汶川、芦山和鲁甸地震滑坡分布规律对比研究

汶川M_S8.0级地震、芦山M_S7.0级地震和鲁甸M_S6.5级地震均引发了大量的滑坡灾害。由于震级差异和地质地形条件的不同,地震滑坡分布情况有较大区别。本文综合已有的研究成果,从地震、地质和地貌3个方面,对比分析了地震滑坡的分布规律。结果表明:（1）3次地震滑坡数量和密度随着PGA和震级的增加而增加。汶川和鲁甸地震随烈度的增加,滑坡数量呈现递增的趋势。但芦山地震在较低烈度区也发育着大量滑坡。（2）断层影响滑坡分布的最大距离随着震级的增加而增加。在最大影响距离0.2倍的范围内,汶川地震分布有80%的滑坡,而其他两次地震仅30%。此外,汶川地震滑坡数量随断层距离呈指数衰减关系。（3）地震滑坡的分布受到地形的强烈影响。Ⅶ度及以上烈度区地形切割深度越大,地震触发的滑坡集中分布区域相对高差越大。同时,滑坡集中发育的坡度会随之增加。切割深度越大,地震滑坡更易发生在地势较陡的山脊或者上坡处,这可能与地形放大效应有关。     

2013年四川芦山地震次生山地灾害发育规律

2013年4月20日8时02分,青藏高原东缘龙门山南部地区发生了芦山地震(Ms 7.0),此次地震诱发了大量次生山地灾害。以地震重灾区宝兴作为研究区,利用卫星遥感影像、数字高程模型和高清航拍图像,以及崩塌滑坡数据统计分析,并结合野外调查研究,对区内次生山地灾害的空间分布与岩性、断裂和坡度关系进行了分析和探讨,总结了宝兴地区地震诱发的次生山地灾害发育规律:1以中小型崩塌滑坡为主,且沿省道S210集中分布;2崩塌滑坡主要发生在宝兴杂岩区浅表强风化层及第四系松散堆积层;3研究区内发育的五龙断裂和小关子断裂不是芦山地震的同震断裂;4该区域70%的崩塌滑坡发生在坡度大于30°的区域范围内,30~40°坡度段崩塌滑坡最为集中;5人类工程活动是宝兴地区次生山地灾害集中发育在S210省道两侧的主要原因;6在汶川地震和芦山地震2次地震及其余震的频繁加载作用下,宝兴地区崩塌滑坡的活动性增加,未来几年将是中小规模崩塌滑坡发育的高峰期。     

汶川地震触发崩塌滑坡数量及其密度特征分析

“5·12”汶川地震由于震级高、持续时间长、震区地质环境复杂,因而触发了大量的崩塌、滑坡。地震一年来,作者根据灾后对崩滑地质灾害的应急排查,并结合震后有限的ALOS、ASTER以及航空摄影等多源数据对地震重灾区的崩滑体数量进行了统计分析,获得确定性的崩滑灾害点有16704处,但是,由于排查范围及遥感数据的局限性,上述数据并不能代表这次地震触发崩塌滑坡总的数量。为了查明这个问题,本文在上述资料的基础上,采用不同烈度地区典型抽取样本统计的方法,获得了不同烈度区崩塌滑坡的发育密度,进而根据不同烈度区面积统计得出了本次地震触发崩塌滑坡数量的估计值,并与国际上若干大地震触发崩塌滑坡数量及分布面积进行了对比分析。最后,作者给出了灾区地震触发崩塌滑坡的密度分布图,并讨论了其分布特征。本文的研究结果表明：汶川地震触发的崩塌滑坡数量约为3．5万处,分布面积约为10×10^4km^2,发育密度最高在映秀一北川断裂上盘都江堰和彭州段以及沿岷江河谷的映秀一草坡段,约为5～6处／km^2。     

