Stability analysis of complex terrain slope based on multi-source point cloud fusion

Numerical modelling is a common routine for slope stability analysis in the complex terrain, and the accuracy of topographic survey has a great impact on the results. In this study, a combination of unmanned aerial vehicle(UAV) photogrammetry and 3D laser scanning technique was first proposed to establish a high-precision digital elevation model(DEM), which could be accurate to 0.2 m, fulfilling the engineering requirements. Then, a series of 3D/2D finite element models(FEM) were constituted on ...     

LAI scale effect research based on compact airborne spectrographic imager data in the Heihe Oasis

As one of the key parameters for characterizing crop canopy structure, Leaf Area Index(LAI) has great significance in monitoring the crop growth and estimating the yield. However, due to the nonlinearity and spatial heterogeneity of LAI inversion model, there exists scale error in LAI inversion result, which limits the application of LAI product from different remote sensing data. Therefore, it is necessary to conduct studies on scale effect. This study was based on the Heihe Oasis, Zhangye city, Gansu province, China and the following works were carried out: Airborne hyperspectral CASI(Compact Airborne Spectrographic Imager) image and LAI statistic models were adopted in muti-scale LAI inversion. The overall difference of muti-scale LAI inversion was analyzed in an all-round way. This was based on two aspects, "first inversion and then integration" and "first integration and then inversion", and on scale difference characteristics of three scale transformation methods. The generation mechanism of scale effect was refined, and the optimal LAI inversion model was expanded by Taylor expansion. By doing so, it quantitatively analyzed the contribution of various inversion processes to scale effect. It was found that the cubic polynomial regression model based on NDVI(940.7 nm, 712 nm) was the optimal model, where its coefficient of determination R2 and the correlation coefficient of test samples R reached 0.72 and 0.936, respectively. Combined with Taylor expansion, it analyzed the scale error generated by LAI inversion model. After the scale effect correction of one-dimensional and twodimensional variables, the correlation coefficient of CCD-LAI(China Environment Satellite HJ/CCD images) and CASI-LAI products(Compact Airborne Spectro graphic Imager products) increased from 0.793 to 0.875 and 0.901, respectively. The mean value, standard deviation, and relative true value of the two went consistent. Compared with onedimensional variable correction method, the twodimensional method had a better correction result. This research used the effective information in hyperspectral data as sub-pixels and adopted Taylor expansion to correct the scale error in large-scale and low-resolution LAI product, achieving large-scale and high-precision LAI monitoring.     

基于GEE和U-net模型的同震滑坡识别方法

地震滑坡解译是震后重建的重要基础工作,主要通过室内人工遥感解译和室外野外调查确定。地震滑坡相比其他地物来说更为复杂,很难通过简单指数识别。室内遥感解译通过滑坡后壁、侧壁和堆积等纹理特征进行识别,大面积同震滑坡解译工作往往耗费大量人力和物力,且耗时长,难以满足灾害应急需求。本研究利用U-net神经网络模型,结合Google Earth Engine（GEE）云平台和人工智能学习平台Tensorflow,以地震局解译的汶川滑坡作为样本数据,以震后30 m分辨率的Landsat影像、高程、坡度以及NDVI数据作为模型输入参数,自动识别并获取了汶川地震后的同震滑坡数据,同时比较了不同参数组合情况下U-net神经网络模型的分割识别精度。研究表明：① U-net模型可以用于以Landsat影像为基础数据的同震滑坡快速自动识别;② 随着高程、坡度以及NDVI等输入参数增加,模型分割精度在逐渐提高,但假阳性结果也会出现增多,震后滑坡影像+高程+坡度+NDVI的输入参数组合精度最高;③ 在细节上,模型在多参数组合的情况下,大型滑坡能够很好被识别,一些较小型的滑坡受制于影像分辨率的影响,分割精度较差。为了更好识别小型滑坡,后续研究可能需提高影像的分辨率。此外,GEE云平台大大提高了训练样本获取的效率,为科研人员快速进行基于神经网络与遥感数据的地物识别研究提供了条件。     

Landslide displacement prediction based on the ICEEMDAN,ApEn and the CNN-LSTM models

Landslide deformation is affected by its geological conditions and many environmental factors.So it has the characteristics of dynamic, nonlinear and unstable, which makes the prediction of landslide displacement difficult. In view of the above problems,this paper proposes a dynamic prediction model of landslide displacement based on the improvement of complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN), approximate entropy(ApEn) and convolution long short-term memory(CN...     

