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Volume 47 Issue 8
Aug.  2022
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Article Contents

ZHANG Chenglong, LI Zhenhong, ZHANG Shuangcheng, WANG Jianwei, ZHAN Jiewei, LI Xinlong, LIU Zhenjiang, DU Jiantao, CHEN Bo, MENG Ling'en, ZHU Wu, FU Xin, YU Chen, ZHOU Bao, SUI Jia, ZHAO Lijiang, WANG Zushun, XIN Bingchang, XU Jiangming, ZHANG Qin, PENG Jianbing. Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1257-1270. doi: 10.13203/j.whugis20220243
Citation: ZHANG Chenglong, LI Zhenhong, ZHANG Shuangcheng, WANG Jianwei, ZHAN Jiewei, LI Xinlong, LIU Zhenjiang, DU Jiantao, CHEN Bo, MENG Ling'en, ZHU Wu, FU Xin, YU Chen, ZHOU Bao, SUI Jia, ZHAO Lijiang, WANG Zushun, XIN Bingchang, XU Jiangming, ZHANG Qin, PENG Jianbing. Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1257-1270. doi: 10.13203/j.whugis20220243

Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing

doi: 10.13203/j.whugis20220243
Funds:

The National Key Research and Development Program of China 2020YFC1512000

the Shaanxi Province Science and Technology Innovation Team 2021TD-51

ESA-MOST DRAGON-5 Project 59339

the Fundamental Research Funds for the Central Universities 300102260301

the Fundamental Research Funds for the Central Universities 300102262902

the Fundamental Research Funds for the Central Universities 300102261108

the Fundamental Research Funds for the Central Universities 300203211261

More Information
  • Author Bio:

    ZHANG Chenglong, PhD candidate, specializes in geohazard detection with InSAR. E-mail: chenglongzhang136@163.com

  • Corresponding author: LI Zhenhong, PhD, professor. E-mail: zhenhong.li@chd.edu.cn
  • Received Date: 2022-05-04
  • Publish Date: 2022-08-05
  •   Objectives  On 8th January 2022, a large earthquake (Mw 6.7) struck Menyuan County, Qinghai, China, causing serious damage to Lanzhou-Xinjiang high speed railway and forcing the closure of the railway for repairs, which has attracted highly domestic and international attention.  Methods  We presented a technical framework to determine earthquake surface ruptures by integrating optical, synthetic aperture radar (SAR) and unmanned aerial vehicle (UAV) images as well as light detection and ranging (LiDAR) data, and evaluated its damage to traffic networks. Firstly, we acquired a range of datasets including GF-1, GF-7, Sentinel-2 optical images and Sentinel-1A SAR images. GF-1 and GF-7 images were used to determine the spatial distribution characteristic of the surface ruptures. Secondly, we employed to estimate 2D surface displacement fields using optical pixel offset technique. One in the east-west (EW) direction and the other in the south-north (SN) direction. SAR pixel offset technique was utilized to acquire surface displacements in the range and azimuth directions whilst interferometric SAR(InSAR) was mainly for surface displacements in the radar line of sight (i.e. the range direction). Structure from motion (SfM) was used to process UAV images to obtain high precision digital surface models (DSMs). Finally, all the abovementioned information was used to precisely determine the spatial distribution and surface displacement characteristics of the earthquake surface ruptures.  Results  Our results show that the maximum surface displacement in the EW direction was about 2.0 m, the maximum in the range direction was approximately 1.5 m, and the total length of the surface ruptures was around 36.22 km. Furthermore, we performed an assessment of traffic inefficiency in Menyuan and its surrounding areas based on the distribution of historical geohazards as well as the earthquake surface ruptures using machine learning methods support vector machine models.  Conclusions  The Menyuan earthquake had the greatest impacts on highways, and the least impacts on rural roads. The southeast sections of the major highways G0611 and G338 had high risks. The technical framework demonstrated in this paper appears to be promising to precisely map surface ruptures, which in turn will directly benefit to earthquake disaster reduction.
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Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing

doi: 10.13203/j.whugis20220243
Funds:

