Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan, Gansu Province
-
摘要:
2023年12月18日23时59分,甘肃省积石山县发生Ms6.2级地震,造成大量人员伤亡及崩塌和滑坡等地震次生灾害。基于震前震后高分辨率卫星影像,利用目视解译的灾害点和影响因子特征集构建MaxEnt模型进行地震后滑坡灾害危险性评价,研究认为:(1)地震诱发滑坡灾害点主要分布在
1700 ~2250 m高程带、坡度20°~25°的阳坡范围内,在距道路距离1.5 km、距断裂带距离1.7 km、距地震中心距离5 km区间广布;(2)由影响因子的贡献率和置换重要性、测试效益值、AUC 值和正则化训练增益值综合得到地震诱发滑坡的主要影响因子为距断裂带的距离、海拔和人口分布;(3)基于构建的MaxEnt模型,得出极高和高危险区主要分布于地震烈度为Ⅷ度区,其面积为5.368 km2,占极高和高危险区总面积的77.82%,而低和极低危险区主要分布于Ⅵ和Ⅶ度区,面积百分比分别为75.33%和97.55%。文章在影响因子重要性分析基础上构建MaxEnt 模型进行震区滑坡灾害危险性评价,研究结果将为震区灾后重建提供参考。Abstract:On December 18, 2023, at 23:59, a magnitude Ms6.2 earthquake occurred in Jishishan County, Gansu Province, resulting in significant casualties and triggering numerous secondary geological disasters such as landslides and collapses. Utilizing high-resolution satellite imagery from before and after the earthquake, this study employs a MaxEnt model, constructed with visually interpreted disaster points and a set of impact factor characteristics, to assess the post-earthquake landslide hazard post-earthquake landslide hazard. The research conclusions are as follows: 1) Earthquake-induced landslide disasters are predominantly distributed within sunny slopes at elevations of
1800 to2300 m and slope gradient of 20° to 25°, extensively across areas 1.5 km from roads, 1.7 km from fault zones, and within 5 km of the earthquake’s epicenter. The majority of the disasters occurred in cropland and loam areas with higher population density in the earthquake region. 2) The main influencing factors for earthquake-induced landslides determined by factor contribution rates, permutation importance, test benefit values, AUC values, and regularization training gain value were comprehensively determined as follows: Distance from the fault zone, Elevation, and Population density; 3) Based on the constructed MaxEnt model, it is found that there is a good consistency between the extremely high and high-risk areas of landslide disasters in the earthquake zone and the seismic intensity. Among them, the extremely high and high-risk areas are mainly distributed in the intensity Ⅷ zone, with an area of 5.368km2, accounting for 77.82% of the total area of the extremely high and high-risk zones. The low and very low-risk areas are mainly distributed in the intensity Ⅵ and Ⅶ zones, accounting for 92.80% of the total area of the study region. This study constructs the MaxEnt model based on the importance analysis of impact factors to evaluate the landslide hazard in the earthquake area, providing references for post-disaster reconstruction in the earthquake zone.-
Keywords:
- Jishishan Ms6.2 earthquake /
- landslide /
- factor /
- importance analysis /
- MaxEnt model /
- risk assessment
-
0. 引言
2023年12月18日23时59分,甘肃省临夏州积石山县柳沟乡发生6.2级地震,震源深度为 10 km[1]。地震造成甘肃117人遇难,781人受伤,青海34人遇难,198人受伤。此次地震属于逆冲型,最大烈度为Ⅷ度[2],地震发生后造成大量山体滑坡失稳,震后多次余震更增加了滑坡发生的风险,给当地居民的影响深远。
地震发生往往会诱发大量的崩塌、滑坡、泥石流等地质灾害,严重威胁当地人民的生命以及财产安全[3]。由于我国位于欧亚板块东南部,受印度板块和太平洋板块的挤压碰撞与俯冲作用的影响,地壳变形强烈,是全球陆内地震最为活跃的地区[4]。特别是青藏高原东缘,地质构造复杂,地形地貌多样,断裂带密集分布,地质灾害频发,造成当地众多人员伤亡和大量财产损失[5 − 6],例如:1933年四川茂县叠溪地震[7]、2008年5月12日汶川地震[8 − 9]、2010年4月14日玉树地震[10 − 11];2014年8月13日云南鲁甸地震[12]、2017年8月8日四川九寨沟地震[13]以及2022年泸定地震[14]等。受活动构造带强震影响,上述地震导致的地面运动强度超过国家设计规范最高标准(9度罕遇)的水平,导致了严重的基础设施破坏、滑坡灾害和人员伤亡。甘肃积石山地震也造成较大的损失[15],同时该区域历史上地质灾害频发[16 − 17],因此,进行积石山震后地质灾害危险性评价,对防灾减灾和保证人民生命财产安全具有现实意义。近年来,国内外学者对地质灾害危险性评价进行了大量的研究,其中,利用GIS技术和统计学方法相融合已成为最有效、最主要的评价方法。这些方法常见的有:层次分析法[18]、信息量法[19]、逻辑回归模型[20]、灰色模型[21]等。由于单一方法的评价结果并不高以及缺乏足够的说服力[18],因此,一些学者也采用多种方法结合应用于地质灾害风险评价[22],取得较好的评价效果。可见,多种评价方法交叉融合可综合多方法优势、科学准确地表达灾害风险评价结果。
然而,上述研究多致力于影响因子与滑坡等灾害的特征分析和风险评价,在影响因子对滑坡的响应分析,特别是影响因子对灾害发生风险的贡献率和重要性分析等方面鲜有研究。为此,本文以甘肃积石山Ms6.2级地震区为研究区,基于多源遥感数据,在深入探讨滑坡灾害与影响因子间的特征分布,以及影响因子对滑坡的响应分析基础上,快速进行震区地质灾害危险性评价和分析,以期为灾后救援和重建工作提供参考。