“4•20”芦山地震诱发地质灾害基本特征及其与“5•12”汶川地震对比分析

2013年4月20日8时2分,四川省雅安市芦山县(30.3°N,103.0°E)发生了Ms7.0级强烈地震,其是继2008年5月12日汶川Ms8.0特大地震后在龙门山断裂带上的又一次破坏性地震。文章分析了"4·20"芦山地震诱发地质灾害基本特征、形成机制和典型案例,并与"5·12"汶川地震诱发地质灾害进行了对比,在此基础上研究了两次地震地质灾害集中区断裂带活动性质、地貌特征、地形坡度、地震烈度等对崩塌滑坡的影响,主要取得了以下认识:①芦山地震诱发的地质灾害类型主要有崩塌(滚石)、滑坡、泥石流和不稳定斜坡四类,与汶川地震诱发灾害相比数量少,规模小,主要以中小型浅表层滑坡崩塌为主,且主要集中在高陡边坡和高山峡谷区;②芦山地震区宝兴县冷木沟和校场沟泥石流和2010年甘肃舟曲三眼峪、罗家峪泥石流灾害具有极为相似的地形地貌特征和孕灾条件;③芦山地震诱发地质灾害机制主要有:拉裂-崩滑-碰撞-铲刮-碎屑流、拉裂-崩落、震动-降雨-崩塌(滑动)-泥石流、震动-抛掷(滚动)四种,震后地质灾害是内外动力地质作用耦合叠加的结果;④芦山地震与汶川地震因其震级、断裂带性质及破碎程度、地形地貌等差异诱发的地质灾害存在明显的异同点,灾害类型基本相同,而方量、数量、危害等方面差异明显。     

川西杂谷脑“冰碛物”中软沉积物变形构造的成因

对四川理县杂谷脑“冰碛物”的地层结构进行了观察,对河湖相地层中的软沉积物变形构造进行了分析。综合研究表明,所谓杂谷脑晚更新世“冰碛物”是由泥石流、崩滑塌堆积物、堰塞湖沉积和河流相砂砾石层构成的多成因“混杂”堆积,湖相地层中发育的强烈褶皱和断裂是河岸陡壁岩石崩塌滑落导致古堰塞湖沉积物发生软沉积变形的结果。软沉积变形构造包括卷曲构造、液化泥块、阶梯状微断裂、球枕构造、火焰构造、落石沉陷构造、环状构造、同生断层和大型褶曲等。一次崩滑塌事件的典型沉积层序由崩塌体、不规则接触面和下伏的河湖相变形地层所组成。沿杂谷脑河上游发现的一系列大型崩塌褶曲构造以及伴生的多种典型的液化变形构造,指示可能是地震活动触发了群发性的崩塌、滑坡灾害事件。     

汶川地震滑坡与地震参数及地质地貌因素之间的相关关系

在对汶川地震诱发的典型滑坡进行野外调查和相关资料收集、分析和整理的基础上,对汶川地震滑坡与地震参数及坡度、岩土体特性等地质地貌因素之间的相关关系进行了统计分析。结果表明: (1)汶川地震滑坡主要发生在Ⅶ～Ⅺ烈度区, Ⅵ度及以下烈度区中发生的滑坡较少; (2)汶川地震滑坡主要发生在距震中300km的范围内,且距震中200km的范围内滑坡分布最为集中; (3)汶川地震滑坡的易发斜坡坡度为30～50,其中30～40是汶川地震滑坡发育最为敏感的坡度; (4)汶川地震滑坡主要发生在600～1500m的高程范围内,在600～1000m高程范围内的中低山和丘陵区滑坡分布最为集中; (5)砂泥岩、板岩、片岩、千枚岩等软岩类和土质类岩性是汶川地震滑坡的易发岩性,其次是软硬岩组合类,在统计的47个典型滑坡中,花岗岩、碳酸盐岩等硬岩类中发生的滑坡最少,而且由汶川地震直接导致复活的老滑坡也比较少。     

芦山地震前的重力扰动

继2008年汶川8.0级大地震“突然”发生后,2013年4月20日芦山7.0地震的“突然”发生,再一次表明大地震短临监测的重要性和迫切性。芦山地震后,对中国大陆构造环境监测网络（简称“陆态网络”）连续重力台站的重力观测资料进行了处理分析,首次发现震前存在两种不同性质的重力扰动信号,周期分别为8~11 s和6~8 s。其中,周期为8~11 s的重力扰动信号从大震前1天开始逐渐增大,并于震中距大于1 570 km时消失;周期为6~8 s的重力扰动信号从大震前7天开始逐渐增大,能够被所有台站监测到。两种重力扰动信号可能反映了芦山地震孕震过程的两个不同阶段,对进一步揭示芦山地震孕震机制具有重要的参考意义。     