The National Key Research and Development Program of China 2020YFC1512000

the Shaanxi Province Science and Technology Innovation Team 2021TD-51

ESA-MOST DRAGON-5 Project 59339

the Fundamental Research Funds for the Central Universities 300102260301

the Fundamental Research Funds for the Central Universities 300102262902

the Fundamental Research Funds for the Central Universities 300102261108

the Fundamental Research Funds for the Central Universities 300203211261

Abstract:   Objectives  On 8th January 2022, a large earthquake (Mw 6.7) struck Menyuan County, Qinghai, China, causing serious damage to Lanzhou-Xinjiang high speed railway and forcing the closure of the railway for repairs, which has attracted highly domestic and international attention.  Methods  We presented a technical framework to determine earthquake surface ruptures by integrating optical, synthetic aperture radar (SAR) and unmanned aerial vehicle (UAV) images as well as light detection and ranging (LiDAR) data, and evaluated its damage to traffic networks. Firstly, we acquired a range of datasets including GF-1, GF-7, Sentinel-2 optical images and Sentinel-1A SAR images. GF-1 and GF-7 images were used to determine the spatial distribution characteristic of the surface ruptures. Secondly, we employed to estimate 2D surface displacement fields using optical pixel offset technique. One in the east-west (EW) direction and the other in the south-north (SN) direction. SAR pixel offset technique was utilized to acquire surface displacements in the range and azimuth directions whilst interferometric SAR(InSAR) was mainly for surface displacements in the radar line of sight (i.e. the range direction). Structure from motion (SfM) was used to process UAV images to obtain high precision digital surface models (DSMs). Finally, all the abovementioned information was used to precisely determine the spatial distribution and surface displacement characteristics of the earthquake surface ruptures.  Results  Our results show that the maximum surface displacement in the EW direction was about 2.0 m, the maximum in the range direction was approximately 1.5 m, and the total length of the surface ruptures was around 36.22 km. Furthermore, we performed an assessment of traffic inefficiency in Menyuan and its surrounding areas based on the distribution of historical geohazards as well as the earthquake surface ruptures using machine learning methods support vector machine models.  Conclusions  The Menyuan earthquake had the greatest impacts on highways, and the least impacts on rural roads. The southeast sections of the major highways G0611 and G338 had high risks. The technical framework demonstrated in this paper appears to be promising to precisely map surface ruptures, which in turn will directly benefit to earthquake disaster reduction.

ZHANG Chenglong, LI Zhenhong, ZHANG Shuangcheng, WANG Jianwei, ZHAN Jiewei, LI Xinlong, LIU Zhenjiang, DU Jiantao, CHEN Bo, MENG Ling'en, ZHU Wu, FU Xin, YU Chen, ZHOU Bao, SUI Jia, ZHAO Lijiang, WANG Zushun, XIN Bingchang, XU Jiangming, ZHANG Qin, PENG Jianbing. Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1257-1270. doi: 10.13203/j.whugis20220243
Citation: ZHANG Chenglong, LI Zhenhong, ZHANG Shuangcheng, WANG Jianwei, ZHAN Jiewei, LI Xinlong, LIU Zhenjiang, DU Jiantao, CHEN Bo, MENG Ling'en, ZHU Wu, FU Xin, YU Chen, ZHOU Bao, SUI Jia, ZHAO Lijiang, WANG Zushun, XIN Bingchang, XU Jiangming, ZHANG Qin, PENG Jianbing. Surface Ruptures of the 2022 Mw 6.7 Menyuan Earthquake Revealed by Integrated Remote Sensing[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1257-1270. doi: 10.13203/j.whugis20220243
  • 中国位于欧亚大陆东南部地区,地处环太平洋和欧亚地震带之间,受太平洋板块、菲律宾板块和印度洋板块的挤压作用,地震活动范围广、强度大、频率高,在全球大陆地区的大地震中,约有1/4~1/3发生在中国[1]。较强的地震伴随着巨大的能量波动,不仅对灾区的居民、房屋、道路和桥梁等造成直接危害,还会诱发山体滑坡、泥石流、崩塌等地质灾害,而且带来长期潜伏、不定期爆发的二次伤害[2-6]。为了减少人员伤亡和经济损失,利用综合遥感技术进行地震应急响应,特别是对地震破坏情况进行快速评估是当前国际上通用的一种做法。

