一种基于高斯马尔可夫随机场的异常目标探测方法     

杜博  陈勇  史瑞芝 《测绘科学》2010,35(6):180-182,154

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。  相似文献   

基于随机森林模型的农业电子商务浅析——洛川苹果网络销售问题与对策     

张晓涵  马敏飞  杨联安 《地下水》2020,(1):119-122

随着互联网产业的飞速发展,电子商务开始进入农业领域。以电子商务起步较早的"洛川苹果"作为研究对象进行调研。基于随机森林模型的决策树集成算法,对农业网络销售体系整体进行数据分析,模型构建,从问题表象出发挖掘其在不同部分的影响因子,最终基于影响因素解决问题,提出合理化建议:加强农村基础设施建设、健全农村公共服务体系以及完善农村电子商务培训制度等,因地制宜,推进农业电子商务的健康发展。  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement     

Yanping Liu  Zhengguo Yan 《Geophysical Prospecting》2020,68(6):1727-1741

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.  相似文献   

考虑填充墙影响的钢筋混凝土厂房结构地震易损性分析     

梁志强  徐超  温增平 《震灾防御技术》2019,14(4):707-718

采用对角斜撑模拟纵向填充墙的作用,建立考虑填充墙和不考虑填充墙厂房结构模型,采用拉丁超立方抽样技术建立考虑材料不确定性的结构分析样本,基于随机Pushover分析确定结构不同破坏状态下的统计参数。综合考虑结构材料强度及输入地震动不确定性的影响,通过非线性时程分析开展单层钢筋混凝土厂房结构易损性研究,在此基础上比较结构横、纵向易损性的差异,研究填充墙对结构易损性的影响。研究结果表明:钢筋混凝土厂房结构体系横向地震易损性显著大于纵向地震易损性;对纵向结构体系而言,加入填充墙会明显降低结构易损性,但在相同强度的地震动作用下,填充墙破坏程度比主体结构严重,这与厂房结构的实际震害特征相符。  相似文献   

Slopewash,surface runoff and fine-litter transport in forest and landslide scars in humid-tropical steeplands,luquillo experimental forest,Puerto Rico     

Matthew C. Larsen  Angel J. Torres-Snchez  Iris M. Concepcin 《地球表面变化过程与地形》1999,24(6):481-502

Rainfall, slopewash (the erosion of soil particles), surface runoff and fine-litter transport steepland sites in the Luquillo Experimental Forest, Puerto Rico (18° 20’ N, 65° 45’ W) were measured from 1991 to 1995. Hillslopes underlain by (1) Cretaceous tuffaceous sandstone and silstone in subtropical rain (tanonuco) forest with vegetation recovering from Hurricane Hugo (1989), and (2) Tertiary quartz diorite in subtropical lower mantone wet (colorado and dwarf) forest with undisturbed forest canopy were compared to recent landslide scars. Monthly surface runoff on these very steep hillslopes (24° to 43°) was only 0·2 to 0·5 per cent of monthly rainfall. Slopewash was higher in sandy loam soils whose parent material is quartz diorite (averaging 46 g m⁻² a⁻¹) than in silty clay loam soils derived from tuffaceous sandstone and siltstone where the average was 9 g m⁻² a⁻¹. Annual slopewash of 100 to 349 g m⁻² on the surfaces of two recent, small landslide scars was measured initially but slopewash decreased to only 3 to 4 g m⁻² a⁻¹ by the end of the study. The mean annual mass of fine litter (mainly leaves and twigs) transported downslope at the forested sites ranged from 5 to 8 g m⁻² and was lower at the tabonuco forest site, where post-Hurricane Hugo recovery is still in progress. Mean annual fine-litter transport was 2·5 g m⁻² on the two landslide scars. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

