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采用当前方法测量地震形变场时,不能有效去除地震图像中存在的噪声,得到的测量结果与实际结果之间的误差较大,存在抗干扰性差和测量精准度低的问题。提出基于无人机摄影的地震形变场测量技术,对无人机摄影得到的主图像和辅图像做配准处理,采用SAR处理器处理图像数据,得到干涉图。利用SUSAN算法检测干涉图的边缘,通过K均值方法划分模板区域中存在的特征值类别,根据划分结果得到干涉图的相关参数和噪声区域,结合非线性扩散方法和SUSAN算法完成干涉图的去噪处理,依据基线估计去除干涉图中存在的平地效应,利用网络流算法完成相位解缠,获得地形相位值,绘制地震形变图,完成地震形变场的测量。实验结果表明,所提方法的抗干扰性强、测量精准度高。 相似文献
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我国西南山区地质条件复杂,且地震频发。地震除引发滑坡、泥石流外,也会导致大量崩塌落石灾害,对基础设施造成严重破坏。以位于程海断裂带沿线、地震发生风险较高且危岩带分布典型的八代村为研究对象,分析地震作用下危岩体的影响范围,并评估山脚高速公路的受灾风险。首先基于无人机影像,利用图像识别技术快速获取危岩体位置及尺寸参数,与人工识别结果对比发现识别准确率达76.2%;然后将危岩体参数代入二维数值模拟软件Rocfall,并结合地震能量计算公式,计算地震作用下危岩体运动距离。结果表明,公路与危岩体影响区最近距离仅57 m,需要在坡脚段设置挡墙等防护措施。 相似文献
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2021年5月22日青海玛多Ms7.4级地震中,野马滩1号、2号大桥结构严重震损,导致共玉高速交通中断。这是较为典型的近断层简支梁桥震害,为全面记录和保存震害信息,采用无人机倾斜摄影测量方法构建了两座大桥的精细震害三维模型。与上行线施工图对比显示,模型一致性较高。结合三维模型的关键尺寸与桥梁构件几何关系,量化分析了两座桥各主梁纵向与横向震致残余位移。结果表明,所有主梁的纵向残余位移以北向为主,反映了大桥遭遇了显著的近场脉冲地震动效应。1号桥的纵向位移平均值大于2号桥;1号桥70%主梁脱落,其纵向位移由北至南围绕位移均值波动变化;2号桥上行线南部6跨主梁落梁,其纵向位移由北至南逐渐增大。两座大桥的横向位移沿纵向呈现波动变化,有一定随机性,且绝大多数盖梁两侧混凝土挡块均严重损伤,反映了在与地震断裂带平行的东西方向上,大桥受到的往复地震动作用较为显著。基于模型量测桥墩间距差对桥墩倾斜移位进行了初步估计,结果表明少数桥墩可能出现倾斜或移位。大桥震害三维模型和位移数据可为大桥震害分析、仿真模拟和修复策略提供技术支撑。 相似文献
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地震作用过程中地震加速度通常呈先增大后减小的特征。利用拟静力法对危岩的稳定性进行分析时,考虑地震过程中地震加速度的变化,对山西太原天龙山危岩体加固工程中的同一危岩体分别以滑塌式和倾倒式破坏模式进行计算,发现地震作用过程中危岩体可能在两种破坏失稳模式之间相互转化。将此问题扩展至一般情况进行计算并讨论,得出如下结论:地震力对危岩体破坏作用的贡献大小不同,通常情况下,地震作用力对危岩的倾覆力矩贡献相对较大;进行稳定性评价时应考虑地震作用过程,以安全系数最先达到1.0的破坏模式作为危岩体的可能破坏模式进行计算;对危岩体进行抗震加固设计时应对加固设计进行多种工况下的校核,保证其在地震作用过程中不同危险状态的稳定性。 相似文献
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无人机倾斜摄影技术建模生成的三维影像较好地展现了建筑物侧面和顶面的震害细节信息,然而影像的高维度特性难以直接基于三维影像提取震害信息,经过降低维度转换的二维纹理影像往往会导致建筑物震害信息的不完整性和破碎性。针对这些问题,本文以2017年九寨沟MS7.0地震为例,提出了一种直接从九寨沟震后三维影像获取侧面纹理信息的方法,即将三维模型打散,实现纹理与不规则三角网分离,从而获取完整的纹理影像,然后利用金字塔模型的瓦片坐标范围、瓦片命名规则和建筑物单体的空间位置选取最优纹理影像,再使用加权均值方差法确定纹理影像中建筑物的外墙最佳分割尺度后,采用面向对象方法提取建筑物外墙和墙皮脱落信息,最后通过对这些建筑物震害特征的分析,判定单体建筑物的破坏等级。结果显示,该方法成功获取了建筑物完整的侧面震害纹理影像,并基于纹理影像提取了外墙、裂缝和墙皮脱落区域信息判定建筑物单体为中等、严重两个破坏等级。 相似文献
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建筑物受损信息是地震受灾程度评估的基础,针对传统建筑物表面信息识别人工成本高、效率低等问题,受深度学习提取建筑物影像的启发,提出利用无人机倾斜摄影模型与深度学习相结合的方法提取震后建筑物表面破损信息。以2019年长宁6.0级地震为例,选用双河镇震后倾斜摄影模型切片图为数据源,对比分析面向对象分类方法、VGG-16模型和DeeplabV3+模型对建筑物表面损毁信息的提取结果。分析结果表明,针对建筑物表面破损信息的提取,尤其是细小裂缝的提取,语义分割网络DeeplabV3+模型具有较强的优势(准确率96.93%、召回率96.85%、总体精度96.89%),可实现建筑物表面破损信息的有效提取,具有较强的应用价值。 相似文献
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2018年10月11日和11月3日,位于金沙江上游右岸的西藏江达县波罗乡白格村先后发生了2次特大规模的滑坡堵江事件,经人工干预后险情得以解除。运用差分干涉合成孔径雷达(D-InSAR)技术对白格滑坡后缘的潜在危岩体进行了7.5d连续不间断的监测,结果表明：滑坡体上侧区域目前仍然不稳定,具有再次发生滑坡堵江的危险。基于此,文中利用颗粒流软件PFC2D模拟了滑坡后缘潜在危岩体在自身重力、强降雨、地震条件下的稳定性状况。模拟结果表明：后缘潜在危岩体在静力作用下不会产生明显的失稳滑动,在强降雨和强地震动条件下会发生失稳破坏,可能会再次堵塞金沙江并形成堰塞湖。根据文中的模拟结果可对滑坡稳定性做出科学评价,同时为以后类似滑坡的防灾减灾提供参考。 相似文献