汶川地震中擂鼓镇地区的滑坡崩塌规律及预测

地震滑坡影响因素与滑坡崩塌分布关系的研究,有助于认识地震滑坡崩塌的发育规律,进而对潜在的地震滑坡危险区段进行划分,为土地的合理使用提供支持.5.12汶川地震引发了大量的滑坡、崩塌、碎屑流等次生灾害,造成了严重的人员伤亡和经济财产损失.以受滑坡、崩塌灾害影响严重的北川县擂鼓镇约180 km2的地区作为研究对象,选取坡度、高程、坡向等影响因素进行确定性及面积发育率分析,探讨它们与滑坡、崩塌等灾害空间分布之间的关系.研究结果表明:在高程低于1 km的地段,滑坡崩塌的发生频率达13.5%,高于其它地段;坡向为东向、北东向、南东向的坡体的滑坡崩塌发生率较其它方向大;随着坡度的增大,滑坡、崩塌的分布也在增大,坡度大于30°的区域滑坡发生频率较高.采用2种方法对研究区进行地震滑坡危险性区划,获得大体一致的划分结果:①基于综合确定性系数与面积发育率方法分析的地震滑坡危险性区划结果中,约有66%的滑坡崩塌落入较高危险和高危险区域;②采用判别分析法获得的地震滑坡危险性区划结果中,约有73%的滑坡崩塌判定为不稳定区域.其中,判别分析法选用的地震动、坡度、曲率等因素在不同地区都对滑坡分布具普遍的影响作用.     

基于GPS速度场的四川地区1999～2018年应变场特征演化分析

基于1999～2018年GPS水平运动速度场数据,解算并分析了四川“Y”形构造区各周期网格速度场、地壳应变率场,并讨论了近20年尺度的地壳应变场演化过程。研究表明：1）2008年汶川地震前1999～2007期GPS速度场相对稳定,整体“Y”型构造区地壳运动变化不大,但汶川地震后龙门山断裂带发生较大变化,由4.0 mm/a增至10.0 mm/a。2）1999～2007年,整个四川“Y”型构造区应变场演化特征微弱,而汶川地震之后的两个周期,最大剪应变自龙门山山前断裂向西到汶川一带,形成了由高到低、平行于龙门山断裂带走向的高密度梯度带。龙门山断裂带以ES或EES向的主压应变为主,其量值变化范围为 5.0×10^-8 /a～12.0×10^-8 /a;鲜水河断裂由震前主拉应变,改为震后近EW向的主压应变特征。面膨胀结果则显示龙门山断裂带由震前低密度梯度带瞬间变为平行于龙门山断裂带走向的高密度变化区。3）2008年汶川地震和2013年芦山地震是最为重要的时刻分割点。近20年的应变率场变化,更似一个“时间—地壳构造运动”的大轮回,目前四川“Y”型构造区整体处于2008年汶川地震前较为稳定的活动周期。龙门山断裂带仍值得我们做出更为深入的研究。     

2008年以来中国5次典型地震事件诱发地质灾害主控因素分析

地震触发地质灾害主控因素的厘定是研究地震地质灾害和灾后应急救援响应的关键问题。笔者在野外典型地震滑坡实地调查和室内图件分析的基础上,以2008年以来我国先后发生的汶川地震、玉树地震、芦山地震、岷县漳县地震和彝良地震触发的地质灾害为研究对象,开展了地震地质灾害主控因素研究,主要取得以下认识：（1）地震地质灾害的空间展布特征主要受控于发震断裂的活动性质、地震动峰值加速度（PGA）、震区地形坡度和高差、距断裂带和水系的距离等4个方面。地震型地质灾害主要沿地震断裂带呈带状分布,逆冲型地震较走滑型地震诱发的高位、高速远程滑坡数量多,方量大,危害严重。（2）地震震级越大,极震区地形坡度越陡,地层风化破碎越严重,发生地质灾害的可能性就越大,造成的危害也更加严重,如2008年5·12汶川地震区。（3）地震地质灾害分布与地震烈度等级大小无必然联系,部分低烈度区因处于高山峡谷区而灾害频发。因此,震后地质灾害排查要充分利用地震地质灾害的成灾模式和成灾规律,震中区和地震高烈度区不一定是地质灾害极易发区。地震型地质灾害主控因素的分析为震后地质灾害空间分布范围预测及为灾后减灾应急响应方案制定提供了技术支撑。     