复杂山地随机介质GMM-ULTI法射线追踪   总被引：2，自引：2，他引：0       下载免费PDF全文

孙章庆  孙建国  岳玉波  齐鹏  黄兴国 《地球物理学报》2016,59(7):2615-2627

对复杂山地介质的非均质性以及介质中地震波运动学特征进行深入研究,对于提高复杂山地区域地震勘探的效果有着重要的理论意义和实际价值.为了研究复杂山地非均质性和该介质中地震波的一些运动特性,提出了一种复杂山地随机介质的建模方法和一种新的射线追踪算法.与常规算法相比,复杂山地随机介质的生成方法采用更贴近实际介质特点的梯度介质作为背景介质,并在模型生成过程中加入地形修正步骤;新提出的GMM-ULTI射线追踪算法,充分融合群推进法、迎风思想、走时插值法的优势,采用先计算走时后追踪射线路径的两步策略完成射线追踪.算法分析与计算实例表明:复杂山地随机介质的生成方法能灵活、精细且更贴近实际地刻画复杂山地介质的非均质特点;新射线追踪算法兼顾精度和效率、能无条件稳定且灵活地适应复杂山地随机介质的特点;同时基于对几个模型试算结果的分析也得出了复杂山地随机介质中的地震波的一些传播规律.  相似文献   

多相离散随机介质模型及其探地雷达波场特征研究   总被引：2，自引：2，他引：0       下载免费PDF全文

郭士礼  冀孟恩  朱培民  李修忠 《地球物理学报》2015,58(8):2779-2791

沥青混凝土是由骨料、沥青胶浆、空气按照一定的体积百分比混合而成的多相非匀质混合物,其骨料、沥青胶浆和空气的体积不等、形状各异、介电特性不同、空间位置随机分布,具有明显的多相、离散、随机介质特征.本文基于随机介质模型理论,(1)测量与统计了介电常数在典型沥青混凝土芯样空间上的随机分布统计特征;(2)估算了沥青混凝土介质的自相关函数及其特征参数(自相关长度、自相关角度等),确定其随机介质类型;(3)提出了量化约束下的多相离散随机介质建模算法,以混合型椭圆自相关函数为基础,构建了不同粗糙度因子的多相离散随机介质模型;(4)构建了不同空隙率的多相离散随机介质模型,正演模拟与对比分析了探地雷达波在均匀介质、连续型随机介质和多相离散随机介质中的传播特征.结果表明:多相离散随机介质模型不仅描述了沥青混凝土的多相、离散与空间随机分布统计特征,而且进一步描述了其各组成物质体积百分比,能更全面、准确地描述沥青混凝土的介质特征,同时也为描述其他类似材料或介质提供了新的方法和途径;在多相离散随机介质模型中,探地雷达波散射强烈,随机、无序传播的散射波相互叠加干涉,形成了明显的随机扰动和"噪声",致使异常体反射波扭曲变形、不连续,降低了探地雷达回波的信噪比和分辨率.研究探地雷达波的随机扰动特征与多相离散随机介质模型参数之间的关系,将为定量评价多相离散随机介质的属性参数提供参考和帮助.  相似文献   

1998-2012年青藏高原TRMM 3B43降水数据的校准   总被引：1，自引：0，他引：1  

石玉立  宋蕾 《干旱区地理》2015,38(5):900-911

运用1998-2012年青藏高原的TRMM 3B43降水数据以及气象台站实测降水数据,对比分析了青藏高原地区TRMM 3B43降水数据偏差分布规律。结果表明:(1) TRMM 3B43降水数据在青藏高原地区存在明显误差,特别是降水量大的地区和月份,偏差量较大。(2)青藏高原地区TRMM 3B43降水数据偏差分布与海拔、经纬度、降水量存在密切的关系。用偏差分布规律,加法修正法结合随机森林算法对青藏高原地区TRMM 3B43降水数据进行了校准。经过校准之后,数据精度得到显著提高,有效增加了数据的可用性,多年月平均数据决定系数R²最大可达到0.9(3、10月),最小也接近于0.5(12月),效率系数E均为正值,最大可达到90(3、10月);多年季平均和多年平均降水数据中除了第一季度结果稍差外(决定系数R²为0.58),其余数据校准效果均较好。  相似文献