1. 研究区概况
甘肃积石山Ms6.2级地震位于甘肃和青海两省交界地带,地震中心位置为北纬35.70°、东经102.79°,震源深度为 10 km,最高震中烈度达Ⅷ度。此次地震涉及甘肃省3个市(州)9个县(市、区),涉及青海省2个市(州)4个县(市),强烈的地震造成大量人员伤亡、房屋、道路等基础设施损毁(图1)。震区地势上属青藏高原一级阶梯向黄土高原二级阶梯的过渡带,高差起伏较大,最大高差为
3000 余米,区内断裂带活动强烈,有拉脊山南、北断裂带,倒淌河-临夏断裂带,西秦岭北缘断裂带等,为地震等自然灾害的频发区[23]。其中拉脊山断裂带是穿过震区的主要断裂,受印度板块俯冲挤压的动力驱动,青藏高原东北缘和东缘持续扩张,导致该区域新构造活跃、地震频发,为我国同震地质灾害发育最频繁的地区[24]。本次同震地质灾害主要分布于拉脊山断裂两侧,就地形地貌来看,海拔西高东低,西侧主要为基岩高山区,该区域主要分布有古生界砂岩、粉砂岩和泥岩等;右侧区域主要为黄土低山丘陵,由于黄河的下切和强烈的构造抬升,形成广泛分布的黄河阶地和大量高陡边坡存在,为地震同震灾害发育创造了条件。震区的气候类型属于典型的大陆季风性气候,年均降水量650 mm/a,年均蒸发量
1110 mm/a[24]。2. 数据与方法
2.1 数据
(1)卫星影像数据
本次所用光学遥感影像为高分一号和吉林一号,其空间分辨率分别为2 m和0.75 m,震前影像为12月18日,震后影像分别为12月20日和12月19日。影像预处理主要包括辐射定标、大气校正、几何纠正和图像融合等处理,最后得到真彩色高分辨率影像,主要用于滑坡灾害点的目视解译[25]。
(2)地震点数据
本次所用地震和余震点发生的地理位置数据来自国家地震科学数据中心(https://search.asf.alaska.edu/#/)。该数据为截至2023年12月20日、震级3.0以上的资料(表1)。
表 1 甘肃积石山Ms6.2级地震及余震数据Table 1. Ms6.2 Jishishan earthquake and its aftershocks in Gansu Province序号 发震时刻 纬度 经度 深度/km 震级/Ms 地名 备注 1 2023-12-18T23:59:30.0 35°42′ 102°47′ 10 6.2 积石山县 震中 2 2023-12-19T00:24:49.9 35°44′ 102°47′ 10 3.9 积石山县 余震 3 2023-12-19T00:36:18.3 35°47′ 102°47′ 10 4.0 积石山县 余震 4 2023-12-19T00:43:12.9 35°47′ 102°46′ 10 3.4 积石山县 余震 5 2023-12-19T00:56:51.3 35°42′ 102°47′ 10 3.4 积石山县 余震 6 2023-12-19T00:59:11.3 35°44′ 102°46′ 10 3.1 积石山县 余震 7 2023-12-19T00:59:39.0 35°50′ 102°47′ 10 4.1 积石山县 余震 8 2023-12-19T01:10:31.4 35°48′ 102°47′ 10 3.2 积石山县 余震 9 2023-12-19T01:20:12.6 35°48′ 102°46′ 10 3.2 积石山县 余震 10 2023-12-19T02:10:06.4 35°50′ 102°46′ 10 3.2 积石山县 余震 11 2023-12-19T00:32:52.9 35°46′ 102°47′ 9 3.4 积石山县 余震 (3)环境变量数据
本文所使用的环境变量数据,主要包括:地形、断裂带、道路、土地利用、植被、人口、土壤质地,并通过数据处理得到14个影响因子(表2)。其中:地形数据为 ALOS 的数字高程模型(DEM),空间分辨率为 12.5 m,主要用于获取坡度、坡向、剖面曲率、平面曲率、曲率、距河流的距离和地形湿度指数(TWI);断裂带,下载自国家地震科学数据中心,经处理得到距断裂带距离;道路数据,来自OSG(Open Street Map)官网,用于计算距道路的距离;土地利用为武汉大学的 CLCD(China land cover dataset)数据集,其空间分辨率为 30m,主要包括:耕地、林地、灌木、草地、水域、雪/冰、裸地、不透水面和湿地;归一化植被指数来自国家青藏高原科学数据中心,其数据源为 MODIS,空间分辨率为 250 m;6)人口分布,来自WorldPop 全球人口数据,空间分辨率为100 m,下载自GEE(Google Earth Engine)平台;土壤质地数据,来自世界土壤数据库,其空间分辨率为1 km。
表 2 甘肃积石山Ms6.2级地震诱发滑坡灾害影响因子数据Table 2. Disaster-causing factors of landslides induced by the Ms6.2 Jishishan earthquake in Gansu Province环境变量 影响因子 数据来源 地形因子 高程、坡度、坡向、剖面曲率、平面曲率、
曲率、距河流距离、TWI高程数据为数字高程模型(DEM),下载自ASF官网(https://search.asf.alaska.edu/#/),
其它因子数据为DEM的派生数据断裂带 距断裂带的距离 断裂带数据下载自国家地震科学数据中心(https://search.asf.alaska.edu/#/) 土壤质地 土壤质地数据 下载自世界土壤数据库(https://www.fao.org/) 道路 距道路的距离 OSM官网(https://www.openstreetmap.org/) 人口 人口分布密度 OSM官网(https://www.openstreetmap.org/) 土地利用 地表覆盖 武汉大学CLCD数据集(https://zenodo.org/) 植被 归一化植被指数 国家青藏高原科学数据中心(https://data.tpda.ac.cn/home ) 2.2 研究方法
2.2.1 MaxEnt模型原理
最大熵(MaxEnt)模型是基于最大熵原理来预测随机事件概率分布的方法,该模型在灾害危险性评价、预测物种分布等领域都得到了广泛应用[26 − 27]。利用MaxEnt模型进行地质灾害预测的原理,主要以地质灾害发生点和环境变量之间的关系为基础,建立地质灾害危险性的概率模型,从而对整个区域的地质灾害危险性进行预测。本文是以14个影响因子为约束条件,以灾害点为事件,寻求地质灾害危险性在14个约束条件下的最大熵,进而估算地质灾害的危险性。
2.2.2 数据预处理
对目视解译的
1205 个滑坡灾害点数据进行预处理,包括:1)剔除滑坡目视解译得到的重复和错误点;2)通过建立渔网去除自相关点;3)数据转换,主要是将最终得到的980个滑坡灾害点数据转成csv格式用于模型构建。对影响因子的预处理,包括将致灾因子重采样为统一像元尺度、裁剪使所有影响因子有相同的行列号,然后将其转换为ASCII格式用于构建MaxEnt模型[28]。
2.2.3 模型参数设置
模型参数调整是构建MaxEnt模型的重要环节。具体流程包括,输出格式设置为logistic,将滑坡灾害点分为测试集和训练集,其中,随机测试集设置比例为25%,75%用于模型训练[29]。精度评定采用受试者工作特征曲线(receiver operating characteristic,ROC),将正则化参数(regularization multiplier)设置为1,重复建模次数设置为10,以防止欠拟合和过拟合现象的发生,并且使模型构建达到最佳效果[30]。
2.2.4 危险评价
对滑坡危险性评价结果等级的划分,参考前人研究成果[31 − 32],并结合高分影像目视解译得到的滑坡灾害点空间分布,将危险区等级划分为五个等级:极低危险区,低危险区,中危险区,高危险区和极高危险区。
2.2.5 精度评价
模型精度评价采用ROC曲线与坐标轴围成的曲线下面积(area under curve,AUC)值来表示,AUC值大小表示预测成功率,其值越大,则准确率越高,说明模型的预测效果就越好[32]。AUC值的具体评价标准如表3 。
表 3 AUC评价标准Table 3. AUC evaluation criteriaAUC值 精度评价 [0, 0.6) 很差 [0.6, 0.7) 较差 [0.7, 0.8) 一般 [0.8, 0.9) 好 [0.9, 1] 极好 3. 结果
3.1 滑坡点的影响因子分布特征