    近年来,随着国内外合成孔径雷达(synthetic aperture radar,SAR)卫星的不断发射,SAR遥感影像越来越多地应用于地震和地质灾害的研究。其中,合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)技术覆盖面广,工作不受时间、气候和云雾等的影响[7],在地震、滑坡、泥石流等领域的研究具有巨大优势[8-16]。例如,文献[17]利用野外勘察与测量对1999年中国台湾Chi-Chi地震的近100 km破裂带进行研究,详细分析了地表破裂带的几何地貌、同震断层倾角及同震位移;文献[18]基于ALOS/PALSAR(advanced land observing satellite/phased array L-band synthetic aperture radar)影像利用InSAR技术和野外调查对2010年玉树地震破裂带进行识别分析;文献[19]基于0.5 m的WorldView-1和GeoEye-1影像,利用光学像素偏移量技术获取2010年Darfield地震和2011年Christchurch地震所造成的地表破裂带的形变特征;文献[20]基于ALOS/PALSAR影像,利用SAR像素偏移量技术对2008年汶川地震整个地表破裂带进行分析,得到破裂带的空间分布和形变特征;文献[21]基于野外调查和无人机影像目视解译对2021-05青海玛多地震地表破裂带进行分析,确定了破裂带的空间分布特征及断层构造。

    目前,较多学者利用SAR或光学遥感影像,再结合野外调查对地表破裂带进行研究。不同方法具有各自的优缺点,通过高分光学遥感影像目视解译可以比较直观地得到地表破裂的空间分布特征,但是其效率较低。自动化识别可以弥补这一缺点,但以上方法均无法获取地表破裂带的形变特征。InSAR技术、SAR和光学像素偏移量技术可以获取地表破裂带的形变信息。InSAR技术可以精密确定卫星视线向(line of sight,LOS)同震形变信息,但是当地表破裂带位于植被覆盖密集区或者其形变梯度过大时,可能导致失相干现象。基于SAR和高分光学遥感的像素偏移量技术可以较好地解决地表破裂带形变梯度较大的问题,获取较精密的二维形变信息。但对于像素偏移量技术,地表特征点(如山脊、湖岸线、公路等)的分布是关键,可以确保即使在失相干的区域也可提取像素偏移量。另外,影像的空间分辨率越高,可探测到的形变量越精密,形变信号越小。综上所述,以多源遥感数据为基础,集成多技术的优点,才是获取震后地表破裂带的有效手段。

    本文以2022-01-08T01:45青海门源地震为例,首先,利用高分1号(GF-1)和高分7号(GF-7)光学遥感影像进行遥感解译,采用光学像素偏移量技术对Sentinel-2影像进行处理,得到东西向和南北向的地表形变场。其次,基于Sentinel-1A影像,利用InSAR和SAR像素偏移量技术获取地震引起的地表形变场。然后,综合利用GF-1和GF-7光学遥感影像、光学/SAR像素偏移量、InSAR同震形变场、相干信息和无人机影像确定研究区域的地表破裂带。最后,基于支持向量机(support vector machines,SVM)模型,结合地表破裂带和历史地质灾害点的分布对门源地区的公路交通网进行分析。

  • 青海省是中国主要地震分布地区之一,门源县位于青海省中北部地区,受亚欧板块和印度板块的相互挤压影响,周围地形崎岖,多分布走滑断层和逆冲带组合,构造活动频繁[22]。1956-02-11张掖市山丹县发生Mw 7.0地震[23];1986-08-26,该区域发生Mw 6.0地震,震源深度约为13 km[24]。2016-01-21门源县附近发生Mw 5.9地震,震源深度约为10 km[25]。2022-01-08T01:45,经中国地震网测定,门源县发生Mw 6.7地震;截至01-13T01:00,共记录3.0级及以上余震21次,此次地震及余震造成青海省房屋、道路桥梁不同程度受损;截至01-09,门源、祁连、刚察县共1 662户5 831人受灾,9人受伤,217间房屋被严重损坏,省道损坏3.3 km,乡村道路损坏8 km,桥梁损坏3座。此次门源Mw 6.7地震位于青藏高原东北缘的昌马堡-古浪-海原构造带,震源机制解显示本次地震为左旋走滑型地震型[26-30]图 1为研究区域多源卫星遥感影像的覆盖范围。