基于滑体渗透性与库水变动的滑坡变形滞后规律研究

三峡水库运行过程中库岸滑坡的变形演化往往滞后于库水位的变化, 表现出时间滞后效应, 而且随渗透系数和库水位波动速率的不同, 滞后效应亦不同。以三峡库区白家包滑坡为例, 通过现场调查、监测数据分析以及数值模拟的方法, 研究了滑坡在不同渗透系数k和不同库水位下降速率v条件下的变形滞后时间变化规律。研究表明: 滑体渗透系数一定时, 库水位下降速率越大, 地下水滞后越明显; 库水位下降速率一定时, 滑体渗透系数越大, 地下水下降越快。当滑体渗透系数一定时, 库水位下降速率越大, 滑坡的变形滞后时间越短; 滑体渗透系数k=0.85 m/d时不同库水位下降速率作用下滑坡的变形滞后时间为3.74~9 d, 当0.47＜v/k＜1.18时, 0.24＜相对变形滞后时间＜1;当1.18＜v/k＜2.38时, 0＜相对变形滞后时间＜0.24。当库水位下降速率一定时, 滑体渗透系数越大, 滑坡变形滞后时间越短, 不同库水位下降速率下滑坡变形滞后时间随渗透系数的变化规律大致相同; 库水位下降速率v=1.8 m/d时不同滑体渗透系数下滑坡的变形滞后时间为1.7~8 d, 当0.52＜v/k＜0.84时, 0＜相对变形滞后时间＜0.16;当0.84＜v/k＜2.12时, 0.16＜相对变形滞后时间＜0.43;当2.12＜v/k＜9时, 0.43＜相对变形滞后时间＜1。研究成果对水库滑坡预测预警具有较强的应用价值。      

基于特征筛选与差分进化算法优化的滑坡危险性评估方法

突发性地质灾害危险性评估对灾害防治与风险管理具有重要意义。由于不同地区影响灾害发生的因子各不相同,实际评估过程中难以全面客观地选取适宜的评估因子。机器学习对处理灾害系统的高维非线性问题独具优势,但因模型难以调优而评估效果有限。本文尝试提出一种双向优化的滑坡危险性评估方法：在构建因子敏感性指数开展定量敏感性分析的基础上,结合重要性分析、相关性分析、共线性分析构建四维(Four-Dimensional, 4D)特征筛选法用于评估因子综合优选;为克服模型难以调优的问题,引入差分进化(Differential Evolution, DE)算法优化支持向量机(Support Vector Machine, SVM)与多层感知机(Multi-Layer Perceptron, MLP) 2种推广能力较强的机器学习模型。最后,以福建省滑坡为例,开展评估方法研究。研究表明：4D特征筛选法能更加客观全面地选取适宜性更高的危险性评估因子,从而降低数据维度、减少信息冗余以提升评估模型性能;DE算法对SVM与MLP具有显著的优化效果,有益于增强模型滑坡危险性的评估准确度,DE-SVM、DE-MLP相较于未优化...     

