Effect of digital elevation models on monitoring slope displacements in open-pit mine by differential interferometry synthetic aperture radar

Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the potential to be a cost-effective method for monitoring surface displacements over extensive areas,such as open-pit mines.DInSAR requires the ground surface elevation data in the process of its analysis as a digital elevation model(DEM).However,since the topography of the ground surface in open-pit mines changes largely due to excavations,measurement errors can occur due to insufficient information on the elevation of mining areas.In this paper,effect of different elevation models on the accuracy of the displacement monitoring results by DInSAR is investigated at a limestone quarry.In addition,validity of the DInSAR results using an appropriate DEM is examined by comparing them with the results obtained by global positioning system(GPS)monitoring conducted for three years at the same limestone quarry.It is found that the uncertainty of DEMs induces large errors in the displacement monitoring results if the baseline length of the satellites between the master and the slave data is longer than a few hundred meters.Comparing the monitoring results of DInSAR and GPS,the root mean square error(RMSE)of the discrepancy between the two sets of results is less than 10 mm if an appropriate DEM,considering the excavation processes,is used.It is proven that DInSAR can be applied for monitoring the displacements of mine slopes with centimeter-level accuracy.

Ground Surface Ruptures and Near-Fault,Large-Scale Displacements Caused by the Wenchuan Ms8.0 Earthquake Derived from Pixel Offset Tracking on Synthetic Aperture Radar Images

The 12 May 2008 Wenchuan Ms8.0 earthquake produced surface displacements along the causative fault, the Yingxiu-Beichuan Fault, which are up to several meters near the fault. Because of the large gradient, satellite synthetic aperture radar （SAR） interferometric data are strongly incoherent; the usual SAR interferometry method does not allow such displacements to be measured. In the present study, we employed another approach, the technique based on pixel offset tracking, to solve this problem. The used image data of six tracks are from the Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar （ALOS/PALSAR） dataset of Japan. The results show that the entire surface rupture belt is 238 km long, extending almost linearly in a direction of 42°north-east. It is offset left laterally by a north-west-striking fault at Xiaoyudong, and turns at Gaochuan, where the rupture belt shifts toward the south by 5 km, largely keeping the original trend. In terms of the features of the rupture traces, the rupture belt can be divided into five sections and three types. Among them, the Beichuan-Chaping and Hongkou-Yingxiu sections are relatively complex, with large widths and variable traces along the trend. The Pingtong-Nanba and Qingping-Jingtang sections appear uniform, characterized by straight traces and small widths. West of Yingxiu, the rupture traces are not clear. North of the rupture belt, surface displacements are 2.95 m on average, mostly 2-3.5 m, with 7-9 m the maximum near Beichuan. South of the rupture belt, the average displacement is 1.75 m, dominated by 1-2 m, with 3-4 m at a few sites. In the north, the displacements in the radar line of sight are of subsidence, and in the south, they are uplifted, in accordance with a right-slip motion that moves the northern wall of the fault to the east, and the southern wall to the west, respectively. Along the Guanxian-Jiangyou Fault, there is a uplift zone in the radar line of sight, which is 66 km long, 1.5-6 km wide, and has vertical displacements of approximately 2 m, but no observable rupture traces.

Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry:Application to the 2016 Kumamoto Earthquake