    Figure 1.  Study Area and Coverage of Satellite Remote Sensing Images

    为了快速确定门源地震地表破裂带的概况,本文主要采用高分光学遥感影像和SAR影像。震后第2天获取到由中国资源卫星应用中心提供的GF-1和GF-7光学遥感数据,震后第3天获取到由欧空局免费提供的Sentinel-1A SAR影像,震后第4天获取到欧空局免费提供的Sentinel-2光学遥感影像。本文实验的遥感影像基本信息如表 1所示,包括3景空间分辨率为0.8 m、宽幅为20 km的GF-7影像,2景空间分辨率为2.0 m、宽幅为60 km的GF-1影像。对比震前的2020-11-30 GF-7影像、2021-12-21 GF-1影像与震后的2022-01-08 GF-7影像、2022-01-09 GF-1影像,并进行光学遥感目视解译;Sentinel-2影像包括震前2022-01-02和2022-01-05,震后2022-01-10和2022-01-12共4景影像;Sentinel-1A影像包括2021-12-29和2022-01-10的两景降轨影像。

    数据类型 采集日期 空间分辨率 幅宽/km 备注
    GF-7 2021-11-30 0.8 m 20 震前
    GF-1 2021-12-21 2.0 m 60 震前
    GF-7 2022-01-08 0.8 m 20 震后
    GF-7 2022-01-08 0.8 m 20 震后
    GF-1 2022-01-09 2.0 m 60 震后
    Sentinel-2 2022-01-02 10 m 290 震前
    Sentinel-2 2022-01-05 10 m 290 震前
    Sentinel-2 2022-01-10 10 m 290 震后
    Sentinel-2 2022-01-12 10 m 290 震后
    Sentinel-1A 2021-12-29 5 m×20 m 250 震前
    Sentinel-1A 2022-01-10 5 m×20 m 250 震后

    Table 1.  Remote Sensing Images Used in This Study

  • 图 2为利用多源卫星遥感影像快速解译地震地表破裂带的技术框架图,具体步骤如下:

    Figure 2.  Technical Framework for Mapping Surface Ruptures

    1)快速收集多源卫星遥感影像,包括高分光学、WordView系列、Landsat系列、Sentinel-2等卫星高分光学遥感影像和GF-3、Sentinel-1A/B、ALOS-1/2、TerraSAR-X等卫星SAR影像。

    2)基于高分光学遥感影像的地表破裂带探测,主要技术包括光学遥感目视解译和自动化识别以及光学像素偏移量技术。利用高分辨率光学遥感目视解译或自动化识别获取地表破裂带的空间分布特征,但只适用于纹理和色度明显区域,对于纹理和色度不明显的区域容易发生漏判现象。光学像素偏移量技术可以获取地表破裂带形变梯度较大区域的二维形变量(东方向和北方向),但无法确切获取破裂形变梯度较小区域的位置和形变信息。

    3)基于SAR影像的地表破裂带探测,主要技术包括InSAR和SAR像素偏移量技术。InSAR可以精密确定同震形变,但在植被过密或者形变梯度过大的区域,容易造成失相干[5, 31-32]。SAR像素偏移量技术可以弥补传统InSAR的缺点,获取形变梯度较大区域的形变信息,进而精密确定地表破裂带的位置。SAR与光学像素偏移量技术相似,可获取卫星视线方向和飞行方向形变量。

    4)基于无人机(unmanned aerial vehicle,UAV)影像和激光雷达(light detection and ranging,LiDAR)点云数据进行地表破裂带探测,主要利用目视解译和运动结构恢复(structure from motion,SfM)的方法探测地貌的变化[33],进一步验证和确定地表破裂带的位置。

    5)综合多源遥感影像信息,将不同技术的地表破裂识别结果进行优势互补,精密确定地表破裂位置和形变量。

  • 首先对高分光学遥感影像的全色影像和多光谱影像进行正射校正,然后利用GS(Gram-Schmidt)全色锐化方法对全色和多光谱影像进行融合,获取卫星遥感影像进行光学遥感解译[34]。在利用高分光学遥感对地表破裂带进行识别时,主要以断裂带的空间信息分析为主,经过前期预处理和图像增强后,地物的噪声得到有效抑制,辨识度较高。通过人工判识,发现地表破裂带具有较高的辨识度,与其周围的结构特征和岩石成分等差异较大,这些明显的判别标志在高分辨率光学遥感影像上显示出不同的纹理和色调,以此提取研究区域地表破裂带的初始概况及周围损毁情况。