Long-range connective sandpile models and its implication to seismicity evolution

We propose a new variant of the sandpile model, the long-range connective sandpile model, by means of introducing randomly internal connections between two separated distant cells. The long-range connective sandpile model demonstrates various self-organized critical states with different scaling exponents in the power-law frequency-size distributions. We found that a sandpile with higher degree of randomly internal long-range connections is characterized by a higher value of the scaling exponent for the distribution, whereas the nearest neighbor sandpile is possessed of a lower scaling exponent. Our numerical experiments on the long-range connective sandpile models imply that higher degree of random long-range connections makes the earthquake fault system more relaxant that releases accumulated energy more easily and produces fewer catastrophic events, whereas lower degree of long-range connections possibly caused by fracture healing very likely motivates accelerating seismicity of moderate events.     

Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake,China

The Ms 8.0 Wenchuan earthquake of May 12, 2008 is one of the most disastrous earthquakes in China. The earthquake triggered tens of thousands of landslides over a broad area, including shallow, disrupted landslides, rock falls, deep-seated landslides, and rock avalanches, some of which buried large sections of some towns and dammed the rivers. The purpose of this study is to investigate correlations between the occurrence of landslides with geologic and geomorphologic conditions, and seismic parameters. Over 56,000 earthquake-triggered landslides, with a total area of 811 km², are interpreted using aerial photographs and remote sensing images taken following the earthquake. The spatial distribution of these landslides is analyzed statistically using both landslide-point density (LPD), defined as the number of landslides per square kilometer, and landslide-area density (LAD), the percentage of the area affected by landslides, to determine how the occurrence of landslides correlates with distance from the epicenter, distance from the major surface rupture, seismic intensity and peak ground acceleration (PGA), slope angle, slope aspect, elevation, and lithology. It is found that both LAD and LPD have strong positive correlations with slope steepness, distance from the major surface rupture and seismic intensity, and that Pre-Sinian schist, and Cambrian sandstone and siltstone intercalated with slate have the most concentrated landslide activities, followed by the Permian limestone intercalated with shale, and Devonian limestone. Statistical analyses also indicate that the major surface rupture has influence on the spatial distribution of landslides, because LAD and LPD are relatively higher on the hanging wall than on the footwall. However, the correlation between the occurrence of landslides with distance from the epicenter of the earthquake is complicated, rather than a relatively simple negative correlation as found from other reported cases of earthquakes. This is possibly due to complicated rupture processes of the earthquake.     

Landslides triggered by the 2016 Mj 7.3 Kumamoto,Japan, earthquake

The aim of this study is to establish a detailed and complete inventory of the landslides triggered by the Mj 7.3 (Mw 7.0) Kumamoto, Japan, earthquake sequence of 15 April 2016 (16 April in JST). Based on high-resolution (0.5–2 m) optical satellite images, we delineated 3,467 individual landslides triggered by the earthquake, occupying an area of about 6.9 km². Then they were validated by aerial photographs with very high-resolution (better than 0.5 m) and oblique field photos. Of them, 3,460 landslides are distributed in an elliptical area about 6000 km², with a NE-SW directed 120-km-long long axis and a 60-km-long NW-SE trending short axis. Most of the landslides are shallow, disrupted falls and slides, with a few flow-type slides and rock and soil avalanches. The analysis of correlation between the landslides and several control factors shows the areas of elevation 1000–1200 m, stratum of Q₃-Hvf, seismic intensity VIII and VIII+, and peak ground acceleration (PGA) 0.4–0.6 g register the highest landslide abundance. This study also discussed the relationship between the spatial pattern of the landslides and the seismotectonic structure featured by a strike-slip fault with a normal component and the volcanism in the study area.     