Conventional synthetic aperture radar(SAR)interferometry(InSAR)has been successfully used to precisely measure surface deformation in the line-of-sight(LOS)direction,while multiple-aperture SAR interferometry(MAI)has provided precise surface deformation in the along-track(AT)direction.Integration of the InSAR and MAI methods enables precise measurement of the two-dimensional(2D)deformation from an interferometric pair;recently,the integration of ascending and descending pairs has allowed the observation of precise three-dimensional(3D)deformation.Precise 3D deformation measurement has been applied to better understand geological events such as earthquakes and volcanic eruptions.The surface deformation related to the 2016 Kumamoto earthquake was large and complex near the fault line;hence,precise 3D deformation retrieval had not yet been attempted.The objectives of this study were to①perform a feasibility test of precise 3D deformation retrieval in large and complex deformation areas through the integration of offset-based unwrapped and improved multiple-aperture SAR interferograms and②observe the 3D deformation field related to the 2016 Kumamoto earthquake,even near the fault lines.Two ascending pairs and one descending the Advanced Land Observing Satellite-2(ALOS-2)Phased Array-type L-band Synthetic Aperture Radar-2(PALSAR-2)pair were used for the 3D deformation retrieval.Eleven in situ Global Positioning System(GPS)measurements were used to validate the 3D deformation measurement accuracy.The achieved accuracy was approximately 2.96,3.75,and 2.86 cm in the east,north,and up directions,respectively.The results show the feasibility of precise 3D deformation measured through the integration of the improved methods,even in a case of large and complex deformation.

Ground settlement of Chek Lap Kok Airport,Hong Kong,detected by satellite synthetic aperture radar interferometry

Satellite synthetic aperture radar (SAR) inter-ferometry is used to investigate the slowly accumulating ground settlement at the new Chek Lap Kok Airport in Hong Kong. Most of the land occupied by the airport was reclaimed from the sea and therefore certain ground settlement in the area has been expected. A pair of ERS-2 SAR images spanning nearly a year is used in the study. The high spatial resolution (20 m×20 m) ground settlement map derived indicates that the settlement that occurred in the area over the time period is as large as 50 mm. The SAR measurement results agree with the levelling measurements at some benchmarks in the area to well within 1 cm(rms error),and the overall correlation between the two types of results is 0.89. The paper presents some brief background of inter-ferometric SAR, and outlines the data processing methods and results.

基于多期时序DInSAR的泥石流地表位移形变分析

随着气象条件、地质环境的恶化以及人类活动对自然环境的过分扰动,滑坡、崩塌、山洪和泥石流等地质灾害频发,严重威胁人民群众生命财产安全,严重影响基础设施,制约国民经济的发展。为分析泥石流发生机制并提出防治建议,以近年多次发生泥石流的云南省宜良县马家冲社区为研究区,从网站获取哨兵1号数据,在ENVI软件SARscape模块中采用合成孔径雷达差分干涉测量技术得到位移形变结果。结果表明,研究区位移形变呈现周期3年“抬升-抬升-下降”的规律,在周期交替时可能发生泥石流,并针对位移形变提出了防治建议。

利用InSAR形变反演滑坡滑面运动特征——以汶川县布瓦村滑坡为例

当前滑坡滑面运动特征获取多采用接触式手段,费时费力、成本高昂,且仅能获取稀疏点位滑面运动状态。因此,本文提出一种非接触式滑坡滑面运动特征获取方法,其基本思路是:基于位错理论建立滑坡表面形变与滑面运动间的关系模型,同时引入滑面运动平滑约束条件,最终以InSAR等技术获取的地表形变为约束,反演获得滑面运动特征。本文选择四川省汶川县布瓦村滑坡作为研究对象,以时序InSAR技术获取的滑坡坡面升降轨形变数据为约束,基于发展的理论方法反演获得布瓦村滑坡滑面几何与运动学参数,并结合野外勘查、Lidar观测以及模型残差较好地验证了反演结果的可靠性,其中反演升降轨模型残差仅3.1 mm·a^(-1)和3.4 mm·a^(-1)。反演结果显示,布瓦村滑坡滑面位于坡表以下平均~15.0 m深度处,滑面整体以沿坡向运动为主,最大运动量级达~80 mm·a^(-1),同时在坡体西侧中下部发现微量的垂直坡向运动。本文提出方法可方便、快速的获取空间连续的滑坡滑面运动特征,为滑坡灾害预警和失稳风险评估提供更为直接可靠的科学参考。