    利用ENVI软件中的光学遥感影像配准与关联功能模块(COSI-Corr)对高分光学遥感影像进行处理[35]。首先将地震前后的光学遥感影像进行裁剪配准,然后对影像进行亚像素相关性匹配计算。参数设置如下:搜索窗口大小为32×32像素,移动步长为8×8像素,通过设置掩膜阈值为0.9来降低失相关噪声,迭代次数为2。初步得到的二维形变场中存在轨道和条带等误差,利用一次多项式曲面拟合模型去除轨道误差[36],采用传统的均值相减法去除条带误差[37],从而获得研究区域的东西(east-west,EW)向和南北(south-north,SN)向形变场[38]

  • 采用InSAR技术对SAR影像进行处理,以震前获取的SAR影像作为主影像,震后获取的SAR影像作为辅影像。首先,利用GAMMA软件[39]对SAR影像进行干涉处理,完成主辅影像的粗配准和精配准;结合30 m分辨率的航天飞机雷达地形测绘任务(shuttle radar topography mission,SRTM)数字高程模型(digital elevation model,DEM)数据去除干涉图的地形和平地效应[39],并采用自适应滤波算法对干涉影像进行空间滤波[40]。然后,利用最小费用流(minimum coat flow,MCF)方法对干涉图进行相位解缠[41],基于通用型大气改正在线服务(generic atmospheric correction online service for InSAR,GACOS)的天顶对流层延迟产品(zenith tropospheric delay,ZTD)削弱大气对流层延迟影响[42-44]。最后,将相位值转换成雷达LOS的位移,通过地理编码将形变结果从雷达坐标系转换到地理坐标系,从而得到此次地震降轨影像视线向的同震形变场。

    地震引发地表破裂,在断层附近区域(即近场)形变梯度过大时,往往造成失相干,导致无法利用InSAR技术提取相位信息。为了精密获取地表破裂带的位置和形变信息,可考虑采用基于SAR影像强度信息即后向散射强度的像素偏移量技术。首先搜索高精度配准之后的SAR单视复数产品(single looking complex,SLC)主辅影像窗口之间最大的互相关系数,计算相应像素之间的偏移量。基于GAMMA软件的偏移跟踪模块[45],设置偏移搜索窗口为300×60像素,互相关函数窗口为32×32像素,相干阈值为0.1,计算得到轨道差异、地形起伏引起的偏移分量、形变引起的偏移分量和其他噪声在内的像素偏移量。然后联合SRTM DEM数据,采用最小二乘准则构建的系统偏移模型去除地形和轨道引起的偏移分量,使用中值滤波器(9×9)过滤掉空间不相关的噪声,最后提取出距离向和方位向的形变场[46]。SAR像素偏移量技术的精度主要取决于SAR影像的空间分辨率,且均匀的搜索窗口以及地形误差等都会影响形变场精度[47-48]。结合SAR像素偏移量距离向和方位向形变场,可确定地表破裂带的形变与位置信息。

  • 首先根据测区地形资料布设航带式仿地三维航线,在区域内采集影像时,利用无人机内置的全球导航卫星系统/惯性导航系统(global navigation satellite system/inertial measurement unit,GNSS/IMU)获取像片拍摄时相机的位置和姿态信息;然后利用Agisoft Metashape Professional软件生成三维地形模型和正射影像[49]。该软件主要基于SfM-MVS(structure-from-motion and multi-view stereo)算法,利用不同视角的重叠影像重建三维模型和像片位姿。SfM算法首先以影像集作为输入,提取特征点并基于像片的位姿信息进行特征匹配;然后通过对极约束构建立体像对并进行特征匹配几何验证;最后使用光束法平差最小化重投影误差,得到稀疏点云和像片位姿。MVS算法基于解算得到的像片位姿信息进行密集匹配,得到密集点云。密集点云通过栅格插值算法得到高分辨率的数字地表模型(digital surface model,DSM);影像集基于像片位姿参数进行正射校正和图像镶嵌,得到高分辨率的数字正射影像(digital orthophoto map,DOM)。最后基于研究区域的高分辨率DSM和DOM解译地震的地表破裂带[50-52]