Statistical evaluation of the effect of earthquake with other related factors on landslide susceptibility: using the watershed area of Shihmen reservoir in Taiwan as a case study

Landslide susceptibility evaluation is one of the most important issues in watershed management. After an earthquake, the landslide susceptibility decreases functionally with increases in the distance from the epicenter. Under the same rainfall intensity, landslides are more likely to occur in an area where earthquakes occur more frequently. However, the questions of how much an earthquake should be weighted and how to evaluate the effects of an earthquake still need to be studied. To understand how earthquakes affect rainfall-triggered landslides, the horizontal peak ground acceleration (PGA) data from the Central Weather Bureau Seismic Network is used as the earthquake factor and combined with other factors to determine the weight of earthquakes in landslide susceptibility using logistic regression. The results indicate that the ability of landslide prediction is better when considering the earthquake factor. This study also proved that although there are no co-seismic landslides (after earthquakes) in the study area, the earthquake factor is still required to increase the model accuracy. PGA has been described as a usable factor. In areas with frequent earthquakes and high geological activity, when using historical data to evaluate landslide susceptibility, the earthquake factor should be taken into consideration to prevent errors.     

2008年汶川地震滑坡详细编目及其空间分布规律分析

最新研究成果表明, 2008年5月12日汶川MS 8.0级地震触发了超过197000处滑坡。首先,基于GIS与遥感技术构建了汶川地震滑坡的3类编目图,分别为单体滑坡面分布数据、滑坡中心点位置和滑坡后壁点位置。构建方法为基于地震前后高分辨率遥感影像的目视解译方法,区分单体滑坡并圈定其边界,对滑坡后壁进行识别与定点,并开展了部分滑坡的野外验证工作。这些滑坡分布在一个面积大约为110000km2的区域内,滑坡总面积约为1160km2。选择一个面积约为44031km2的区域作为研究区,区内滑坡数量为196007个,滑坡面积为1150.622km2,这是最详细完整的汶川地震滑坡编录成果,也是单次地震事件触发滑坡最多的记录。其次,开展研究区内的地震滑坡空间分布规律的研究。基于滑坡面与滑坡中心点分别构建滑坡空间分布面积密度图与点密度图,结果表明:滑坡多沿着映秀北川断裂分布,多发生在断裂的上盘。滑坡的高密度区位于映秀北川同震地表破裂的南西段(映秀镇与北川县之间)的上盘区域,这一区域恰对应着逆冲分量为主的断裂上盘,表明逆冲断裂对上盘区域发生滑坡的极强烈的控制作用,而该区域正是形变最大的区域,因此说明是地震滑坡发生的强烈控制作用。基于滑坡面密度(LAP)、滑坡中心点密度(LCND)与滑坡后壁点密度(LTND)这3个衡量指标,使用统计分析方法,评价了汶川地震滑坡与地震参数、地质参数、地形参数的关系。结果表明:LAP、LCND与LTND这3个衡量指标与坡度、地震烈度与PGA存在明显的正相关关系; 与距离震中、距离映秀北川同震地表破裂存在负相关关系; 斜坡曲率越接近0,滑坡越不易发生; LAP、LCND与LTND的高值高程区间为1200～3000m; 滑坡发生的优势坡向为E、SE、S方向; 滑坡发育的易发岩性为砂岩与粉砂岩(Z)、花岗岩; 滑坡与坡位的相关关系不太明显。统计结果还表明LCND与LTND两个衡量指标的差异对地震与地质因子不敏感,而对地形因子较敏感。最后将本文的统计结果与以往的汶川地震滑坡空间分布规律统计成果进行了一些对比,对比结果表明,对于某些因子,如高程、岩性、距离震中、距离映秀北川断裂的统计分析结果,采用不完整的滑坡分布数据或点数据,与采用较完整的滑坡分布面数据会有一定的差异,这种差异并未出现在针对坡度与坡向等因子的统计对比结果中。总之,作者认为一个完备、详细的地震滑坡分布面要素编目图是地震滑坡空间分布规律定量分析、危险性定量分析与滑坡控制的地震区地貌演化研究的重要基础,否则,与实际情况相比,得到统计结果会有一定的偏差,本文的研究成果与以往成果的对比结果证明了这一点。     

