土水特征曲线的通用数学模型研究

土水特征曲线对于研究非饱和土的物理力学特性至关重要。根据土水特征曲线可以确定非饱和土的强度、体应变和渗透系数 ,甚至可以确定地下水面以上水份分布。由于土体物理力学特性的差异 ,导致描述其土水特征曲线的数学模型也各不相同。因此 ,建立土水特征曲线的通用数学表达式 ,显得尤为必要。本文对土水特征曲线数学模型进行了研究 ,依据这些模型的数学表达式形式 ,将其划分为 4种类型。分别由这 4种类型的数学模型推导出具有统一表达式形式的土水特征曲线通用数学模型 ,并运用陕北高原黄土土水特征曲线试验数据对通用数学模型进行了研究     

卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例

大数据人工智能地质学刚刚起步,基于大数据智能算法的地质研究是非常有意义的探索性实验。利用大数据和机器学习解决矿产预测问题,有助于人们克服不能全面考虑地质变量的困难及评估当前模型在已有数据中的可靠性。元素地表分布特征量主要受原岩成分、成矿作用影响和地表过程的影响,它们携带某些指示矿体就位的信息,即矿体在地下空间就位时在地表的响应,且未在地表过程中消失。以往的地球化学勘查工作仅仅识别异常,但未能发现矿体在地表响应的成矿特征量。本文以安徽省兆吉口铅锌矿床为例,通过机器学习,利用卷积神经网络算法,不断挖掘元素Pb分布特征与矿体地下就位空间的耦合相关性。经过1000次训练后,可以得到准确率0. 93,损失率0. 28的卷积神经网络模型。这种神经网络模型就是矿体在地下就位时元素在地表分布的响应,可以用来进行矿产资源预测。应用该模型对未知区进行预测,结果显示第53号区域具有很大概率存在尚未发现的矿体。     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

基于卷积神经网的CCTV视频中排水管道缺陷的智能检测

作为地下空间信息测绘工作的一个重要部分,基于排水管道内部测绘信息的管道缺陷检测越来越受到人们的重视。CCTV技术是一种广泛使用的排水管道内部测绘与缺陷检测技术。近些年基于卷积神经网的人工智能技术在图像识别中取得了巨大成功,受此启发,提出了一种基于卷积神经网络的排水管道缺陷的检测方法,以提高CCTV视频中的管道缺陷检测的自动化和智能化。试验证明了该方法的有效性,其在缺陷识别的准确率和召回率及识别速度上均满足了排水管道缺陷智能检测的需要;同时该方法也已经在深圳市的排水管道检测中得到广泛的应用。     

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.     

利用多项式拟合模板法消除地震数据中的低频随机干扰

提出利用多项式拟合模板法消除地震数据中的低频随机干扰，分析模板的性质并推导出模板参数与低频随机干扰最高频率之间的关系式。     

表面气温内部变率估算方法的比较研究

本文利用37个CMIP5模式和CESM（Community Earth System Model）包含40个成员的超级集合试验的表面气温预估数据，比较了工业革命前气候参照试验、多项式拟合法和方差分析方法这三种目前在国际上运用较多的方法所估算的表面气温内部变率的异同，分析了内部变率的估算对气候预估中信号萌芽时间（TOE）的影响。结果表明:若采用CMIP5多模式集合，则工业革命前气候参照试验和多项式拟合法都是估算内部变率的合理方法，而方差分析方法则由于包含模式性能自身的影响会夸大内部变率故不推荐使用。内部变率的全球分布呈现出极向强化的现象，中高纬度地区的内部变率幅度远大于热带、副热带地区。内部变率受不同排放情景的影响较小，且随时间无显著变化，但方差分析方法估算的内部变率在热带地区容易受到排放情景的影响。若基于类似CESM这样的单个气候模式的超级集合模拟试验来估算内部变率，三种方法估算的结果相似。不同方法估算的内部变率对TOE的影响主要位于北大西洋拉布拉多海、南大洋威德尔海和罗斯海等邻近海洋深对流区。对于中国区域平均来说，基于CESM超级集合模拟试验，三种方法估算的内部变率与强迫信号之比都小于15%；对CMIP5多模式集合，采用工业革命前气候参照试验和多项式拟合法得到的结果与此接近，但若采用方差分析方法则显著高估内部变率的作用。     

基于有理多项式模型的星载高分InSAR影像制作数字高程模型的研究

合成孔径雷达干涉测量是一种利用SAR影像复数据的相位信息提取地面三维信息的技术,它通过获取地面目标在两幅SAR影像上的相位差值来解算该点的三维坐标。有理多项式（RPC,Rational Polynomial Coefficient）模型作为一种数学意义上的几何模型,它独立于传感器和平台,简单且具有通用性,可建立地面任意坐标与影像空间的关系。本文在RPC模型用于替代星载SAR的距离多普勒模型和星载InSAR的干涉相位方程基础上,研究 RPC模型应用于星载高分辨率InSAR影像制作DEM的可行性和精度。利用兰州COSMO-SkyMed数据和资源三号三线阵数据制作的DEM为参考数据进行实验验证,RPC模型用于InSAR技术生成的DEM,中误差为7.55米。     

A simple prediction model for wall deflection caused by braced excavation in clays

Deep excavations particularly in deep deposits of soft clay can cause excessive ground movements and result in damage to adjacent buildings. Extensive plane strain finite element analyses considering the small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by retaining walls and bracing. The excavation geometry, soil strength and stiffness properties, and the wall stiffness were varied to study the wall deflection behavior. Based on these results, a simple Polynomial Regression (PR) model was developed for estimating the maximum wall deflection. Wall deflections computed by this method compare favorably with a number of field and published records.     

利用IKONOS立体像对提取DEM精度的实验

由于IKONOS不提供基于共线模型所需的卫星星历参数,而提供基于有理函数(RPC)的近似模型,因此点位精度和高程精度主要由RPC模型精度来决定。直接使用RPC模型对标准几何校正产品进行DEM提取,这种精度不能满足高精度制图的要求。本文讨论了基于IKONOS立体像对提取翠华山地区的DEM及其精度情况。为提高精度,采用高精度GPS仪(Mobile Mapper CX高程精度达亚米级)野外采集控制点,对RPC模型进行修正。对比分析结果表明DEM精度明显提高,仅需少量的控制点,DEM和正射影像均满足15∶000比例尺地形图和遥感平面制图要求。