2010年海地太子港地震触发滑坡危险性区划和合理性检验
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摘要
2010年1月12日海地Mw 7.0级地震触发了数以万计的滑坡。本文应用二元统计分析模型、地理信息系统与遥感技术,开展海地地震区滑坡危险性区划研究,并对结果合理性及模型预测功能进行检验。地震前后多源高分辨率遥感数据目视解译结果表明,海地地震触发了30828处滑坡。选择高程、坡度、坡向、曲率、与水系距离、地震动峰值加速度(PGA)、与恩里基约-芭蕉园断裂距离、沿恩里基约-芭蕉园断裂距离8个地震滑坡影响因子进行海地地震滑坡危险性区划。将这30828处滑坡随机分为训练样本与预测样本两类,分别进行模型的建立与测试,训练样本包括滑坡总数的70%(21579处滑坡,覆盖面积为11.18km2);预测样本包括滑坡总数的30%(9249处滑坡,覆盖面积为4.56km2)。基于滑坡训练样本、8个滑坡影响因子、权重系数模型(二元统计方法模型)和GIS技术,构建了滑坡危险性概率分布图。结果合理性检验表明了模型的正确率为84.966%,预测率为84.547%,两者都表明了实际滑坡位置与滑坡危险性结果图具有良好一致性。按照滑坡危险性索引值的大小,将研究区分为非常高、高、中等、低、非常低5类。本文证明了在地震滑坡危险性区划中,权重系数模型作为一种二元统计方法具有良好的建模与预测能力,可为海地地震灾区恢复重建中滑坡的防灾减灾工作提供参考。
Tens of thousands of landslides were triggered by the January 12,2010 Port-au-Prince,Haiti earthquake(M w 7.0).The main purpose of this study was to apply and verify the hazard mapping techniques of earthquake triggered landslide by Bivariate Statistics(BS) method,Geographical Information System(GIS) and remote sensing technologies in the Haiti earthquake stuck area.A total of 30828 landslides were delineated in the study area from visual interpretation of multi-source and high resolution remote sensing images preand post-earthquake.Eight factors,including elevation,slope angle,slope aspect,slope curvature,distance from drainages,Peak Ground Acceleration(PGA),distance from the Enriquillo-Plantain Garden Fault(EPGF) and distance along the Enriquillo-Plantain Garden Fault(EPGF) were selected as impact factors for the Haiti earthquake triggered landslide hazard mapping.The 30828 landslides were randomly partitioned into two subsets:A training dataset,which contained 70%(21579 landslides,with a total area of 11.18km 2),was used for building the model;and a testing dataset containing 30%(9249 landslides,with a total area of 4.56km 2) was used for model testing.Landslide hazard probability index map was then generated using the training dataset,the eight landslide impact factors,the weight index modeling,a BS method,and GIS technology.The validation results showed a success rate of 84.966% between the hazard probability index map and the training dataset.The predictive rate of 84.547% was obtained from comparing the testing dataset and the landslides hazard probability index map.Both the success rate and the predictive rate showed sufficient agreement between the landslide hazard map and the existing landslides data.The resulting landslide hazard map showed five classes of landslide hazard,such as very high,high,moderate,low and very low.This paper showed weight index modeling,as a BS method in earthquake triggered landslide hazard mappingowned high predictive ability.The results of landslide hazard mapping might help planners to choose favorable locations for reconstruction in the Haiti earthquake struck area.
Tens of thousands of landslides were triggered by the January 12,2010 Port-au-Prince,Haiti earthquake(M w 7.0).The main purpose of this study was to apply and verify the hazard mapping techniques of earthquake triggered landslide by Bivariate Statistics(BS) method,Geographical Information System(GIS) and remote sensing technologies in the Haiti earthquake stuck area.A total of 30828 landslides were delineated in the study area from visual interpretation of multi-source and high resolution remote sensing images preand post-earthquake.Eight factors,including elevation,slope angle,slope aspect,slope curvature,distance from drainages,Peak Ground Acceleration(PGA),distance from the Enriquillo-Plantain Garden Fault(EPGF) and distance along the Enriquillo-Plantain Garden Fault(EPGF) were selected as impact factors for the Haiti earthquake triggered landslide hazard mapping.The 30828 landslides were randomly partitioned into two subsets:A training dataset,which contained 70%(21579 landslides,with a total area of 11.18km 2),was used for building the model;and a testing dataset containing 30%(9249 landslides,with a total area of 4.56km 2) was used for model testing.Landslide hazard probability index map was then generated using the training dataset,the eight landslide impact factors,the weight index modeling,a BS method,and GIS technology.The validation results showed a success rate of 84.966% between the hazard probability index map and the training dataset.The predictive rate of 84.547% was obtained from comparing the testing dataset and the landslides hazard probability index map.Both the success rate and the predictive rate showed sufficient agreement between the landslide hazard map and the existing landslides data.The resulting landslide hazard map showed five classes of landslide hazard,such as very high,high,moderate,low and very low.This paper showed weight index modeling,as a BS method in earthquake triggered landslide hazard mappingowned high predictive ability.The results of landslide hazard mapping might help planners to choose favorable locations for reconstruction in the Haiti earthquake struck area.