基于无人机成像点云的禄丰恐龙谷南缘环状地貌空间特征探测实验分析

为了定量获取复杂地貌构造的特征参数,需要获取高精度、高分辨率的地形地貌数据。在复杂地貌特征探测中,基于无人机航测结合成像点云,可以快速、高效、安全、准确地完成复杂地貌空间探测任务。针对禄丰恐龙谷南缘环状地貌的典型局地场景,采用无人机测量技术获取测区高分辨率的地貌影像数据,构建实景三维场景模型并对其地貌特征进行探测分析研究。试验结果表明:①基于无人机成像点云构建0.2 m分辨率DEM数据能够精准表达研究区真实地貌特征。②通过构建环状场景"内-中-外"7条典型高程剖面线,对比分析证实了研究区地形呈现环形"盆缘"形态特征,内外两侧高程逐渐递减,并且"盆缘"外部剖面高程起伏变化剧烈,地形相较于内部地貌更加复杂。③为进一步探测提取微形地貌特征信息,利用无人机成像原理构建的精准DEM数据结合测区实景三维模型,定量提取了该区坡度、坡向、相对高差、等值线、山脊线、山谷线等相关参数进行精准定量测量及分析探讨。④利用立体三维模型的多视角目视解译与典型场景分析,可清楚辨别出测区冲沟发育以及地质体的节理层理面等微地貌特征。通过以上对地貌三维场景探测试验研究表明,利用实景三维模型能够快速准确呈现测区地貌形态特征,并且成像点云数据综合分析,能够量化、半量化揭示区域构造地质信息。总体而言,无人机测量技术与成像点云3D产品在地质调查中的应用具有实用意义,并具备独特的技术优势。      

基于自标准数据和数据港口架构的数据共享技术

为满足复杂数据共享需求、减轻数据提供者负担,分析自标准与数据港口架构理论机器实现技术.将数据模式的概念引入自标准数据模型,根据NIEM原理,构建由公共数据模型、业务领域数据模型及编码表构成的自标准数据模型库;基于MFI-13表单注册标准,根据自标准数据模型对数据集进行注册,为实现软件之间的互操作对应生成元数据,并描述元数据遵循的语法及语义;为实现自标准数据的共享,提出基于数据港口架构的数据共享模型,并构建原型系统,证明其有效性和技术可行性.     

基于尖点突变理论及非趋势波动分析的滑坡稳定性及变形特征研究

以尖点突变理论及非趋势波动分析为基础,构建滑坡稳定性及变形趋势判断模型。首先利用尖点突变理论对滑坡整体稳定性进行评价,并进一步探讨滑坡的分阶段稳定性特征;再利用非趋势波动分析判断滑坡累计序列和速率序列的整体及分阶段变形趋势,并与稳定性分析结果进行对比。实例表明,两种分析方法的结果具有较好的一致性。在分阶段研究中,等时段和递增时段的分析结果具有明显差异,说明阶段分解方式对结果的影响较大,且以递增时段分析结果的规律性更强。     

Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

The Karakoram Highway(KKH), a part of the China–Pakistan Economic Corridor(CPEC), is a major highway connecting northern Pakistan to China. The inventorying and analysis of landslides along KKH are challenging because of poor accessibility, vast study area, limited availability of ground-based datasets, and the complexity of landslide processes in the region. In order to preserve life, property, and infrastructure, and to enable the uninterrupted and efficient operation of the KKH, it is essential to strengthen measures for the prevention and control of geological disasters. In the present study, SBASInSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar) was used to process 150 scenes of Sentinel 1-A images in the year 2017 along the Karakoram Highway. A total of 762 landslides, including 57 complex landslides, 126 rock falls, 167 debris slides, and 412 unstable slopes, ranging in size between 0.0017 and 10.63 km2 were identified. Moreover, this study also gains an inventory of 40 active glacier movements in this region. Landslide categorization, displacements characteristics, spatial distribution, and their relationship with various contributing factors have been successfully investigated along the entire KKH using image interpretation and frequency-area statistics. The criteria adopted for landslides categorization is presented in the study. The results showed that the 2-D ground deformation derived in Hunza valley echoes well with the general regional landslides characteristics. The spatial distribution analysis revealed that there are clumped distributions of landslides in the Gaizi, Tashkurgan, and Khunjerab in China, as well as in Hunza valley, and north of Chilas city in Pakistan. Statistical results indicated that these landslides mainly occur on south-facing slopes with a slope angle of 20°– 45° and elevation relief of 550 – 2,100 m. Landslide development is also related to low vegetation cover and weathering effects in mountain gullies. Overall, our study provides scientific data support and theoretical references for prevention, control, and mitigation of geological disasters in the Karakoram region.     