    地震引发的地表破裂带位于城区活动或森林覆盖区域时,上述几种技术往往无法较好地解译地表破裂带,而机载LiDAR技术可以对地表破裂带进行地貌的全方位、高精度、三维直接测量,近实时地提供整个破裂带的高精度DEM数据。机载LiDAR是一种主动式全新空间测量技术,将激光探测和测距系统搭载在飞机上,集激光扫描仪、GPS(global positioning system)和IMU于一体,能够快速准确获取目标对象的三维坐标、回波强度、回波次数等信息。首先,利用机载LiDAR飞行平台对研究区域进行点云数据的采集,通过移动测量操控软件纠正系统姿态,处理GNSS数据,进行点云数据的解算,进一步生成包含地物、地面点三维坐标信息las格式的高精度点云数据。然后,采用商用处理软件如TerraSolid[53]进行数据预处理,利用三角网渐进加密滤波算法对原始点云数据进行滤波[54],对滤波后的地面点以手动分类的方式进行点云去噪,生成最终的地面点云数据,并基于ArcGIS平台对导入的点云数据采用平均值以及自然邻域插值法生成相对应的DEM[55]。最后,利用近实时的高分辨率DEM解译地震的地表破裂带。

  • 根据研究区内地表破裂带的分布和历史地质灾害点,首先结合地质灾害成灾机理,基于地形地貌选取高程、坡度、坡向、曲率、平面曲率、剖面曲率6个地形因子,基于地质选取距断层距离和距地表破裂带距离2个地质灾害影响因子,基于人类活动、水文等方面选取距道路距离、距河流距离2个地质灾害影响因子。然后对所选10个地质灾害影响因子进行特征选择,通过计算各影响因子的信息增益比[56],发现信息增益比值均大于0,表明所选因子对道路均有影响。最后利用灾害点和影响因子信息,基于ArcGIS平台制作灾害样本与非灾害样本,并以7∶3的比例随机划分训练样本与测试样本。通过机器学习方法学习灾害因子间的特征,建立灾害因子与灾害发生概率间的关系,使用训练好的最优模型绘制研究区内道路影响评估图。本文使用的SVM模型基于Python平台实现,引入核函数将影响因子映射到高维的特征空间,找到灾害点与非灾害点间的最大间距的最优超平面,从而实现样本分类。本文使用的核函数为径向基核函数(radial basis function,RBF),RBF位置变量少,运算简便,能够很好地将低维度数据映射到高维度空间[57]

  • 针对门源地震地表破裂带,首先对GF-1和GF-7高分光学遥感进行目视解译,可以快速获取此次地震地表破裂带的空间分布特征,比较直观地获取主要区域道路桥梁的受灾概况。然后利用InSAR技术确切获取破裂形变梯度较小区域的位置和形变信息。SAR/光学像素偏移量作为技术补充,可以识别形变梯度较大区域,通过无人机影像识别主破裂带附近存在的次生裂缝和伴生羽裂状的裂缝等。最后将地表破裂带识别的结果进行对比验证和互为补充,精密确定地表破裂位置和形变量。

  • 采用高分光学遥感目视解译的方法对GF-1和GF-7光学遥感影像进行快速解译,发现地震导致的地表破裂明显,如图 3(a)所示。破裂带自东向西延伸达12.76 km,可分为8段(见图 3(b)~3(i)),白色标志表示其实际位置,发现西段地表出现明显破裂,最大宽度约为2 m(37°48′14″N,101°15′04″E)。虽然东段雪山覆盖,但仍存在断断续续的地表破裂现象,图 3(j)3(k)为破裂带现场照片。

    Figure 3.  Surface Ruptures Revealed by the High-Resolution GF-7 Optical Image Acquired on 2022-01-08

    图 4展示了地表破裂对主干道路和兰新高铁的影响,红色方框内道路发生不同程度的损坏(见图 4(a)),在图 3(e)3(f)段破裂带间的县道受到严重影响(见图 4(b)),位于图 3(d)段破裂带附近的兰新高铁同样受到较为严重的破坏(见图 4(c))。