引文
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[25]Xu C,Xu X W.Controlling parameters analyses and hazard mapping for earthquake triggered landslides:An example from a square region in Beichuan County,Sichuan Province,China[J].Arabian Journal of Geosciences,2012.doi:10.1007/s12517-012-0646-y.
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[27]Sezer E A,Pradhan B,Gokceoglu C.Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping:Klang valley,Malaysia[J].Expert Systems with Applications,2011,38(7):8208-8219.
[28]Arora M K,Das Gupta A S,Gupta R P.An artificial neural networks approach for landslide hazard zonation in the Bhagirathi(Ganga)Valley,Himalayas[J].International Journal of Remote Sensing,2004,25(3):559-572.
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[30]Pradhan B,Pirasteh S.Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping[J].Disaster Advances,2010,3(3):26-34.
[31]许冲,徐锡伟.基于GIS与ANN模型的地震滑坡易发性区划[J].地质科技情报,2012,31(3):116-121.Xu Chong,Xu Xiwei.Geological Science and Technology Information,2012,31(3):116-121.
[32]Xu C,Dai F C,Xu X W,et al.GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed,China[J].Geomorphology,2012,145-146:70-80.
[33]Yilmaz I.Comparison of landslide susceptibility mapping methodologies for Koyulhisar,Turkey:Conditional probability,logistic regression,artificial neural networks,and support vector machine[J].Environmental Earth Sciences,2010,61(4):821-836.
[34]许冲,徐锡伟.基于不同核函数的2010年玉树地震滑坡空间预测模型研究.地球物理学报,2012,55(9):2994-3005.Xu Chong,Xu Xiwei.Chinese Journal of Geophysics,2012,55(9):2994-3005.
[35]Xu C,Xu X W,Yu G H.Earthquake triggered landslide hazard mapping and validation related with the2010Port-au-Prince,Haiti earthquake[J].Disaster Advances,2012,5(4):1297-1304.
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[2]Xu C,Xu X W.Comment on"Spatial distribution analysis of landslides triggered by2008.5.12Wenchuan Earthquake,China"by Shengwen Qi,Qiang Xu,Hengxing Lan,Bing Zhang,Jianyou Liu[Engineering Geology116(2010)95-108][J].Engineering Geology,2012,133-134:40-42.
[3]Xu C,Xu X W,Yu G H.Landslides triggered by slipping-fault-generated earthquake on a plateau:an example of the14April2010,M s7.1,Yushu,China earthquake[J].Landslides,2012,doi:10.1007/s10346-012-0340-x.
[4]许冲,戴福初,徐锡伟.汶川地震滑坡灾害研究综述.地质论评,2010,56(6):860-874.Xu Chong,Dai Fuchu,Xu Xiwei.Geological Review,2010,56(6):860-874.
[5]Calais E,Freed A,Mattioli G,et al.Transpressional rupture of an unmapped fault during the2010Haiti earthquake[J].Nature Geoscience,2010,3(11):794-799.
[6]Chacon J,Irigaray C,Fernandez T,et al.Engineering geology maps:landslides and geographical information systems[J].Bulletin of Engineering Geology and the Environment,2006,65(4):341-411.
[7]Brenning A.Spatial prediction models for landslide hazards:review,comparison and evaluation[J].Natural Hazards and Earth System Sciences,2005,5(6):853-862.
[8]许冲,徐锡伟,于贵华.玉树地震滑坡分布调查及其特征与形成机制[J].地震地质,2012,34(1):47-62.Xu Chong,Xu Xiwei,Yu Guihua.Seismology and Geology,2012,34(1):47-62.
[9]许冲.汶川地震滑坡详细编录及其与全球其他地震滑坡事件对比[J].科技导报,2012,30(25):18-26.Xu Chong.Science&Technology Review,2012,30(25):18-26.
[10]许冲,戴福初,陈剑,等.汶川Ms8.0地震重灾区次生地质灾害遥感精细解译[J].遥感学报,2009,13(4):754-762.Xu Chong,Dai Fuchu,Chen Jian,et al.Journal of Remote Sensing,2009,13(4):754-762.
[11]许冲,戴福初,姚鑫.汶川地震诱发滑坡灾害的数量与面积[J].科技导报,2009,27(11):79-81.Xu Chong,Dai Fuchu,Yao Xin.Science&Technology Review,2009,27(11):79-81.
[12]许冲,戴福初,姚鑫,等.基于GIS的汶川地震滑坡灾害影响因子确定性系数分析[J].岩石力学与工程学报,2010,29(S1):2972-2981.Xu Chong,Dai Fuchu,Yao Xin,et al.Chinese Journal of Rock Mechanics and Engineering,2010,29(S1):2972-2981.