本文通过对比积石山Ms6.2级地震前后的高分一号和吉林一号卫星影像,对滑坡进行目视解译。由于地震前,该区域发生大面积降雪,在地震发生后,发生滑坡的区域裸露出新的土壤,因此有利于滑坡灾害点的卫星影像目视解译。此次目视解译原则为:首选区域内空间分辨率高的影像,若存在云层遮挡或地形阴影,将按照空间分辨率从高到低的顺序,选择时相相近的影像,最终达到覆盖整个震区。在滑坡点目视解译中,主要采取震前震后影像对比的方式,本次解译共编目滑坡和潜在灾害点
1205 处(图2),大多以小型崩塌和滑坡为主,主要集中于震区黄河两岸黄土丘陵区、道路与沟谷附近,多发育于建房和公路内边坡陡坎部位[30],其威胁的主要承灾体为公路和农田等。为进一步分析灾害点在各影响因子上的分布特征,研究中将灾害点与各因子进行叠加,分别作直方图进行统计分析,其中横坐标为各因子分级,纵坐标为密度。
对于各地形因子(图3),分析中将高程因子按100 m间隔分为九级,叠加统计分析发现,地震诱发滑坡灾害点在高程因子上基本呈抛物线分布(R2 =
0.7394 ),主要分布在1700 ~2250 m高程带;就灾害点在坡度上的分布,将坡度按5°间隔等间距分级,统计发现灾害点主要发生在小于25°区间内,大于30°坡度范围零星分布;在地形湿度指数(TWI)上,灾害点发生基本呈现指数分布(R2 =0.6816 );就坡向分布来看,地震诱发地质灾害点大多数发生于东、东南和南向范围内。对于各类距离因子(图4),研究中将距道路距离因子按间隔0.5 km等间距作缓冲区分级,分析发现地震诱发的绝大多数灾害点发生于距道路1.5 km范围内;对于距断裂带距离,第一级按1 km、第二级以后按2 km进行分级统计,分析发现地震诱发地质灾害点在距断裂带距离上基本呈现指数型分布(R2 = 0.201),主要分布于小于1 km、3~4 km和8~12 km范围内,其中3~4 km和8~12 km区间为黄河两岸;就距河流距离来看,地震诱发地质灾害点分布呈指数型分布(R2 =
0.7727 ),主要分布在河流两岸及附近;对于距地震中心距离因子,研究中第一级按1 km统计,第二级按5 km统计,后面各级按10 km统计,分析发现灾害点呈指数型分布(R2 =0.8706 )。同时,也对土地利用、土壤质地、归一化植被指数和人口分布因子进行地质灾害点分布统计(图5)。就土地利用来看,地震诱发地质灾害点主要发生于耕地、部分草地和水域附近也有一些地质灾害发生;就土壤质地来看,地震诱发地质灾害点主要分布在壤土、有少量分布于黏土和壤砂土层;就NDVI来看,地震诱发地质灾害主要分布在0.08~0.16;就震区人口分布来看,人口密度大的地方人类活动也较强烈,因而地震诱发地质灾害点的分布也相应多一些。
3.2 影响因子对滑坡的响应分析
1)模型评价精度
将滑坡灾害点数据和选取的14个影响因子数据输入MaxEnt模型,通过10次的迭代计算,最后得到AUC值为0.854(图6),模型可靠性达到“好”的水平。因此,本次研究利用解译的地质灾害点和各影响因子,通过10次迭代计算构建MaxEnt模型进行积石山Ms6.2级地震诱发滑坡危险性评价,其结果具有较好的可靠性。
2)影响因子重要性分析
置换重要性是反映模型对该变量的依赖程度的指标[33]。表4为各影响因子对滑坡灾害影响程度的贡献率和置换重要性。可见,排名前五的影响因子为距断裂带的距离、高程、人口分布、土壤质地和距河流的距离,其贡献率分别为39.0%、38.1%、17.8%、1.3%和1.2%,其累计贡献率占比高达97.4%。同时可以看出,置换重要性排名前五的影响因子为距断裂带的距离、高程、距河流的距离、人口分布以及土壤质地,其置换重要性分别为48.3%、45.1%、2.4%、1.4%和1.3%,累计值达98.5%。
表 4 滑坡灾害主要影响因子贡献率和置换重要性Table 4. Contribution rates and permutation importance of main disaster-causing factors of landslides序号 因子 贡献率/% 置换重要性/% 1 距断裂带距离 39 48.3 2 高程 38.1 45.1 3 人口分布 17.8 1.4 4 土壤质地 1.3 1.3 5 距河流距离 1.2 2.4 6 归一化植被指数 0.8 0.6 7 坡度 0.8 0.1 8 距道路距离 0.6 0.5 9 坡向 0.3 0.2 10 地形湿度指数 0.1 0 11 土地利用 0 0 12 平面曲率 0 0 13 剖面曲率 0 0 14 曲率 0 0 图7为通过刀切法检验对各影响因子重要性的检验结果。由测试增益值[34],见图7(a),重要性位列前五的影响因子为距断裂带的距离、高程、人口分布、土壤质地和归一化植被指数,其值分别为:0.35、0.32、0.22、0.12和0.1。由AUC值,见图7(b),排位前五的影响因子为高程、距断裂带的距离、人口分布、归一化植被指数和距河流的距离,其值分别为0.74、0.73、0.67、0.65和0.59。由正则化训练增益,见图7(c),位列前五的影响因子为高程、距断裂带距离、人口分布、距道路距离和距河流距离,其值分别为0.3、0.29、0.22、0.1 和 0.08。
3)影响因子对滑坡危险性响应分析
图8和图9分别为各影响因子对滑坡发生的响应曲线,其中,纵轴代表滑坡发生的概率,横轴代表各因子的取值范围。设定参考概率阈值为0.5[35],当大于0.5时,认为该因子取值范围有利于灾害的发生。图8可知,坡向因子对滑坡发生的响应最高。其他因子的某一取值范围对滑坡发生的响应也较敏感。例如,当地形湿度指数大于4 m时,其概率均大于0.5,极易引起滑坡的发生;同理,当高程带在
1700 ~2250 m、剖面曲率为−4.2~3、平面曲率为−3.9~4.1、综合曲率为−6~11时,该范围对滑坡发生强响应;当距断裂带距离小于1.7 km、距河流距离小于3.8 km、距道路距离小于2 km、坡度小于30°、人口分布密度大于20人/km2时,其概率均大于0.5,极易引起滑坡的发生。同时也可以看出,当归一化植被指数小于−0.04和0.06~0.15时,其概率均大于0.5,该区段滑坡灾害发生的响应较好;就土地利用因子来看(图9),耕地、草地和水域的概率均大于0.5,极易发生滑坡灾害;对于土壤质地,砂质壤土和壤土的概率大于0.5,极易发生滑坡灾害。3.3 滑坡危险性评价
本文采用影响因子重要性和相关系数法,剔除贡献率较低(即土地利用、平面曲率、泡面曲率和曲率)和相关性高(即高程)的因子,然后将其余因子构建模型,计算最大熵结果,并按自然断点法分五级[36],图10为本研究获取的积石山Ms6.2级地震诱发滑坡危险性评价结果。经统计得出,极高危险区面积为 49.38 km2,占研究区总面积的0.84%;高危险区面积为157.79 km2,占研究区总面积的 2.69%;中危险区面积为430.03 km2,占研究区总面积的7.33%;低危险区面积为526.07 km2,占研究区总面积的8.96%;极低危险区面积为
4699.02 km2,占研究区总面积的80.18%。可见,由于本次地震发生在冬季,大多数地方为季节性冻土,因而本次地震诱发滑坡大多为小型,其极高和高危险性分布相对较少,主要位于黄河两岸的部分地区,与文献[25]结果一致。为进一步分析危险区与地震烈度之间的关系,将危险性评价结果与地震烈度图[37]进行叠加,统计得出(表5),极高和高危险区密度主要位于地震烈度为Ⅷ区,其面积为21.2 km2,占Ⅷ区的百分比为26.38%;中危险区密度主要分布于Ⅶ和Ⅷ区,面积为341.22 km2,占Ⅶ和Ⅷ区面积百分比分别为16.92%和28.82%;低和极低危险区主要分布于Ⅶ和Ⅵ区,面积占比分别为75.33%和97.55%,该区域远离发震区,地震诱发地质灾害危险性也较低。
表 5 不同地震烈度区的危险性等级面积百分比统计Table 5. Area percentage of different risk grades in various seismic intensity zones地震烈度 极高危险/% 高危险/% 中危险/% 低危险/% 极低危险/% Ⅷ区 6.91 19.47 28.82 11.76 33.05 Ⅶ区 1.80 5.95 16.92 16.12 59.21 Ⅵ区 0.03 0.23 2.19 6.15 91.40 4. 讨论
影响因子的选择是地震诱发滑坡危险性评价可靠性的主要环节[38]。与前人研究不同,本研究从地形因子、断裂带、土壤质地、道路、人口、土地利用以及植被等环境变量出发,选取影响因子,在分析影响因子对滑坡危险性响应的基础上,通过获取贡献率和置换重要性、采用刀切法对影响因子进行重要性评估,来优选主要影响因子。研究发现,距断裂带距离、高程、人口分布、土壤质地和距河流距离的贡献率和置换重要性均大于1,其中,距断裂带距离最大,分别为39.0%和48.3%。
