Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Parallelized combined finite-discrete element (FDEM) procedure using multi-GPU with CUDA

This paper focuses on the efficiency of finite discrete element method (FDEM) algorithmic procedures in massive computers and analyzes the time-consuming part of contact detection and interaction computations in the numerical solution. A detailed operable GPU parallel procedure was designed for the element node force calculation, contact detection, and contact interaction with thread allocation and data access based on the CUDA computing. The emphasis is on the parallel optimization of time-consuming contact detection based on load balance and GPU architecture. A CUDA FDEM parallel program was developed with the overall speedup ratio over 53 times after the fracture from the efficiency and fidelity performance test of models of in situ stress, UCS, and BD simulations in Intel i7-7700K CPU and the NVIDIA TITAN Z GPU. The CUDA FDEM parallel computing improves the computational efficiency significantly compared with the CPU-based ones with the same reliability, providing conditions for achieving larger-scale simulations of fracture.     

灰色组合模型在滑坡变形监测中的应用分析

在GM(1,1)模型的基础上,主要研究了改进残差修正模型、灰色BP神经网络模型、灰色线性回归模型在变形数据的预计精度,并且结合实例分析了不同灰色组合模型在滑坡变形预计的精度以及优缺点。     

GPS精密卫星钟差估计研究

随着精密单点定位技术的发展,对于精确的卫星坐标以及卫星钟差改正精度的要求越来越高,精密卫星星历以及精密卫星钟差的求解成为制约精密单点定位技术发展的瓶颈。本文基于修复周跳的载波相位观测值与相位平滑伪距观测值,采用无电离层延迟星间单差精密卫星钟差估计模型,在先估计出整周模糊度后,进行了精密卫星钟差的估计,并采用与IGS事后精密钟差作二次差的方法进行精度分析,这对于提高精密单点定位精度具有一定的意义。     

北斗系统周跳探测与修复方法探究

北斗卫星导航系统是中国自行研制的继美国GPS和俄罗斯GLONASS之后第3个成熟的卫星导航系统。进行GNSS相位数据处理的过程中,由于某种原因,载波相位观测值突然出现整周数的跳变的现象称为周跳。周跳现象的发生将会严重影响后续的数据处理,因此,有必要在解算前完成周跳探测与修复工作。本文就北斗系统周跳探测与修复课题进行了较为全面的探究,先后介绍了高次差法、多普勒法、伪距载波相位组合法、电离层残差法、三频数据组合探测法等一系列周跳探测与修复方法,可分别应用于不同频段接收机、不同观测环境、不同数据处理模式。     

回归-Elman网络在矿坝变形预测中的应用

金矿开展矿坝的变形监测工作,引入多层递阶回归分析模型,有较高的预测精度,但方法较繁琐,计算较复杂。由于变形数据可分离成趋势项与随机项,趋势项可用多元线性回归良好地拟合;随机项的预测,文中采用Elman网络建模计算,最后利用矿坝的实测高程位移数据进行验证,并与多层递阶回归进行比较。结果表明:回归-Elman网络模型比多层递阶回归的预测精度更高,效果更好,且方法简洁实用性强。     

InSAR生成DEM中WRF大气校正

利用星载重复轨道合成孔径雷达干涉测量InSAR技术获取数字高程模型(DEM),无法避免大气延迟效应的影响。InSAR大气校正的方法很多,但在DEM获取方面的大气校正研究却非常少见。本文研究星载重轨InSAR生产DEM时利用大气数值模式WRF(Weather Research and Forecasting model)得到的水汽结果进行大气校正的问题,重点讨论大气校正的策略,包括WRF模式设置和大气校正时机的选择,简要介绍了基于WRF运算结果的大气校正方法。利用Terra SAR-X数据进行实验,检验了所提出方法的有效性,证明了在干涉相位解缠前进行大气校正,比在相位解缠后进行的效果更好。将所提出方法应用于多基线、多波段InSAR干涉结果融合中,实验结果表明大气校正能够有效降低误差,对于相干性较高的地区效果更好。     