    Figure 4.  Impacts of the Surface Ruptures on Major Highways and Lanzhou-Xinjiang High Speed Railway

    图 5(a)5(b)分别为兰新高铁受灾后、受灾前的影像。此路段共发生4处桥梁错位(红色方框),并产生弯曲倒影(黄色实线)。通过现场野外调查,兰新高铁路段发生明显的桥梁断裂(见图 5(c)),隧道洞口的铁路也产生明显弯曲(见图 5(d)5(e))。

    Figure 5.  Damage to Lanzhou-Xinjiang High Speed Railway

  • 采用InSAR同震形变场和相干性信息、光学/SAR像素偏移量技术进一步精密确定地表破裂带。图 6为光学像素偏移量的地表形变场,其中蓝色代表靠近卫星方向移动,红色代表远离卫星方向移动,此次地震东西向最大形变约为2.0 m。

    Figure 6.  Optical Pixel Offsets

    SAR像素偏移量技术可以较好地得到该区域距离向和方位向形变场,图 7为SAR像素偏移量的地表形变场,此次地震距离向最大形变约为1.5 m。

    Figure 7.  SAR Pixel Offsets

    图 8展示了InSAR地表形变场及地震前后InSAR的相干性。由图 8可知,在震中及附近区域形变量过大,产生失相干现象,相邻两个像素形变值超过1/4卫星波长,导致LOS向形变场震中及附近区域为空值。

    Figure 8.  InSAR Surface Displacement Field in the Radar Line of Sight and InSAR Coherence

    图 6(a)中剖线AA′、BB′和CC′在不同方向的形变量分别如图 9(a)9(b)9(c)所示。由图 9可以发现,3种方法的结果在距离A点约7 200 m处,距离B点约6 600 m处和距离C点约7 000 m处均产生断裂,进一步验证本文确定的破裂带位置信息的准确性。

    Figure 9.  Deformation in the Different Directions of the Profiles AA′, BB′ and CC

    根据SAR像素偏移量的形变场和无人机影像确定地表破裂带的位置(见图 10(a)),从而将光学遥感解译中间段的地表破裂(图 3)带串联起来,得到最终连续的地表破裂带形变与位置信息(图 10(b)黑色实线),最终确定的地表破裂带长度约为36.22 km。图 10(c)~10(n)为此次地表破裂带的野外调查照片。图 10(c)图 10(d)分别位于拖莱山断裂的上大圈沟(37.81°N,101.17°E)和道沟附近(37.80°N,101.22°E),该区域存在大量地表裂隙,主破裂带成NE分布,走向约为270°,在主破裂带附近存在次生裂缝,裂缝宽度约为10 cm。图 10(e)10(f)分别为道沟东(37.80°N,101.23°E)、石门峡附近(37.80°N,101.24°E)裂隙,随着距离震中越近,破裂越来越明显,而且在主破裂带附近广泛分布着伴生羽裂状的裂缝,分支破裂走向多为NW和NE。在图 10(e)右侧存在明显的NE向左旋走滑断层(20 cm)。图 10(g)~10(i)为硫磺沟区域(37.79°N,101.25°E)的野外调查照片。该区域分布着桥梁、山坡和河流阶地等,地表破裂最为明显,最大宽度为1.5 m(图 10(h)),桥梁发生位错断裂,山坡错断抬升约1 m,冰面产生挤压破碎,破裂带沿着NW走向向深山蔓延。图 10(j)为兰新高铁附近区域(37.78°N,101.31°E),大梁隧道和硫磺沟大桥受灾害影响严重,大梁隧道里面的铁路需要重新修建,硫磺沟大桥断裂明显,桥面向东倾斜。该区域的破裂带走向为260°~320°。在隧道背面山坡上的地表破裂带距离大梁隧道约500 m,走向约为300°,裂缝宽约40 cm(见图 10(k));图 10(l)(37.77°N,101.33°E)、图 10(m)区域(37.76°N,101.35°E)和图 10(n)区域(37.75°N,101.36°E)地表破裂带特征相似,都位于冷龙岭断裂附近,走向约为290°~310°,裂缝宽为10~20 cm。虽然被积雪覆盖,但还是可以明显看到延伸的地表裂缝,地表断裂带呈NE向穿过硫磺沟河流向冷龙岭北侧断裂延伸(见图 10(n))。