为进一步分析本研究的可靠性,采用刀切法对各影响因子重要性进行检验,由测试增益值、AUC 值以及正则化训练增益三个指标进行计算,得出对本次地震诱发滑坡危险性的主要响应因子为距断裂带距离、高程和人口分布。可见,除属于孕灾环境条件的高程因子外,距断裂带距离和人口分布是本次地震诱发滑坡危险性的主要驱动因子。由于震区相对高差约
3000 m,地形起伏较大,高海拔地区因地形坡度陡峭,土壤和岩石的稳定性相对较差,特别是在1700 ~2300 m区间,人类活动(例如:修路、建房、耕作等)也较强烈,遥感解译发现,大多发生在距离道路较近的地区,道路建设往往会破坏原有地形和土壤结构,为滑坡崩塌发育创造了条件。断裂带区因地壳应力集中以及地质结构脆弱,在地震等外部因素的驱动下,极易引发滑坡灾害;同时,由于河流的侵蚀作用往往会破坏斜坡的稳定性,导致滑坡灾害的发生。实地调查发现,由于本次地震主要发生在冬季,其诱发地质灾害主要以小型崩塌和滑坡为主。从灾害分布区域、规模和密集程度来看,与本研究计算确定的主要影响因素吻合性较好。例如:图11为实地地质灾害调查中,获得的典型性滑坡,其中,图11a和图11b为砂/泥岩滑坡类,大多分布于公路等的内边坡区;图11c和图11d为黄土滑坡类,大多发生于建房和公路等的切坡区;图11e为裂缝,发育于震区黄河两岸阶地居民区。
5. 结论
(1)从地形因子来看,本次地震诱发滑坡灾害点主要分布在
1700 ~2250 m高程带、20°~25°的坡度区间,且大多数群发于坡向为东、东南和南向的阳坡范围内,在TWI上基本呈现指数分布,同时在距道路距离1.5 km、距地震中心距离5 km范围内广布。从LULC来看,地震诱发地质灾害点主要发生于耕地,且土壤质地多为壤土区域;在植被覆盖上,主要集中发生于NDVI为0.2~0.4区间、震区人口分布密度大的地方。(2)由影响因子的贡献率和置换重要性、刀切法计算的测试效益值、AUC 值和正则化训练增益值,得到本次地震诱发地灾危险性的主要影响因子为:距断裂带的距离、高程和人口分布。当距断裂带距离小于1.7 km、人口分布密度达20人/km2时,其概率均大于0.5,对滑坡危险性发生的响应较明显。
(3)基于构建的MaxEnt模型得出,震区滑坡高危险区主要分布于黄河两岸及附近区域。其中,极高和高危险区密度主要位于地震烈度为Ⅷ区,其面积为21.2 km2,占Ⅷ区的面积百分比为26.38%;中危险区密度主要分布于Ⅶ和Ⅷ区,面积百分比分别为16.92%和28.82%;低和极低危险区主要分布于Ⅶ和Ⅵ区,面积百分比分别为75.33%和97.55%。
-
表 1 甘肃积石山Ms6.2级地震及余震数据
Table 1 Ms6.2 Jishishan earthquake and its aftershocks in Gansu Province
序号 发震时刻 纬度 经度 深度/km 震级/Ms 地名 备注 1 2023-12-18T23:59:30.0 35°42′ 102°47′ 10 6.2 积石山县 震中 2 2023-12-19T00:24:49.9 35°44′ 102°47′ 10 3.9 积石山县 余震 3 2023-12-19T00:36:18.3 35°47′ 102°47′ 10 4.0 积石山县 余震 4 2023-12-19T00:43:12.9 35°47′ 102°46′ 10 3.4 积石山县 余震 5 2023-12-19T00:56:51.3 35°42′ 102°47′ 10 3.4 积石山县 余震 6 2023-12-19T00:59:11.3 35°44′ 102°46′ 10 3.1 积石山县 余震 7 2023-12-19T00:59:39.0 35°50′ 102°47′ 10 4.1 积石山县 余震 8 2023-12-19T01:10:31.4 35°48′ 102°47′ 10 3.2 积石山县 余震 9 2023-12-19T01:20:12.6 35°48′ 102°46′ 10 3.2 积石山县 余震 10 2023-12-19T02:10:06.4 35°50′ 102°46′ 10 3.2 积石山县 余震 11 2023-12-19T00:32:52.9 35°46′ 102°47′ 9 3.4 积石山县 余震 表 2 甘肃积石山Ms6.2级地震诱发滑坡灾害影响因子数据
Table 2 Disaster-causing factors of landslides induced by the Ms6.2 Jishishan earthquake in Gansu Province
环境变量 影响因子 数据来源 地形因子 高程、坡度、坡向、剖面曲率、平面曲率、
曲率、距河流距离、TWI高程数据为数字高程模型(DEM),下载自ASF官网(https://search.asf.alaska.edu/#/),
其它因子数据为DEM的派生数据断裂带 距断裂带的距离 断裂带数据下载自国家地震科学数据中心(https://search.asf.alaska.edu/#/) 土壤质地 土壤质地数据 下载自世界土壤数据库(https://www.fao.org/) 道路 距道路的距离 OSM官网(https://www.openstreetmap.org/) 人口 人口分布密度 OSM官网(https://www.openstreetmap.org/) 土地利用 地表覆盖 武汉大学CLCD数据集(https://zenodo.org/) 植被 归一化植被指数 国家青藏高原科学数据中心(https://data.tpda.ac.cn/home ) 表 3 AUC评价标准
Table 3 AUC evaluation criteria
AUC值 精度评价 [0, 0.6) 很差 [0.6, 0.7) 较差 [0.7, 0.8) 一般 [0.8, 0.9) 好 [0.9, 1] 极好 表 4 滑坡灾害主要影响因子贡献率和置换重要性
Table 4 Contribution rates and permutation importance of main disaster-causing factors of landslides
序号 因子 贡献率/% 置换重要性/% 1 距断裂带距离 39 48.3 2 高程 38.1 45.1 3 人口分布 17.8 1.4 4 土壤质地 1.3 1.3 5 距河流距离 1.2 2.4 6 归一化植被指数 0.8 0.6 7 坡度 0.8 0.1 8 距道路距离 0.6 0.5 9 坡向 0.3 0.2 10 地形湿度指数 0.1 0 11 土地利用 0 0 12 平面曲率 0 0 13 剖面曲率 0 0 14 曲率 0 0 表 5 不同地震烈度区的危险性等级面积百分比统计
Table 5 Area percentage of different risk grades in various seismic intensity zones
地震烈度 极高危险/% 高危险/% 中危险/% 低危险/% 极低危险/% Ⅷ区 6.91 19.47 28.82 11.76 33.05 Ⅶ区 1.80 5.95 16.92 16.12 59.21 Ⅵ区 0.03 0.23 2.19 6.15 91.40 -
[1] 中国地震台网中心. 12月18日23时59分在甘肃省临夏州积石山县发生6.2级地震[EB/OL]. [2023-12-27]. https://www.cenc.ac.cn/cenc/dzxx/409064/index.html. [China Earthquake Networks Center. A magnitude 6.2 earthquake struck Jishishan County, Linxia Prefecture, Gansu Province at 23:59 on December 18[EB/OL]. [2023-12-27]. https://www.cenc.ac.cn/cenc/dzxx/409064/index.html.] China Earthquake Networks Center. A magnitude 6.2 earthquake struck Jishishan County, Linxia Prefecture, Gansu Province at 23:59 on December 18[EB/OL]. [2023-12-27]. https://www.cenc.ac.cn/cenc/dzxx/409064/index.html.