[13]许冲,徐锡伟.俯冲带地区压扭断裂型地震触发滑坡及其剥蚀厚度空间分布规律分析[J].工程地质学报,2012,20(5):732-744.Xu Chong,Xu Xiwei.Journal of Engineering Geology,2012,20(5):732-744.
[14]Pandey A,Dabral P P,Chowdary V M,et al.Landslide hazard zonation using remote sensing and GIS:A case study of Dikrong river basin,Arunachal Pradesh,India[J].Environmental Geology,2008,54(7):1517-1529.
[15]Saha A K,Gupta R P,Arora M K.GIS-based landslide hazard zonation in the Bhagirathi(Ganga)Valley,Himalayas[J].International Journal of Remote Sensing,2002,23(2):357-369.
[16]许冲.青海玉树2010-04-14地震发震构造与地震滑坡危险性初步评价[J].工程地质计算机应用,2012(1):1-14.Xu Chong.Engineering Geological Computer Applications,2012(1):1-14.
[17]许冲,戴福初,姚鑫,等.GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J].岩石力学与工程学报,2009,28(S2):3978-3985.Xu Chong,Dai Fuchu,Yao Xin,et al.Chinese Journal of Rock Mechanics and Engineering,2009,28(S2),3978-3985.
[18]Nefeslioglu H A,Gokceoglu C,Sonmez H.An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps[J].Engineering Geology,2008,97(3-4):171-191.
[19]Yilmaz I,Keskin I.GIS based statistical and physical approaches to landslide susceptibility mapping(Sebinkarahisar,Turkey)[J].Bulletin of Engineering Geology and the Environment,2009,68(4):459-471.
[20]Xu C,Xu X W,Lee Y H,et al.The2010Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling[J].Environmental Earth Sciences,2012,66(6):1603-1616.
[21]Xu C,Xu X W,Dai F C,et al.Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of2008Wenchuan earthquake struck region[J].Journal of Earth Science,2012,23(1):97-120.
[22]Oh H J,Lee S.Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas,Korea[J].Disaster Advances,2010,3(2):44-55.
[23]许冲,戴福初,姚鑫,等.基于GIS与确定性系数分析方法的汶川地震滑坡易发性评价[J].工程地质学报,2010,18(1):15-26.Xu Chong,Dai Fuchu,Yao Xin,et al.Journal of Engineering Geology,2010,18(1):15-26.
[24]许冲,戴福初,徐锡伟.基于GIS平台与证据权的地震滑坡易发性评价.地球科学,2011,36(6):1155-1164.Xu Chong,Dai Fuchu,Xu Xiwei.Earth Science,2011,36(6):1155-1164.
[25]Xu C,Xu X W.Controlling parameters analyses and hazard mapping for earthquake triggered landslides:An example from a square region in Beichuan County,Sichuan Province,China[J].Arabian Journal of Geosciences,2012.doi:10.1007/s12517-012-0646-y.
[26]许冲,徐锡伟.逻辑回归模型在玉树地震滑坡危险性评价中的应用与检验[J].工程地质学报,2012,20(3):326-333.Xu Chong,Xu Xiwei.Journal of Engineering Geology,2012,20(3):326-333.
[27]Sezer E A,Pradhan B,Gokceoglu C.Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping:Klang valley,Malaysia[J].Expert Systems with Applications,2011,38(7):8208-8219.
[28]Arora M K,Das Gupta A S,Gupta R P.An artificial neural networks approach for landslide hazard zonation in the Bhagirathi(Ganga)Valley,Himalayas[J].International Journal of Remote Sensing,2004,25(3):559-572.
[29]Xu C,Xu X W,Dai F C,et al.Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the2008Wenchuan earthquake in China[J].Computers&Geosciences,2012,46:317-329.
[30]Pradhan B,Pirasteh S.Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping[J].Disaster Advances,2010,3(3):26-34.
[31]许冲,徐锡伟.基于GIS与ANN模型的地震滑坡易发性区划[J].地质科技情报,2012,31(3):116-121.Xu Chong,Xu Xiwei.Geological Science and Technology Information,2012,31(3):116-121.
[32]Xu C,Dai F C,Xu X W,et al.GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed,China[J].Geomorphology,2012,145-146:70-80.
[33]Yilmaz I.Comparison of landslide susceptibility mapping methodologies for Koyulhisar,Turkey:Conditional probability,logistic regression,artificial neural networks,and support vector machine[J].Environmental Earth Sciences,2010,61(4):821-836.
[34]许冲,徐锡伟.基于不同核函数的2010年玉树地震滑坡空间预测模型研究.地球物理学报,2012,55(9):2994-3005.Xu Chong,Xu Xiwei.Chinese Journal of Geophysics,2012,55(9):2994-3005.
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