遥感与地球系统科学

地球作为一个高度复杂的非线性系统,各圈层(大气、海洋、陆地、生物、冰雪圈、固体地球)尤其是人类活动等任何组成成份的变化,都会引起地球系统的变化。人类可持续发展面临的巨大科学挑战之一是认识人类赖以生存的、复杂变化的地球系统,认识地球系统如何变化及主要驱动因素,认识地球系统未来变化趋势及如何提高对全球变化的适应能力。卫星独特的全球覆盖和日尺度的观测改变了地球科学的研究方法,它强调所能探测到的多时空尺度上的物理动力过程,在全球范围应对气候变化、能源和环境挑战具有重要作用,揭开了地球系统多学科交叉的新纪元。以地球系统的视野,抓住驱动地球系统的关键循环过程(如能量、水、生物化学循环),是当前地球系统科学的发展趋势。地球系统科学(全球变化)研究需要长期稳定、准确性较高的卫星观测数据,以水循环为例,卫星遥感具备获取全球范围水循环关键参数能力,但是系统性综合观测能力不足,整体精确性受到综合化的可靠空间数据集的限制。目前中国正在积极研制发展新型水循环卫星WCOM(Water Cycle Observation Misssion),并寄希望以此为核心传感器发起全球分布式水循环观测星座系统,进一步提高中国在国际水循环观测与地球系统科学研究方面的话语权与领先能力。     

松嫩盆地北部林甸地热田供暖尾水处理试验

将混入污染物和杂质的地热尾水直接排向地表水体,会造成水、土环境污染,也会造成水资源的浪费。为高效减少地热尾水中污染物,实现供暖地热尾水的无害化排放,以林甸县花园镇供暖地热尾水为例,通过混凝沉淀、超滤、纳滤工艺开展供暖尾水处理的室内实验和中试试验研究,连续监测获取地热尾水各处理工艺运行参数,组合工艺稳定运行期间监测到溶解性总固体浓度由5 824.7 mg/L降为432.40 mg/L、氯化物浓度由3 010.13 mg/L降为194.16 mg/L、氟化物浓度由1.57 mg/L降为0.31 mg/L、硼浓度由4.04 mg/L降为1.62 mg/L。确定该区地热尾水处理的最佳组合工艺为采用超滤运行压力为0.1 MPa,纳滤膜为陶氏NF90,操作压力为0.60 MPa,进水量为0.8 m3/h。室内实验溶解性总固体去除率90.21%～92.49%,氯化物去除率91.63%～93.02%,氟化物去除率96.81%,硼去除率55.20%～55.69%。中试试验溶解性总固体去除率为92.62%;氯化物浓度200 mg/L,氯化物去除率为92.57%;硼浓度1.77 mg/L,硼去除率为55.7%,以上实验结果均达到相关规范标准。试验证明本工艺处理松嫩盆地北部林甸地热田供暖尾水是可行的,解决了供暖尾水处理中资源浪费和尾水回收利用的技术性问题,为地热资源可持续利用提供了新的途径。     

同层型气钾复合矿藏的形成机理浅析:以四川盆地磨溪背斜中三叠统雷口坡组富钾卤水矿为例

四川盆地中三叠统雷口坡组中发育多种类型的钾盐矿藏,是我国找钾的重点目标层系。磨溪气田雷口坡组地层卤水富含K、Li、Br、B元素,定性为中低矿化度KCl、LiCl、Br^-、B₂O₃等超标的优质化工原料水,显示出较好的钾盐勘探前景。地震资料解释结果表明,磨溪气田雷口坡组一段的第一亚段(雷一¹)顶面表现为一个完整的长轴背斜形态,断层不发育。富钾卤水发育在背斜的翼部,而天然气则占据了背斜的核部,二者共同贮存在雷一¹亚段溶蚀孔洞发育的滩相白云岩中,构成了典型的同层型气钾复合矿藏。借鉴含油气系统的概念,将富钾卤水的形成、聚集及后期保存等影响卤水型钾矿形成全过程的各种因素总结归纳为6大关键要素,即钾的物质来源,储层物性、封盖条件、圈闭条件、运移方式和保存条件。在乐山—龙女寺古隆起形成和演化的背景下,确定构造圈闭形成时间,分析优质储层发育有利因素以及后期保存条件,并按照各要素在时空上的搭配关系,动态地分析雷口坡组同层型气钾复合矿藏的形成过程。烃类物质和卤化物都是地下水所含的特殊物质,在地下水动力场的作用下,地层水在雷口坡组内部向着川中地区的继承性发育的古构造高点——磨溪背斜持续运移。被运移而来的由蒸发浓缩的古海水和石膏脱水形成的初始富钾地层水不断捕获下伏绿豆岩释放的钾离子,使得钾离子二次富集成藏。在地层水运移和气藏压力的共同作用下,形成气在上、富钾卤水在下的复合矿藏。长期继承性发育的古隆起不仅是油气聚集的有利部位,亦是二次运移、聚集的富钾卤水矿藏发育部位。本次研究成果为四川盆地及其他类似盆地的“气钾兼探”工作提供了一种新的方法和思路。