[2] 王勤彩,罗钧,陈翰林,等. 2023年12月18日甘肃积石山6.2级地震震源机制解[J]. 地震,2024,44(1):185 − 188. [WANG Qincai,LUO Jun,CHEN Hanlin,et al. Focal mechanism for the December 18,2023,Jishishan Ms6.2 earthquake in Gansu Province[J]. Earthquake,2024,44(1):185 − 188. (in Chinese with English abstract)] WANG Qincai, LUO Jun, CHEN Hanlin, et al. Focal mechanism for the December 18, 2023, Jishishan Ms6.2 earthquake in Gansu Province[J]. Earthquake, 2024, 44(1): 185 − 188. (in Chinese with English abstract)
[3] 许强,黄润秋. “5•12”汶川大地震诱发大型崩滑灾害动力特征初探[C]//中国岩石力学与工程学会. 汶川大地震工程震害调查分析与研究,北京:科学出版社,2009:8. [XU Qiang,HUANG Runqiu. A preliminary study of the dynamic characteristics of large-scale landslide disasters induced by the “5•12” Wenchuan earthquake[C]//China Society of Rock Mechanics and Engineering. The Wenchuan earthquake engineering seismic damage investigation and research,Beijing:Science Press,2009:8. (in Chinese with English abstract)] XU Qiang, HUANG Runqiu. A preliminary study of the dynamic characteristics of large-scale landslide disasters induced by the “5•12” Wenchuan earthquake[C]//China Society of Rock Mechanics and Engineering. The Wenchuan earthquake engineering seismic damage investigation and research, Beijing: Science Press, 2009: 8. (in Chinese with English abstract)
[4] 刘俊来,张进江,张培震. 中国构造地质学发展:百年回顾与展望[J]. 地质学报,2022,96(10):3283 − 3296. [LIU Junlai,ZHANG Jinjiang,ZHANG Peizhen. Structural geology development in China:One hundred years[J]. Acta Geologica Sinica,2022,96(10):3283 − 3296. (in Chinese with English abstract)] LIU Junlai, ZHANG Jinjiang, ZHANG Peizhen. Structural geology development in China: One hundred years[J]. Acta Geologica Sinica, 2022, 96(10): 3283 − 3296. (in Chinese with English abstract)
[5] 李振洪,朱武,余琛,等. 影像大地测量学发展现状与趋势[J]. 测绘学报,2023,52(11):1805 − 1834. [LI Zhenhong,ZHU Wu,YU Chen,et al. Development status and trends of imaging geodesy[J]. Acta Geodaetica et Cartographica Sinica,2023,52(11):1805 − 1834. (in Chinese with English abstract)] LI Zhenhong, ZHU Wu, YU Chen, et al. Development status and trends of imaging geodesy[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(11): 1805 − 1834. (in Chinese with English abstract)
[6] 黄润秋. 汶川8.0级地震触发崩滑灾害机制及其地质力学模式[J]. 岩石力学与工程学报,2009,28(6):1239 − 1249. [HUANG Runqiu. Mechanism and geomechanical modes of landslide hazards triggered by Wenchuan 8.0 earthquake[J]. Chinese Journal of Rock Mechanics and Engineering,2009,28(6):1239 − 1249. (in Chinese with English abstract)] HUANG Runqiu. Mechanism and geomechanical modes of landslide hazards triggered by Wenchuan 8.0 earthquake[J]. Chinese Journal of Rock Mechanics and Engineering, 2009, 28(6): 1239 − 1249. (in Chinese with English abstract)
[7] 柴贺军,刘汉超,张倬元. 中国滑坡堵江事件目录[J]. 地质灾害与环境保护,1995,6(4):1 − 9. [CHAI Hejun,LIU Hanchao,ZHANG Zhuoyuan. The catalog of Chinese landslide dam events[J]. Journal of Geological Hazards and Environment Preservation,1995,6(4):1 − 9. (in Chinese with English abstract)] CHAI Hejun, LIU Hanchao, ZHANG Zhuoyuan. The catalog of Chinese landslide dam events[J]. Journal of Geological Hazards and Environment Preservation, 1995, 6(4): 1 − 9. (in Chinese with English abstract)
[8] 许冲,戴福初,肖建章. “5•12” 汶川地震诱发滑坡特征参数统计分析[J]. 自然灾害学报,2011,20(4):147 − 153. [XU Chong,DAI Fuchu,XIAO Jianzhang. Statistical analysis of characteristic parameters of landslides triggered by May 12,2008 Wenchuan earthquake[J]. Journal of Natural Disasters,2011,20(4):147 − 153. (in Chinese with English abstract)] XU Chong, DAI Fuchu, XIAO Jianzhang. Statistical analysis of characteristic parameters of landslides triggered by May 12, 2008 Wenchuan earthquake[J]. Journal of Natural Disasters, 2011, 20(4): 147 − 153. (in Chinese with English abstract)
[9] 陶和平,刘斌涛,刘淑珍,等. 遥感在重大自然灾害监测中的应用前景——以5•12汶川地震为例[J]. 山地学报,2008,26(3):276 − 279. [TAO Heping,LIU Bintao,LIU Shuzhen,et al. Natural hazards monitoring using remote sensing:A case study of 5•12 Wenchuan earthquake[J]. Mountain Research,2008,26(3):276 − 279. (in Chinese with English abstract)] TAO Heping, LIU Bintao, LIU Shuzhen, et al. Natural hazards monitoring using remote sensing: A case study of 5•12 Wenchuan earthquake[J]. Mountain Research, 2008, 26(3): 276 − 279. (in Chinese with English abstract)
[10] 许冲,徐锡伟,戴福初,等. 2010年4月14日玉树地震滑坡空间分布与控制变量分析[J]. 工程地质学报,2011,19(4):505 − 510. [XU Chong,XU Xiwei,DAI Fuchu,et al. Analysis of spatial distribution and controlling parameters of landslides triggered by the April 14,2010 Yushu earthquake[J]. Journal of Engineering Geology,2011,19(4):505 − 510. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1004-9665.2011.04.011 XU Chong, XU Xiwei, DAI Fuchu, et al. Analysis of spatial distribution and controlling parameters of landslides triggered by the April 14, 2010 Yushu earthquake[J]. Journal of Engineering Geology, 2011, 19(4): 505 − 510. (in Chinese with English abstract) DOI: 10.3969/j.issn.1004-9665.2011.04.011
[11] NIU Quanfu,CHENG Weiming,LIU Yong,et al. Risk assessment of secondary geological disasters induced by the Yushu earthquake[J]. Journal of Mountain Science,2012,9(2):232 − 242. DOI: 10.1007/s11629-012-2076-4
[12] 和海霞,李素菊,刘明,等. 云南鲁甸6.5级地震灾区滑坡分布特征研判分析[J]. 灾害学,2016,31(1):92 − 95. [HE Haixia,LI Suju,LIU Ming,et al. Research on landslide spatial distribution in Ludian earthquake disaster area[J]. Journal of Catastrophology,2016,31(1):92 − 95. (in Chinese with English abstract)] HE Haixia, LI Suju, LIU Ming, et al. Research on landslide spatial distribution in Ludian earthquake disaster area[J]. Journal of Catastrophology, 2016, 31(1): 92 − 95. (in Chinese with English abstract)