SBAS-InSAR技术融合CNN-LSTM模型的矿区开采沉陷监测与预测

针对传统矿区开采沉陷监测方法耗费人力财力和预测预警模型较少的问题,研究提出一种基于短基线集合成孔径雷达干涉测量(Small Baseline Subset-Interferometry Synthetic Aperture Radar,SBAS-InSAR)技术和卷积神经网络(Convolutional Neural Networks,CNN)与长短期记忆网络(Long Short-Term Memory,LSTM)相结合的矿区开采沉陷监测预测方法。首先,利用SBAS-InSAR技术对建新煤矿进行矿区开采沉陷监测,获取了该矿区的年平均沉降速率和累计沉降值。用GNSS监测数据与SBAS-InSAR结果进行对比验证,其拟合效果较好。其次,在此基础上利用CNN-LSTM模型预测后6期沉降数据,其结果与CNN和LSTM预测结果进行对比。研究显示,CNN-LSTM模型的平均绝对误差(S_(MAE))和均方根误差(S_(RMSE))比单一的CNN和LSTM分别至少降低了44.8%和40.6%,其决定系数均高于98%。最后,进一步预测前6期和中6期沉降数据,验证了CNN-LSTM预测模型在时间上的一致性。因此,SBAS-InSAR融合CNN-LSTM模型在类似矿山开采沉陷监测和预测中有较好的应用前景。

基于最优PS点获取方法的矿山工业广场沉降监测

为保障矿山地面和井下工作人员、工业广场周边居民以及运煤系统运行安全,实时监测地表变形情况,提出永久散射体(PS)点的优化获取方法,以提升PS-合成孔径雷达干涉测量(InSAR)技术在沉降监测中的适用性。首先,选择淮北市郊煤矿工业广场煤柱Ⅱ513工作面开采沉陷期间2020年10月31日—2022年8月22日共26景SAR卫星影像,运用PS-InSAR技术确定最优的PS点目标选取方法;然后,基于该方法获取该工作面回采过程中的开采损害保护区地面沉降速率、累计沉降量;最后,基于地面水准点实测数据验证PS-InSAR监测精度并分析研究区地表及建(构)筑物动态沉降情况。结果表明:相干系数和振幅离差指数双阈值法较适应于研究区沉降监测;研究区内可探测到的最大下沉速率为-26.5 mm/a,最大累计下沉值为-53.7 mm;同时,探测到研究区西北部存在岩溶塌陷地质灾害因素;利用水准数据验证精度,均方根误差(RMSE)仅为3.8 mm,决定系数达到0.91。

2023年积石山Ms6.2级地震InSAR同震形变探测与断层滑动分布反演

2023年12月18日,甘肃省临夏回族自治州积石山县发生了Ms6.2级地震,地震灾害损失显著高于同震级地震,查明其同震形变场与断层滑动分布特征,有助于揭示此次地震灾害损失严重的原因。利用Sentinel-1卫星升、降轨雷达影像,采用合成孔径雷达干涉技术获取了本次地震的同震形变场,进而基于Okada弹性位错模型,确定了本次地震的震源参数,并基于分布式滑动模型反演了本次地震断层面上的滑动分布。结果表明,积石山地震为逆冲型地震,升、降轨同震形变场沿视线向最大形变量分别为7.1 cm和7.8 cm,断层最大滑动量为0.31 m,主要集中在地下0~8 km。与2019年6月17日四川长宁Ms 6.0级地震对比发现,长宁地震地表最大形变量为8 cm,断层最大滑动量为0.38 m,均大于积石山地震,推测积石山地震灾害损失严重的主要原因为震区居民房屋相对集中、房屋抗震性能差和黄土区场地地震波放大效应等。