    Figure 10.  Geomorphic Features of the Surface Rupture Zones

  • 门源县道路总里程约601.51 km,其中高速公路24.43 km、国道75.98 km、省道133.98 km、县道220.56 km、乡道146.56 km。本次地震涉及高受灾害影响区域路网里程约159.17 km,占总里程的26.5%;受灾害影响区域路网里程188.03 km,占总里程的31.3%;低受灾害影响区域路网里程172.83 km,占总里程的28.7%。本文结合门源地区的路网信息进行分析,如图 11(a)所示,历史灾害点发生的路段包括Y502、Y511、Y513、Y514、Y515、Y524、Y538、Y544和G338。同时对灾害等级分布路段进行分析,如图 11(b)所示,受灾害影响程度低区域路段集中分布在门源回族自治县西北部;中区域路段集中在门源回族自治县中部,以乡道分布居多;高区域路段分布主要集中在门源回族自治县以G0611、G338为主的东南地区道路段,主要包括Y511、Y512、Y514、Y524、Y528、Y545、Y548、Y550、X503、G0611和G338等。

    Figure 11.  Impacts of the Surface Ruptures on Major Traffic Routes

    各等级道路受灾害影响程度如图 11(c)所示,高、中和低影响区域路网比例分别为26.46%、31.26%、28.73%,未受影响区域路网比例为13.55%。对不同等级公路的受灾害影响里程进一步分析(见图 11(d)),发现高速公路受到的危害最大,不存在未受影响里程,高、中、低受影响里程分别占70.05%、16.92%和13.03%;省道和乡道的高受影响里程占比最大,省道的高、中、低受影响里程分别占43.29%、26.24%和22.94%,未受影响里程为7.53%;乡道的高、中、低受影响里程分别占28.65%、27.29%和19.67%,与其他等级的道路相比,乡道的未受影响里程占比最大为24.39%;国道的低受影响里程占比最大为63.7%,高受影响里程占比最小为2.6%,中受影响里程和未受影响里程分别为18.62%和15.08%;与其他等级的道路相比,县道中受影响里程占比最大为42.89%,高、低受影响里程和未受影响里程分别为18.13%、27.97%和11.01%。通过以上分析可以发现,此次地震对高速公路影响最大,乡道影响最小。主要原因是高速公路附近区域的历史地质灾害点分布较多,地震引发的次级灾害易发性较高。

  • 本文以2022-01-08青海省门源县Mw 6.7地震为研究对象,提出了一种利用多源遥感影像快速解译地震地表破裂带的技术框架。首先通过GF-1的GF-7光学遥感影像快速解译破裂带的空间分布特征,初步判定可见破裂带自东向西长达12.76 km,最宽区域位于西端约2 m(37°48′14″N,101°15′04″E),并导致兰新高铁浩门至山丹军马场区间隧道群受损,出现了暂时关闭停运的情况;进一步综合光学像素偏移量东西向和南北向形变场、SAR像素偏移量距离向和方位向形变场、InSAR的同震形变场、相干性和无人机影像快速确定地表破裂带形变特征和空间分布。由光学像素偏移量的结果可知,东西向的最大形变为2.0 m,由SAR像素偏移量的结果可知,距离向最大形变为1.5 m,最终得出地表破裂的形变和空间分布信息。解译结果表明此次地震导致的地表破裂由东(37°42′00″N,101°28′04″E)向西(37°48′53″N,101°06′02″E)约为36.22 km;结合门源地区公路交通网、历史地质灾害点的分布以及震后地表破裂带信息进行综合分析可知,交通干线G0611和G338东南段受地震影响较大。本文将获取地表破裂带的不同技术优缺点进行分析,针对门源地震系统提出一种基于多源卫星遥感影像快速解译地震震后地表破裂带的方法,并对门源县道路交通网各个路段的风险等级进行分析,为积极预防次生灾害带来的二次灾害起了一定的指导作用,促进了地震震后灾情快速应急响应与防灾减灾的顺利进行。

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