[13] 梁昌健. 四川九寨沟Ms7.0级地震的发震构造及成因机制分析[D]. 成都:成都理工大学,2019. [LIANG Changjian. Analysis of seismogenic structure and genetic mechanism of Jiuzhaigou earthquake with Ms7.0 in Sichuan Province[D]. Chengdu:Chengdu University of Technology,2019. (in Chinese with English abstract)] LIANG Changjian. Analysis of seismogenic structure and genetic mechanism of Jiuzhaigou earthquake with Ms7.0 in Sichuan Province[D]. Chengdu: Chengdu University of Technology, 2019. (in Chinese with English abstract)
[14] 徐浪,陈强,吴远昆,等. 2022年泸定Mw6.7地震滑动模型及地震风险性评估[J]. 大地测量与地球动力学,2024,44(5):473 − 478. [XU Lang,CHEN Qiang,WU Yuankun,et al. Coseismic slip model of the 2022 Luding Mw6.7 earthquake and seismic risk assessment[J]. Journal of Geodesy and Geodynamics,2024,44(5):473 − 478. (in Chinese with English abstract)] XU Lang, CHEN Qiang, WU Yuankun, et al. Coseismic slip model of the 2022 Luding Mw6.7 earthquake and seismic risk assessment[J]. Journal of Geodesy and Geodynamics, 2024, 44(5): 473 − 478. (in Chinese with English abstract)
[15] 铁永波,张宪政,曹佳文,等. 积石山Ms6.2级和泸定Ms6.8级地震地质灾害发育规律对比[J]. 成都理工大学学报(自然科学版),2024,51(1):9 − 21. [TIE Yongbo,ZHANG Xianzheng,CAO Jiawen,et al. Comparative research of the characteristics of geological hazards induced by the Jishishan(Ms6.2) and Luding(Ms6.8) earthquakes[J]. Journal of Chengdu University of Technology (Science & Technology Edition),2024,51(1):9 − 21. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1671-9727.2024.01.02 TIE Yongbo, ZHANG Xianzheng, CAO Jiawen, et al. Comparative research of the characteristics of geological hazards induced by the Jishishan(Ms6.2) and Luding(Ms6.8) earthquakes[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51(1): 9 − 21. (in Chinese with English abstract) DOI: 10.3969/j.issn.1671-9727.2024.01.02
[16] 杜源,王纯,张勤,等. 顾及黄土滑坡灾害状态特征的实时GNSS滤波算法[J]. 武汉大学学报(信息科学版),2023,48(7):1216 − 1222. [DU Yuan,WANG Chun,ZHANG Qin,et al. Real-time GNSS filtering algorithm considering state characteristics of loess landslide hazards[J]. Geomatics and Information Science of Wuhan University,2023,48(7):1216 − 1222. (in Chinese with English abstract)] DU Yuan, WANG Chun, ZHANG Qin, et al. Real-time GNSS filtering algorithm considering state characteristics of loess landslide hazards[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1216 − 1222. (in Chinese with English abstract)
[17] 黄观文,景策,李东旭,等. 甘肃积石山6.2级地震对滑坡易发区的变形影响分析[J/OL]. 武汉大学学报(信息科学版) [2024-01-09](2024-05-08). https://doi.org/10.13203/j.whugis20230490. [HUANG Guanwen,JING Ce,LI Dongxu,et al. Analysis of deformation impacts on landslide-prone areas by the magnitude 6.2 earthquake in Jishishan,Gansu[J/OL]. Geomatics and Information Science of Wuhan University. [2024-01-09](2024-05-08). https://doi.org/10.13203/j.whugis20230490. (in Chinese with English abstract)] HUANG Guanwen, JING Ce, LI Dongxu, et al. Analysis of deformation impacts on landslide-prone areas by the magnitude 6.2 earthquake in Jishishan, Gansu[J/OL]. Geomatics and Information Science of Wuhan University. [2024-01-09](2024-05-08). https://doi.org/10.13203/j.whugis20230490. (in Chinese with English abstract)
[18] 于开宁,吴涛,魏爱华,等. 基于AHP-突变理论组合模型的地质灾害危险性评价——以河北平山县为例[J]. 中国地质灾害与防治学报,2023,34(2):146 − 155. [YU Kaining,WU Tao,WEI Aihua,et al. Geological hazard assessment based on the models of AHP,catastrophe theory and their combination:A case study in Pingshan County of Hebei Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(2):146 − 155. (in Chinese with English abstract)] YU Kaining, WU Tao, WEI Aihua, et al. Geological hazard assessment based on the models of AHP, catastrophe theory and their combination: A case study in Pingshan County of Hebei Province[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(2): 146 − 155. (in Chinese with English abstract)
[19] 牛全福,冯尊斌,张映雪,等. 基于GIS的兰州地区滑坡灾害孕灾环境敏感性评价[J]. 灾害学,2017,32(3):29 − 35. [NIU Quanfu,FENG Zunbin,ZHANG Yingxue,et al. Susceptibility assessment of disaster environment for landslide hazard based on GIS in Lanzhou area[J]. Journal of Catastrophology,2017,32(3):29 − 35. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1000-811X.2017.03.006 NIU Quanfu, FENG Zunbin, ZHANG Yingxue, et al. Susceptibility assessment of disaster environment for landslide hazard based on GIS in Lanzhou area[J]. Journal of Catastrophology, 2017, 32(3): 29 − 35. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-811X.2017.03.006
[20] 杨得虎,朱杰勇,刘帅,等. 基于信息量、加权信息量与逻辑回归耦合模型的云南罗平县崩滑灾害易发性评价对比分析[J]. 中国地质灾害与防治学报,2023,34(5):43 − 53. [YANG Dehu,ZHU Jieyong,LIU Shuai,et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value,weighted information value and logistic regression coupled model in Luoping County,Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):43 − 53. (in Chinese with English abstract)] YANG Dehu, ZHU Jieyong, LIU Shuai, et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value, weighted information value and logistic regression coupled model in Luoping County, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(5): 43 − 53. (in Chinese with English abstract)