基于时序InSAR技术的中贵天然气管道天水市段沿线滑坡隐患识别与形变分析

中贵天然气管道天水市段沿线地处黄土高原,地貌类型多样、构造活跃、复杂地质条件下发育多种具有隐蔽性和潜伏性的地质灾害,其中滑坡灾害对中贵天然气管道的安全危害极大,因此,对中贵天然气管道天水段沿线滑坡隐患进行有效识别与分析具有重要意义。采用Sentinel-1A升降轨卫星数据,基于时序合成孔径雷达干涉测量(synthetic aperture radar interferometry,InSAR)对中贵天然气天水段沿线滑坡隐患进行了解译识别、现场复核、发育特征与典型滑坡形变分析。研究区共识别17处滑坡隐患点,现场复核最终确定13处,其余4处为人类工程活动区,其中常沟村和磨峪沟村滑坡形变受降雨影响。统计发现研究区管道沿线滑坡多发生在坡度40°~50°,高差400~600 m,东北坡向和距管道100~150 m范围内,且岩土体强度较低,地层以第四系全新统冲洪积层(Q_(4)^(a1+p1))为主。结果表明:联合升降轨的时序InSAR技术可以有效识别管道沿线滑坡隐患,为油气管线的安全运营及今后油气管道选线提供重要的科学依据和参考。

DInSAR技术对不同方位形变的敏感性研究

以DEM数据为假想的地面目标,只考虑距离对相位的影响,模拟竖直向、距离向和方位向形变的干涉纹图,从而研究DInSAR技术对空间不同方位形变的敏感性。在所有模拟参数和形变位移大小都相同的情况下,三个不同方位形变所产生的相位从0到2π变化的完整干涉环的数目是各不相同的,揭示DInSAR技术对不同方位地表形变的敏感性存在差异,且敏感性由大到小依次为:竖直向、距离向和方位向。

2024年新疆乌什Ms 7.1地震InSAR同震形变探测与断层滑动分布反演

2024年1月23日,新疆维吾尔自治区乌什县发生Ms 7.1地震,快速查明其同震形变场与发震断层滑动特征,有助于科学认识此次地震发震机理。利用Sentinel-1卫星升、降轨雷达影像,采用合成孔径雷达差分干涉测量技术获取了本次地震的同震形变场,进而基于Okada弹性位错模型,确定了本次地震的震源参数,并基于分布式滑动模型反演了本次地震断层面上的滑动分布。结果表明,升、降轨同震形变场沿视线向最大形变量分别为75 cm和48 cm,断层最大滑动量为4.2 m,主要集中在地下3~20 km区域;模型正演结果表明,此次地震在垂直方向最大抬升约53 cm,最大沉降约12 cm;发震断层运动以逆冲为主,兼具少量左旋走滑,根据InSAR结果及地质资料综合判断,本次发震断层为迈丹断裂。

短时空基线PS-DInSAR提取地表形变时间序列

为减小合成孔径雷达差分干涉技术中时空失相关对区域地表形变探测的影响，采用同时限制时间和空间基线的方式自由组合干涉对，基于永久散射体构建差分网络模型以提取区域形变，并利用奇异值分解方法恢复其时间序列．为验证算法的有效性，以美国Phoenix市为试验区域，采用39幅ERS-1／2卫星C波段SAR图像（时间跨度为1992～2000年）进行地表沉降探测．结果表明，该算法能有效提取区域地表沉降的时间序列．

DInSAR技术在成昆铁路复线路基及边坡垂直变形监测中的应用

针对常规铁路路基及边坡垂直变形监测技术效率较低、局限性较大的问题,本文将合成孔径雷达差分干涉测量(Differential Interferometric Synthetic Aperture Radar,DInSAR)技术引入铁路路基及边坡的垂直变形监测中。以成昆(成都-昆明)铁路复线越西段一处(R1区域)路基及边坡为依托,利用4期ALOS-2卫星PALSAR-2高分辨率合成孔径雷达影像,研究DInSAR技术在路基及边坡监测中应用的可行性。结果表明:在监测的126 d内,R1区域地表垂直变形速率为0.12^-0.13 mm/d;以水准测量监测结果为真值,经误差分析得出DInSAR监测中误差为7.57 mm;在污水处理厂的应用实践证明,雷达影像与高分辨率遥感影像、无人机影像等光学影像的互补使用是解决铁路工程领域监测问题的有效途径;DInSAR监测得到的点(像元)密度远大于水准点的密度,能更好地反映区域性地表变形的细节特征。

PS-DInSAR时序差分干涉图公共主影像选取方法

针对基于相干系数的PS-DInSAR时序差分干涉图主影像优化选取方法存在的干涉对垂直空间基线、时间基线和多普勒质心频率差3个因素中有任意一个因素大于临界值时,其他2个因素的影响将被忽略的问题,提出了基于正则化距离的主影像优化选取方法.实验结果表明,当干涉图序列的时空基线或多普勒质心频率差总体较大时,所提方法较基于相干系数的方法更加有效和可靠.

