 Ouzounov D,Bryant N,Logan T,et al.Satellite thermal IRphenomena associated with some of the major earthquakes in1999-2003.Physics and Chemistry of the Earth,2006,31(4-9):154-163.[doi:10.1016/j.pce.2006.02.036]
17 Ho SS,Wechsler H.A martingale framework for detecting changes in data streams by testing exchangeability.IEEETransactions on Pattern Analysis and Machine Intelligence,2010,32(12):2113-2127.
18 Nayak A,Vishwanath A.Quantum walk on the line.DIMACS Technical Report.2000.arXiv:quant-ph/0010117.
19 Vork V,Nouretdinov I,Gammerman A.Testing exchangeability on-line.Proceedings of the 12th International Conference on Machine Learning.Washington,DC,USA.2003.768-775.
2 Honkura Y,Oshiman N,Matsushima M,et al.Rapid changes in the electrical state of the 1999 Izmit earthquake rupture zone.Nature Communications,2013,4:2116.[doi:10.1038/ncomms3116]
3 Němec F,Santolík O,Parrot M.Decrease of intensity of ELF/VLF waves observed in the upper ionosphere close to earthquakes:A statistical study.Journal of Geophysical Research,2009,114(4):A04303.
4张元生,郭晓,钟美娇,等.汶川地震卫星热红外亮温变化.科学通报,2010,55(10):904-910.
5 Kong XZ,Bi YX,Glass DH.Detecting seismic anomalies in outgoing long-wave radiation data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(2):649-660.[doi:10.1109/JSTARS.2014.2363473]
6 Yeh YL,Cheng KC,Wang WH,et al.Very short-term earthquake precursors from GPS signal interference based on the 2013 Nantou and Rueisuei earthquakes,Taiwan.Journal of Asian Earth Sciences,2015,114(2):312-320.
7 Kuo CL,Lee LC,Heki K.Preseismic TEC changes for Tohoku-Oki earthquake:Comparisons between simulations and observations.Terrestrial,Atmospheric and Oceanic Sciences,2015,26(1):63-72.
8 Xiong P,Shen XH,Bi YX,et al.Study of outgoing longwave radiation anomalies associated with Haiti earthquake.Natural Hazards and Earth System Science,2010,10(10):2169-2178.
9 Konstantaras A,Varley MR,Vallianatos F,et al.Detection of weak seismo-electric signals upon the recordings of the electrotelluric field by means of neuro-fuzzy technology.IEEE Geoscience and Remote Sensing Letters,2007,4(1):161-165.
10李正媛,陈晶,王丽娜,等.一种基于误差和关键点的地震前兆观测数据异常挖掘算法.计算机应用研究,2011,28(8):2987-2901.[doi:10.3969/j.issn.1001-3695.2011.08.051]
11徐秀登,强祖基,赁常恭.临震卫星热红外异常与地面增温异常.科学通报,1991,36(4):291-294.[doi:10.3321/j.issn:0023-074X.1991.04.009]
12 Wang T,Bebbington M.Identifying anomalous signals in GPS data using HMMs:An increased likelihood of earthquakes?Comptutational Statistics and Data Analysis,2013,58(1):27-44.
13 Cervone G,Kafatos M,Napoletani D,et al.Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes.Natural Hazards and Earth System Science,2004,4(3):359-374.[doi:10.5194/nhess-4-359-2004]
14 Marzocchi W,Zechar JD,Jordan TH.Bayesian forecast evaluation and ensemble earthquake forecasting.Bulletin of the Seismological Society of America,2012,102(6):2574-2584.[doi:10.1785/0120110327]
15钱国红.量子算法及其在数据挖掘中的应用[硕士学位论文].杭州:浙江工业大学,2012.15-18.
16 Ouzounov D,Bryant N,Logan T,et al.Satellite thermal IRphenomena associated with some of the major earthquakes in1999-2003.Physics and Chemistry of the Earth,2006,31(4-9):154-163.[doi:10.1016/j.pce.2006.02.036]
17 Ho SS,Wechsler H.A martingale framework for detecting changes in data streams by testing exchangeability.IEEETransactions on Pattern Analysis and Machine Intelligence,2010,32(12):2113-2127.
18 Nayak A,Vishwanath A.Quantum walk on the line.DIMACS Technical Report.2000.arXiv:quant-ph/0010117.
19 Vork V,Nouretdinov I,Gammerman A.Testing exchangeability on-line.Proceedings of the 12th International Conference on Machine Learning.Washington,DC,USA.2003.768-775.