[21] 朱吉祥,张礼中,周小元,等. 基于信息熵的灰色模型在地质灾害评价中的应用——以四川青川县为例[J]. 灾害学,2012,27(1):78 − 82. [ZHU Jixiang,ZHANG Lizhong,ZHOU Xiaoyuan,et al. Application of entropy-based grey model in geological hazard assessment:A case study of Qingchuan County,Sichuan Province[J]. Journal of Catastrophology,2012,27(1):78 − 82. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1000-811X.2012.01.016 ZHU Jixiang, ZHANG Lizhong, ZHOU Xiaoyuan, et al. Application of entropy-based grey model in geological hazard assessment: A case study of Qingchuan County, Sichuan Province[J]. Journal of Catastrophology, 2012, 27(1): 78 − 82. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-811X.2012.01.016
[22] SHAHINUZZAMAN M,HAQUE M N,SHAHID S. Delineation of groundwater potential zones using a parsimonious concept based on catastrophe theory and analytical hierarchy process[J]. Hydrogeology Journal,2021,29(3):1091 − 1116. DOI: 10.1007/s10040-021-02322-2
[23] 王立朝,侯圣山,董英,等. 甘肃积石山Ms6.2级地震的同震地质灾害基本特征及风险防控建议[J]. 中国地质灾害与防治学报,2024,35(3):108 − 118. [WANG Lichao,HOU Shengshan,DONG Ying,et al. Basic characteristics of co-seismic geological hazards induced by Jishishan Ms6.2 earthquake and suggestions for their risk control[J]. The Chinese Journal of Geological Hazard and Control,2024,35(3):108 − 118. (in Chinese with English abstract)] WANG Lichao, HOU Shengshan, DONG Ying, et al. Basic characteristics of co-seismic geological hazards induced by Jishishan Ms6.2 earthquake and suggestions for their risk control[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(3): 108 − 118. (in Chinese with English abstract)
[24] 郭富赟,张永军,窦晓东,等. 甘肃积石山Ms 6.2地震次生地质灾害分布规律与发育特征[J]. 兰州大学学报(自然科学版),2024,60(1):6 − 12. [GUO Fuyun,ZHANG Yongjun,DOU Xiaodong,et al. Distribution patterns and development characteristics of secondary geological hazards caused by the Ms 6.2 earthquake in Jishishan,Gansu[J]. Journal of Lanzhou University (Natural Sciences),2024,60(1):6 − 12. (in Chinese with English abstract)] GUO Fuyun, ZHANG Yongjun, DOU Xiaodong, et al. Distribution patterns and development characteristics of secondary geological hazards caused by the Ms 6.2 earthquake in Jishishan, Gansu[J]. Journal of Lanzhou University (Natural Sciences), 2024, 60(1): 6 − 12. (in Chinese with English abstract)
[25] 陈博,宋闯,陈毅,等. 2023年甘肃积石山Ms6.2地震同震滑坡和建筑物损毁情况应急识别与影响因素研究[J/OL]. 武汉大学学报(信息科学版)[2024-01-09](2024-05-08). https://doi.org/10.13203/J.whugis20. [CHEN Bo,SONG Chuang,CHEN Yi,et al. Study on contingency identification and influencing factors for co-seismic landslides and building damage in the 2023 Gansu Jishishan Ms6.2 earthquake[J/OL]. Geomatics and Information Science of Wuhan University [2024-01-09](2024-05-08). https://doi.org/10.13203/J.whugis20. (in Chinese with English abstract)] CHEN Bo, SONG Chuang, CHEN Yi, et al. Study on contingency identification and influencing factors for co-seismic landslides and building damage in the 2023 Gansu Jishishan Ms6.2 earthquake[J/OL]. Geomatics and Information Science of Wuhan University [2024-01-09](2024-05-08). https://doi.org/10.13203/J.whugis20. (in Chinese with English abstract)
[26] 王浩,牛全福,刘博,等. 基于MaxEnt结合粒子群优化的陇南市山洪灾害空间分布预测研究[J/OL]. 武汉大学学报(信息科学版),2023. [2023-10-20](2024-05-08). http://kns.cnki.net/KCMS/detail/detail.aspx?filename=WHCH20231018002&dbname=CJFD&dbcode=CJFQ. [WANG Hao,NIU Quanfu,LIU Bo,et al. Study on spatial distribution prediction of mountain torrents in Longnan city based on MaxEnt combined with particle swarm optimization[J/OL]. China Industrial Economics,2023. [2023-10-20](2024-05-08) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=WHCH20231018002&dbname=CJFD&dbcode=CJFQ. (in Chinese with English abstract)] WANG Hao, NIU Quanfu, LIU Bo, et al. Study on spatial distribution prediction of mountain torrents in Longnan city based on MaxEnt combined with particle swarm optimization[J/OL]. China Industrial Economics, 2023. [2023-10-20](2024-05-08) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=WHCH20231018002&dbname=CJFD&dbcode=CJFQ. (in Chinese with English abstract)
[27] 牛全福,熊超,雷姣姣,等. 基于FFPI模型的甘肃陇南山区山洪灾害风险评价[J]. 自然灾害学报,2023,32(4):36 − 47. [NIU Quanfu,XIONG Chao,LEI Jiaojiao,et al. Risk assessment of flash flood disasters in Longnan mountain area of Gansu Province based on FFPI model[J]. Journal of Natural Disasters,2023,32(4):36 − 47. (in Chinese with English abstract)] NIU Quanfu, XIONG Chao, LEI Jiaojiao, et al. Risk assessment of flash flood disasters in Longnan mountain area of Gansu Province based on FFPI model[J]. Journal of Natural Disasters, 2023, 32(4): 36 − 47. (in Chinese with English abstract)
[28] 欧阳泽怡,李志辉,欧阳硕龙,等. 基于Maxent和ArcGIS的赤皮青冈在中国的潜在适生区预测[J]. 中南林业科技大学学报,2023,43(2):19 − 26. [OUYANG Zeyi,LI Zhihui,OUYANG Shuolong,et al. Prediction of the potential distribution of Cyclobalanopsis gilva in China based on the Maxent and ArcGIS model[J]. Journal of Central South University of Forestry & Technology,2023,43(2):19 − 26. (in Chinese with English abstract)] OUYANG Zeyi, LI Zhihui, OUYANG Shuolong, et al. Prediction of the potential distribution of Cyclobalanopsis gilva in China based on the Maxent and ArcGIS model[J]. Journal of Central South University of Forestry & Technology, 2023, 43(2): 19 − 26. (in Chinese with English abstract)
[29] 姚政宇,韩其飞,林彬. 基于最大熵模型的新疆主要有毒杂草分布区预测[J]. 生态学报,2023,43(12):5096 − 5109. [YAO Zhengyu,HAN Qifei,LIN Bin. Prediction of distribution area of main noxious and miscellaneous weeds in Xinjiang based on MaxEnt model[J]. Acta Ecologica Sinica,2023,43(12):5096 − 5109. (in Chinese with English abstract)] YAO Zhengyu, HAN Qifei, LIN Bin. Prediction of distribution area of main noxious and miscellaneous weeds in Xinjiang based on MaxEnt model[J]. Acta Ecologica Sinica, 2023, 43(12): 5096 − 5109. (in Chinese with English abstract)