基于蚁群优化的GPS/DInSAR三维形变监测方法

提出一种基于蚁群优化的GPS/DInSAR形变测量方法,将高精度地表散点GPS三维观测量与DInSAR视线向观测量进行深度融合,提取地表真三维形变量.以GPS约束与DInSAR约束基于Gibbs方程构建能量函数,利用蚁群优化算法在连续空间求解能量函数最小值.实验研究结果表明,该方法在连续地表三维形变测量中具有较高精度,特别是垂直方向上具明显优势.

DInSAR技术的最新进展

从传统合成孔径雷达差分干涉测量(DInSAR)技术面临的问题出发,分析了近年来DInSAR技术发展的新趋势,详细论述了最小二乘(Least Squares,LS)方法、永久散射体(Permanent Scatterer,PS)方法和小基线集(Small Baseline Subset,SBAS)方法的原理、应用及发展趋势。

基于GAMMA软件二次开发的地表形变监测系统

InSAR技术具有大范围、高精度、全天时、全天候的优点,已被广泛地应用于地表形变监测与高程获取。GAMMA软件具有支持数据源丰富、处理功能全面、结果精度可靠和效率高等优势,是当前国际上常用的InSAR数据处理软件。但该软件采用命令行交互,操作命令繁杂、对专业要求程度高,不易于非专业用户使用。针对以上问题,本文利用界面友好、开源的QGIS对GAMMA进行二次开发。结合GAMMA提供的py_gamma库和QGIS提供的py_QGIS API接口,采用模块化设计、算法并行优化等技术,开发了一套界面化InSAR形变监测系统,并以西藏沃达地区106景哨兵1号数据进行处理试验,从软件性能和处理结果方面证明了基于QGIS和GAMMA二次开发的可行性和可靠性。相关内容可为命令行用户界面软件转图形化用户界面提供参考。

面向时序InSAR的卫星任务规划框架设计

随着国内自主雷达卫星和差分干涉合成孔径雷达技术(D-InSAR)及衍生的时序InSAR技术的发展,以时间序列上重复观测为主的长周期形变监测的应用与日俱增。对遥感卫星地面任务规划系统来说,规划和管理大量有时序关联的重复任务,从而最大限度地满足观测要求,是提升以形变监测为使命的雷达卫星运控效率的关键。在综合考虑雷达差分干涉任务序列性、一致性、周期性特征的基础上,设计了一种面向雷达卫星时序InSAR类型任务的规划框架,旨在规划序列任务时,考虑序列整体特性,进行序列任务组间的冲突检测。基于此方法,对现有的地面任务规划系统进行了优化升级,验证了框架的有效性。结果证明:该方法可快速、自动进行序列任务冲突消解,可为长周期序列任务的规划和管理提供设计参考。

融合可微分渲染的SAR多视角样本增广

合成孔径雷达(SAR)因其全天候、全天时的监测能力在民用和军事领域得到广泛应用。近年来,深度学习已被广泛应用于SAR图像自动解译。然而,由于卫星轨道和观测角度的限制,SAR目标样本面临视角覆盖率不全的问题,这为学习型SAR目标检测识别算法带来了挑战。该文提出一种融合可微分渲染的SAR多视角样本生成方法,结合逆向三维重建和正向渲染技术,通过卷积神经网络(CNN)从少量SAR视角图像中反演目标三维表征,然后利用可微分SAR渲染器(DSR)渲染出更多视角样本,实现样本在角度维的插值。另外,方法的训练过程使用DSR构建目标函数,无需三维真值监督。根据仿真数据的实验结果,该方法能够有效地增加多视角SAR目标图像,并提高小样本条件下典型SAR目标识别率。