基于随机森林的光子计数激光雷达点云滤波     

陈博伟  庞勇  李增元  卢昊  梁晓军 《地球信息科学学报》2019,21(6):898-906

新一代星载激光雷达卫星ICESat-2首次采用了微脉冲光子计数激光雷达技术,由于单光子探测的灵敏性导致数据在大气和地表下层产生了大量噪声,因此对光子计数激光雷达点云数据实现信号和噪声的分离是开展进一步应用研究的前提和基础。本文选择美国俄勒冈州和弗吉尼亚州2个研究区,采用MATLAS数据,根据光子点云数据的特点构造了12个光子点云特征,对所构造的特征利用随机森林进行变量筛选,用机器学习方法对光子点云进行分类,并将建立好的模型推广到整个研究区。研究结果表明,本文构建的分类器分类总精度达到了96.79%,Kappa系数为0.94,平均生产者精度和用户精度分别为97.1%和96.8%。在相对弱噪声、平坦地形区域和强噪声、复杂地形区域都取得较好的分类结果。本文结果显示了基于少量样本通过机器学习的方法构建模型,可以推广到较大范围区域的光子点云分类应用中。  相似文献   

滇西南钾盐调查MK-3大口径超深井绳索取心钻进工艺          下载免费PDF全文

吕利强 《探矿工程》2020,47(3):53-58

滇西南中生代盐盆地钾盐资源调查评价项目MK-3井，设计井深2700 m，全孔取心，岩心直径≮80 mm，一般井段岩心采取率达到80%以上，盐岩段岩心采取率＞95%。工程技术要求高，施工难度大。施工中遇到了地层坍塌、涌水、掉块、盐岩溶蚀等问题，通过采用氯化镁饱和盐水冲洗液，HXY-9B型钻机取心钻进、TSJ-2000型水源钻机扩孔钻进，分级下管隔离等措施，圆满完成了钻探任务。完钻井深2701 m，岩心采取率90%，盐岩段岩心采取率达98.65%，终孔口径127 mm，岩心直径81 mm。经地球物理测井，各项指标都达到了设计要求。刷新了国内CHD127标准绳索取心钻杆P口径钻探深度的记录。  相似文献   

A model‐based data‐driven dictionary learning for seismic data representation          下载免费PDF全文

Can Evren Yarman  Rajiv Kumar  James Rickett 《Geophysical Prospecting》2018,66(1):98-123

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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流塑淤泥地层深基坑钢板桩围堰位移突变原因分析及处理          下载免费PDF全文

杨勇  詹元林 《探矿工程》2020,47(3):69-74

广东省珠海市洪鹤大桥主墩承台位于珠江西江流域的流塑状淤泥地层，采用钢板桩围堰进行基坑支护，基坑开挖过程中，钢板桩围堰发生较大的变形。经详细分析，发现导致事故的主要原因有地下水位持续升高导致土体力学性能显著下降、边跨侧钢板桩长度不足、基坑边缘集中荷载过大、施工控制不严、内支撑体系施工精度不足等。为了确保深基坑支护的安全，在全面分析总结了钢板桩围堰变形原因的基础上，结合实际情况，采取了增设穿透淤泥质土层的钢管桩围堰、加强内支撑体系等加固处理措施，并在实施过程中进行持续监测，最终安全地完成了基坑工程的施工。  相似文献   

水源钻机在超深土层绳索取心钻进中的应用          下载免费PDF全文

杨联锋  段云星 《探矿工程》2020,47(11):44-50

山西省清徐县区域地质调查项目设计800、2000、3000 m科学钻探孔，以调查填补新生界底板埋深控制空白区，各孔钻入基岩30 m完钻。要求全孔取心，岩心采取率≮85%，岩心直径≮60 mm，采用塑料保护管采取原状岩样。针对超深软土层、各组地层特性及厚度未知、钻遇基岩完钻深度未知、大直径高保真全孔取心、项目价格远低于目前市场成本等难题，经过“水源钻机+大提钻取心+长裸眼孔段”实施800 m孔、“岩心钻机+绳索取心+套管固井”实施640 m参数对比孔，创新性使用“水源钻机+绳索取心+长裸眼孔段”工艺完成了2000 m孔的施工。该工艺岩心采取率达到93%，孔径和孔斜符合地质要求，为3000 m孔顺利施工打下了坚实的基础，为同类型项目提供了经验和借鉴。  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

基于深度置信网络的GNSS-IR土壤湿度反演     

陈堃  沈飞  曹新运  朱逸凡 《测绘通报》2020,(9):100-105

基于大地测量型GNSS接收机获取的反射信号反演土壤湿度是GNSS领域的研究热点。为克服常规线性回归和BP神经网络算法等的缺陷，本文提出了一种基于深度置信网络的GNSS-IR土壤湿度反演方法。试验结果表明，基于该方法得到的决定系数、土壤湿度平均绝对误差和均方根误差分别为0.909 8、0.017、0.021，与线性回归和BP神经网络算法相比，与实测数据吻合度更高，可有效提高土壤湿度反演精度，证明了方法的有效性和可靠性。  相似文献