[30] 刘明明,刘丹丹,芦星,等. 基于MaxEnt模型的新疆地区钝缘蜱适生区分布研究[J]. 中国媒介生物学及控制杂志,2023,34(5):671 − 678. [LIU Mingming,LIU Dandan,LU Xing,et al. MaxEnt model-based analysis of distribution of suitable habitats of Ornithodoros ticks in Xinjiang Uygur Autonomous Region,China[J]. Chinese Journal of Vector Biology and Control,2023,34(5):671 − 678. (in Chinese with English abstract)] DOI: 10.11853/j.issn.1003.8280.2023.05.015 LIU Mingming, LIU Dandan, LU Xing, et al. MaxEnt model-based analysis of distribution of suitable habitats of Ornithodoros ticks in Xinjiang Uygur Autonomous Region, China[J]. Chinese Journal of Vector Biology and Control, 2023, 34(5): 671 − 678. (in Chinese with English abstract) DOI: 10.11853/j.issn.1003.8280.2023.05.015
[31] 牛全福,冯尊斌,党星海,等. 黄土区滑坡研究中地形因子的选取与适宜性分析[J]. 地球信息科学学报,2017,19(12):1584 − 1592. [NIU Quanfu,FENG Zunbin,DANG Xinghai,et al. Suitability analysis of topographic factors in loess landslide research[J]. Journal of Geo-Information Science,2017,19(12):1584 − 1592. (in Chinese with English abstract)] NIU Quanfu, FENG Zunbin, DANG Xinghai, et al. Suitability analysis of topographic factors in loess landslide research[J]. Journal of Geo-Information Science, 2017, 19(12): 1584 − 1592. (in Chinese with English abstract)
[32] 王晓帆,段雨萱,金露露,等. 基于优化的最大熵模型预测中国高山栎组植物的历史、现状与未来分布变化[J]. 生态学报,2023,43(16):6590 − 6604. [WANG Xiaofan,DUAN Yuxuan,JIN Lulu,et al. Prediction of historical,present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica,2023,43(16):6590 − 6604. (in Chinese with English abstract)] WANG Xiaofan, DUAN Yuxuan, JIN Lulu, et al. Prediction of historical, present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica, 2023, 43(16): 6590 − 6604. (in Chinese with English abstract)
[33] 何学高,刘欢,张婧,等. 基于优化的MaxEnt模型预测青海省祁连圆柏潜在分布区[J]. 北京林业大学学报,2023,45(12):19 − 31. [HE Xuegao,LIU Huan,ZHANG Jing,et al. Predicting potential suitable distribution areas for Juniperus przewalskii in Qinghai Province of northwestern China based on the optimized MaxEnt model[J]. Journal of Beijing Forestry University,2023,45(12):19 − 31. (in Chinese with English abstract)] HE Xuegao, LIU Huan, ZHANG Jing, et al. Predicting potential suitable distribution areas for Juniperus przewalskii in Qinghai Province of northwestern China based on the optimized MaxEnt model[J]. Journal of Beijing Forestry University, 2023, 45(12): 19 − 31. (in Chinese with English abstract)
[34] 刘怡彤,郭慧,裴顺祥,等. 基于MaxEnt模型的天然元宝枫在我国的适生区区划及合理性分析[J]. 林业科学,2023,59(12):13 − 24. [LIU Yitong,GUO Hui,PEI Shunxiang,et al. Regionalization and rationality analysis of natural acer truncatum in China based on MaxEnt model[J]. Scientia Silvae Sinicae,2023,59(12):13 − 24. (in Chinese with English abstract)] DOI: 10.11707/j.1001-7488.LYKX20210823 LIU Yitong, GUO Hui, PEI Shunxiang, et al. Regionalization and rationality analysis of natural acer truncatum in China based on MaxEnt model[J]. Scientia Silvae Sinicae, 2023, 59(12): 13 − 24. (in Chinese with English abstract) DOI: 10.11707/j.1001-7488.LYKX20210823
[35] 周安晟,成彦丽,陈鸿,等. 基于MaxEnt模型预测含笑在中国的潜在适生区[J]. 安徽科技学院学报,2023,37(6):19 − 27. [ZHOU Ansheng,CHENG Yanli,CHEN Hong,et al. Prediction of potential suitable areas of Michelia figo in China based on MaxEnt model[J]. Journal of Anhui Science and Technology University,2023,37(6):19 − 27. (in Chinese with English abstract)] ZHOU Ansheng, CHENG Yanli, CHEN Hong, et al. Prediction of potential suitable areas of Michelia figo in China based on MaxEnt model[J]. Journal of Anhui Science and Technology University, 2023, 37(6): 19 − 27. (in Chinese with English abstract)
[36] 黄煜,谢婉丽,刘琦琦,等. 基于GIS与MaxEnt模型的滑坡易发性评价——以铜川市中部城区为例[J]. 西北地质,2023,56(1):266 − 275. [HUANG Yu,XIE Wanli,LIU Qiqi,et al. Landslide susceptibility assessment based on GIS and MaxEnt model:Example from central districts in Tongchuan City[J]. Northwestern Geology,2023,56(1):266 − 275. (in Chinese with English abstract)] HUANG Yu, XIE Wanli, LIU Qiqi, et al. Landslide susceptibility assessment based on GIS and MaxEnt model: Example from central districts in Tongchuan City[J]. Northwestern Geology, 2023, 56(1): 266 − 275. (in Chinese with English abstract)
[37] 王兰民,许世阳,王平,等. 2023年积石山6.2级地震诱发大规模黄土液化流滑的特征与启示[J]. 岩土工程学报,2024,46(2):235 − 243. [WANG Lanmin,XU Shiyang,WANG Ping,et al. Characteristics and lessons of liquefaction-triggered large-scale flow slide in loess deposit during Jishishan M6.2 earthquake in 2023[J]. Chinese Journal of Geotechnical Engineering,2024,46(2):235 − 243. (in Chinese with English abstract)] WANG Lanmin, XU Shiyang, WANG Ping, et al. Characteristics and lessons of liquefaction-triggered large-scale flow slide in loess deposit during Jishishan M6.2 earthquake in 2023[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(2): 235 − 243. (in Chinese with English abstract)
[38] NIU Quanfu,DANG Xinghai,LI Yuefeng,et al. Suitability analysis for topographic factors in loess landslide research:A case study of Gangu County,China[J]. Environmental Earth Sciences,2018,77(7):294. DOI: 10.1007/s12665-018-7462-y
-
期刊类型引用(1)
1. 刘星宇,朱立峰,孙建伟,贾煦,刘向东,黄虹霖,程贤达,孙亚柯,胡超进,张晓龙. 沟谷型泥石流特征参数的等代面积递归精细求解. 西北地质. 2024(03): 272-284 . 百度学术
其他类型引用(0)