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An unmanned aerial vehicle (UAV) was flown over a boulder beach (area 20 000 m2) on the southern coast of Galicia (northwestern Spain) in May 2016, continuing earlier work based on flights over the same beach in July 2012, May 2013, and late March 2014. Digital surface models (DSMs) with 1.8 cm resolution were constructed from the 2014 and 2016 data to identify changes in beach morphology over the intervening period. Analyses were conducted using a Limit of Detection (LoD) of 0 cm and 3.71 cm. In both cases, the analyses showed that erosion dominated over 19% of the beach area. Accretion occurred over the rest of the beach, which acquired an additional 1500 m3 of material over the study period. Re-analysis of the data from earlier flights suggested that erosion dominated on the beach in 2012–2013 and deposition in 2013–2014. Without any clear relationship between beach behaviour and storm severity during each winter period, it is proposed that gravitationally induced erosion and storm-wave induced deposition are the result of perturbations about an equilibrium beach gradient. The UAV data also suggested that an essentially random component modulates regional patterns of movement. © 2018 John Wiley & Sons, Ltd. 相似文献
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房屋建筑结构数据是了解房屋抗震设防能力的基础,获取房屋建筑结构信息具有重要的现实意义。本文在简单介绍无人机遥感系统、房屋建筑信息无人机遥感调查技术流程的基础上,以全国多地多架次飞行任务为应用实例,对无人机照片进行筛选、姿态匹配、照片拼接、纹理映射等处理,获取了房屋建筑密集区的正射镶嵌图和三维场景模型,然后对房屋建筑结构类型进行目视判读,并与地面调查的真实结果比对分析,计算得到目视判读的准确率为91.17%,Kappa系数为0.80。结果表明,轻小型无人机轻便灵活,获取的三维场景模型能有效、直观、准确地进行房屋建筑结构类型判定,可弥补传统实地调查的不足,为评估大范围建筑物的抗震能力提供重要的参考依据。 相似文献
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The key parameters of houses such as distribution, area and height play an important role for urban-rural planning, earthquake emergency and disaster mitigation. The computer automatic extraction method is an effective way to acquire large area house information using satellite-borne or airborne optical remote-sensing images. However, because of the similarity of spectral characters for different land cover types or the influence of snow coverage, the classification accuracy of house type using traditional spectral based method can be decreased. To acquire the accurate houses distribution, a method based on the height information is proposed using unmanned aerial vehicle(UAV)in this study. With UAV flying at the height of 100m above ground, the route of the UVA was planned with the heading direction overlap of 77% and side direction overlap of 50%for the nearby pictures. Taking Qionghalajun Village in Xinjiang Uygur Autonomous Region for example, 69 pictures of the study area were obtained with DJI Phantom 3 professional. With those pictures input into the EasyUAV software, the Digital Elevation Model(DEM), Digital Surface Model(DSM), and Digital Orthophoto Map(DOM)were acquired based on photogrammetry method using the overlapped optical remote-sensing images of UAV. After that, the house distribution and height were