基于机器学习的滑坡易发性预测建模及其主控因子识别

不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性的主控因子意义重大。针对上述问题, 以支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)为例探讨了基于机器学习的滑坡易发性预测及其不确定性, 创新地提出了"权重均值法"来综合计算出更准确的滑坡主控因子。首先获取陕西省延长县滑坡编录和10类基础环境因子, 将因子频率比值作为SVM和RF的输入变量; 再将滑坡与随机选择的非滑坡样本划分为训练集和测试集, 用训练好的机器学习预测出滑坡易发性并制图; 最后用受试者工作曲线、均值和标准差等来评估建模不确定性, 并计算滑坡主控因子。结果表明: ①机器学习能有效预测出区域滑坡易发性, RF预测的滑坡易发性精度高于SVM, 而其不确定性低于SVM, 但两者的易发性分布规律整体相似; ②权重均值法计算出延长县滑坡主控因子依次是坡度、高程和岩性。实例分析和文献综述显示RF模型相较于其他机器学习模型属于可靠性较高的易发性模型。      

基于物理力学机制的滑坡数值预报模式: 综述、挑战与机遇

滑坡预测预报是地质灾害防治领域长期以来备受关注的前沿科学问题。当前的研究框架专注于滑坡的变形行为特征与外动力因素, 面临着普适性不强与预报精准度不高的双重瓶颈问题。基于目前研究现状, 系统梳理了滑带流变力学行为与强度弱化效应的内涵, 阐述了滑坡渐进破坏演化机制, 总结了滑坡预测预报模型所包含的类型, 并介绍了其中的典型模型, 指出当前研究主要存在如下问题: ①滑坡演化物理力学模型尚需扩展; ②预测预报模型未能充分结合滑坡演化过程和物理力学模型; ③物理力学模型预测与多场监测数据间的兼容性问题未能实际解决。针对上述问题, 阐述了基于物理力学过程的滑坡预测预报所面临的挑战, 并立足多学科融合与交叉, 提出了开展滑坡预测预报研究的新思路。新思路要求从滑坡滑带介质特性与流变力学行为出发, 建立滑坡演化过程物理力学模型, 紧密结合实时多场监测数据, 建立滑坡数值预报模式, 实现滑坡物理力学过程实时动态更新, 以期实现理论与技术的突破。      

基于线激光扫描的岩石激光钻孔的三维重建和可视化

激光钻进岩石形成的钻孔的孔形较为复杂,具有较小的孔直径和较高的孔壁粗糙度,使得利用传统方法进行钻孔尺寸的测量较为困难。为了精确钻孔测量和方便孔形研究,提出了一种基于线激光扫描及逆向建模的钻孔建模方法。首先,搭建了线激光扫描平台,建立了空间坐标系,以获取钻孔的三维坐标,构建了钻孔的初始点云数据。其次,在MATLAB中对获取的点云数据进行无效点移除及多视角点云配准,其中,无效点移除利用顺序查找法实现,多视角点云配准则基于迭代最近点（ICP）算法,包括初始配准和精确配准两个阶段。最后,基于Delaunay三角网格划分及曲面重建算法,实现了钻孔模型的重建和可视化。此外,还采用滴液法和切割法进行实际钻孔容积值测量及钻孔轮廓线获取,并与由点云重建的钻孔模型上获取的测算结果进行对比分析,以验证所述方法建立的钻孔模型的精度。结果表明:重建的钻孔模型与实际钻孔之间的误差小于4%,重建的模型能够满足激光岩石钻进钻孔的测量要求,证实了所述方法的可行性。与传统测量方法相比,所述方法属于非接触、非破坏性方法,可重复性测量。      

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation. The first one is the random displacement, which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide. The second one is the dynamic evolution of a landslide, which could not be feasibly simulated simply by traditional prediction models. In this paper, a dynamic model of displacement prediction is introduced for composite landslides based on a combinat...     