走滑断裂型地震诱发的滑坡在断裂两盘的空间分布差异

以玉树地震滑坡为实例,选择高程、坡度、坡向、坡位、水系、地层岩性、同震地表破裂、地震动峰值加速度(PGA)8个因子,以地震滑坡面积百分比(LAP)与滑坡点密度(LND)为指标,研究走滑断裂型地震诱发滑坡在断裂两盘的空间分布差异。在分析这些影响因子的断裂两盘的差异的基础上,基于LAP与LND两个指标详细分析断裂两盘每个因子内部级别滑坡发育的情况。结果表明,总体上北盘的滑坡较南盘发育,除了个别因子级别内南北盘滑坡差别较大外,大部分表现为南北盘滑坡分布情况类似。总之,玉树地震滑坡在断裂两盘的空间分布基本类似,只是在某些因子分级内存在一定的差异。     

Scale-invariance of soil moisture variability and its implications for the frequency-size distribution of landslides

Power spectral analyses of soil moisture variability are carried out from scales of 100 m to 10 km on the microwave remotely-sensed data from the Washita experimental watershed during 1992. The power spectrum S(k) has an approximate power-law dependence on wave number k with the exponent −1.8. This behavior is consistent with the behavior of a stochastic differential equation for soil moisture at a point, and it has important consequences for the frequency-size distribution of landslides. We present the cumulative frequency-size distributions of landslides induced by precipitation in Japan and Bolivia as well as landslides triggered by the 1994 Northridge, California earthquake. Large landslides in these regions, despite being triggered by different mechanisms, have a cumulative frequency-size distribution with a power-law dependence on area with an exponent ranging from −1.5 to −2. We use a soil moisture field with the above statistics in conjunction with a slope stability analysis to model the frequency-size distribution of landslides. In our model, landslides occur when a threshold shear stress dependent on cohesion, pore pressure, internal friction and slope angle is exceeded. This implies a threshold dependence on soil moisture and slope angle since cohesion, pore pressure and internal friction are primarily dependent on soil moisture. The cumulative frequency-size distribution of domains of shear stress greater than a threshold value with soil moisture modeled as above and topography modeled as a Brownian walk is a power-law function of area with an exponent of −1.8 for large landslide areas. This distribution is similar to that observed for landslides. The effect of strong ground motion from earthquakes lowers the shear stress necessary for failure, but does not change the frequency-size distribution of failed areas. This is consistent with observations. This work suggests that remote sensing of soil moisture can be of great importance in monitoring landslide hazards and proposes a specific quantitative model for landslide hazard assessment.     

Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake

On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km². The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m². The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q₄ al-pl, N, and T₃ kn ¹ have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.     

2013年7月22日岷县漳县MS6.6级地震滑坡编录与空间分布规律分析

2013年7月22日,中国甘肃省岷县漳县交界地区发生MS6.6级地震。地震触发了大量的滑坡,主要类型为小型的黄土崖崩、滑、倾,还有一些深层连贯型滑坡、大型土质流滑、侧向滑移等地震滑坡类型。本文主要基于地震滑坡野外考察与高分辨卫星影像人工目视解译方法,开展本次地震触发滑坡的编录图制作工作,并基于编录成果开展地震滑坡空间分布规律分析工作。滑坡编录结果表明此次地震至少触发了2330处滑坡。基于GIS平台下开展了地震滑坡与地形控制因子、地质控制因子与地震控制因子的关系统计。结果表明岷县漳县地震滑坡受地形因子的控制作用较小; 受地层岩性的控制作用较大。其中1401处滑坡(占总滑坡的60.13%)分布在下伏基岩为古近系下统地层(Eb)砾岩与砂岩的区域内,其中滑坡密度是所有地层分类中最高的,达到22.78个km-2。地震滑坡与PGA的统计关系并不明显。地震滑坡主要集中在一个与发震断层走向一致的长条形区域内,长约14km,宽约5km。该区域内滑坡数量为1864个,占所有滑坡数量的80%,表明了岷县漳县地震滑坡空间分布的密集特征。文章获得的基于地震事件的区域滑坡编录成果是后续地震滑坡相关科学研究的重要基础与核心数据; 空间分布规律分析成果可为地震区滑坡与泥石流防灾减灾提供科学参考。