acquired with the differences between DSM and DEM images larger than 2.6m. To eliminate the influences of disintegrated pixels on the house extraction, mainly caused by the trees or noise point, the classification aggregation tool of ENVI software was used with the disintegrated pixels' area less than 4m2. Compared with visual interpretation result, the user accuracy and mapping accuracy of the house extraction method proposed in this study is 88.69% and 97.42%, respectively. In addition, to evaluate the performance of the proposed method, the result of traditional supervised classification method using DOM data acquired previously was compared with the result of new method. The results show that the new method is more accurate the user accuracy and mapping accuracy of the supervised classification method, which is 43.23% and 85.30%, respectively. Besides the study area in this study, the performance of the proposed method will be evaluated at the other places in the further study. 相似文献
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2021年5月22日青海玛多发生MS7.4地震,震源断层错动在地表形成了长达160 km的同震地表破裂。可靠的地震地表破裂带参数是研究震源断层活动机制和评价地震危险性的重要基础。采用无人机倾斜摄影测量技术可以获得高精度的点云数据并产出DOM和DEM数据。通过跨破裂带的地形测量,获取了玛多MS7.4地震同震地表变形的垂直位移、水平缩短量和水平拉张量等参数。测量结果显示,玛多MS7.4地震发震断层在不同破裂段具有不同性质和大小的倾滑分量,其中具有压扭性质的野马滩观测点断层垂直位移为0.69～1.01 m,倾向水平缩短量为0.17～0.41 m,倾滑位移为0.71～1.09 m;具有张扭性质的朗玛加合日段断层垂直位移为0.34～0.54 m,倾向水平拉张量为1.99～2.08 m。 相似文献
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无人机地震灾情监测系统主要由无人机飞行平台、飞行控制系统、遥感系统、软件系统和无线电遥控系统等几部分组成,具有运行成本低、执行任务灵活性高等特点,是快速获取震后灾情信息的重要手段。结合四川高原山区的自然地理和气象条件,对无人机地震灾情监测系统在四川高原山区的应用进行了研究。该系统在高原山区的应用将在提升地震灾情现场勘查能力,迅速获取灾情信息,增加救灾工作的时效性,及为后期科学考察提供辅助信息等方面提供重要的技术支撑。 相似文献
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Manuel Stark Tobias Heckmann Livia Piermattei Fabian Dremel Andreas Kaiser Patrick Machowski Florian Haas Michael Becht 《地球表面变化过程与地形》2021,46(10):2019-2043
Uncrewed aerial systems (UAS), combined with structure-from-motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes. However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (−0.4 to −1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends (i.e. differences increase with distance across the survey—doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally. 相似文献
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Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits. 相似文献
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危岩体发育会对石窟寺的稳定和游客安全造成威胁,利用变形监测分析石窟濒危岩体变形特征对岩体稳定性评估起重要作用。文章以甘肃庆阳北石窟寺为例,采用全球导航卫星系统(Global Navigation Satellite System,GNSS)变形监测、测缝计和非接触式裂隙监测等技术,从北石窟寺分布区域地质体、石窟赋存崖体和洞窟关键块体三个尺度研究濒危岩体的变形特征。监测周期内,区域地质体上部位移呈突发性变化,连续降雨后产生5.2 mm的沉降,中部基岩出露部位位移遵循先缓慢增长后逐渐恢复的变化规律,变形量维持在±1 mm以内。崖体内构造裂隙底部变形量呈波动式上升,于次年1—2月达到全年最高值,且随着温度降低,裂隙中部变形速率大于底部。32窟内浅表性裂隙变形量在0 mm附近±2 mm范围内持续波动,无进一步扩张或闭合趋势。区域地质体变形与降雨有高度相关性,崖体变形与温度呈强烈负相关,洞窟关键块体变形也易受温湿度和人为扰动影响。目前三个尺度的岩体变形量均在小范围内变化,相互之间影响较小,无协同性。对濒危岩体变形特征的分析可为北石窟寺稳定性评估和预测分析工作提供数据参考。 相似文献