基于AJAX的数据分页的设计与实现

针对传统数据分页存在用户等待时间较长的问题,提出一种基于AJAX的数据分页方法,并成功地在设备管理系统的查询模块上得到应用,实际应用的结果表明,在现有的Web应用中采用AJAX技术,能减轻服务器压力,提高系统的响应速度,减少用户的等待时间。首先对比了传统Web和标准AJAX的交互过程,接着介绍了A-JAX概念及核心技术,同时给出了结合AJAX和J2EE技术实现数据分页的例子。     

基于勘查大数据和数据集市的锰矿床三维地质建模

以黔东北锰矿矿集区的李家湾-高地-道坨超大型"大塘坡式"锰矿床为例,探讨了在开展勘查地质大数据的特征和结构分析的基础上,厘定事实表和维度表,进而通过对勘查数据的收集、提取、ETL操作,并应用OWB工具建立锰矿山勘探数据的数据集市的思路与方法。进而探讨了以所建立的数据集市为数据支撑平台,建立黔东北超大型锰矿床高精度三维地质模型的途径。该矿床三维地质模型汇集了海量的空间数据与属性数据,是锰矿矿山异质异构勘查开发大数据的有效载体。基于该精细、全息的矿床三维地质模型,不但可以直观反映矿床和矿体特征,而且可以作为矿山管理及其深部和外围找矿预测的可视化分析工具。      

基于数据挖掘的录井剖面归位解释方法

录井解释过程中,由于数据特征维度较高和数据集成化能力不足等,需要人工校正录井综合图的岩性剖面。提出一种基于数据挖掘的录井剖面归位解释处理方法,对选取的录井解释数据进行数据清洗,选取有效影响因子,进行基于主成分分析的特征降维;分析录井解释数据特点,选择神经网络算法作为模式挖掘模型;对模式挖掘模型进行表达与解释,将实验获得的最优特征识别模式运用于实际数据。结果表明,采用模式挖掘模型在未知区块的平均识别准确率接近于92%,模型泛化能力相对稳定,对部分常用的岩性的识别准确率接近于95%,与多次人工校正后的归位结果相近。     

Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures. Many factors can influence the occurrence of landslides, which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy. Then the generalization ability of the model will also decline sharply when there are only small samples. To reduce the dimension of calculation and balance the model's generalization and learning ability, this study proposed a landslide prediction method based on improved principal component analysis(PCA) and mixed kernel function least squares support vector regression(LSSVR) model. First, the traditional PCA was introduced with the idea of linear discrimination, and the dimensions of initial influencing factors were reduced from 8 to 3. The improved PCA can not only weight variables but also extract the original feature. Furthermore, combined with global and local kernel function, the mixed kernel function LSSVR model was framed to improve the generalization ability. Whale optimization algorithm(WOA) was used to optimize the parameters. Moreover, Root Mean Square Error(RMSE), the sum of squared errors(SSE), Mean Absolute Error(MAE), Mean Absolute Precentage Error(MAPE), and reliability were employed to verify the performance of the model. Compared with radial basis function(RBF) LSSVR model, Elman neural network model, and fuzzy decision model, the proposed method has a smaller deviation. Finally, the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.     

一种适合于科学数据的聚类算法

聚类是科学数据挖掘中的核心问题.在已提出的聚类算法中大都是基于＂距离＂的概念,这类算法的缺点在于处理数据量大和维数高的科学数据时不够有效,因此提出迭代网格算法.这个算法与基于距离的损法有根本不同,它抛弃了距离的概念,而采取一种新的思路.它不仅能够自动发现包含有趣知识的子空间,并将里面存在的所有聚类挖掘出来;而且它能很好的处理维数高和数据量大的科学数据.     

基于GIS和RS的库区滑坡风险分析——以北京密云库区为例

基于遥感(RS)和地理信息系统(GIS)技术,采用多层次分析(AHP)法,以北京密云水库上游部分库区为研究对象,根据库区实际情况,选取植被覆盖率、土壤类型、坡度、高程、降雨量、土地利用六种影响滑坡灾害发生的因素作为评价因子,对区域滑坡风险进行分析。同时,在ArcGIS的空间分析环境中运行权重叠加,将研究区域划分成无危险区、低危险区、中危险区、高危险区和极危险区。结果表明,研究区域内大部分地区比较稳定,最易发生滑坡的区域主要集中在密云水库东北部的河谷、山谷地带,呈零星状分布。