高起伏区无人机摄影测量精度评估?以西秦岭光盖山-迭山断裂为例

经过近10年的迅速发展,无人机摄影测量已成为活动构造研究的常用方法之一。但对于无人机摄影测量的精度评估,尤其是高起伏地区的精度评估存在不足。为此,选择白龙江北岸光盖山-迭山断裂沿线的黑峪寺、化马村,开展无人机摄影测量,并构建正射影像（DOM）和数字地表模型（DSM）,配合差分GPS测绘进行校正和精度验证。通过对比实测控制点和图像提取点分析点精度,通过对比实测剖面与提取剖面分析剖面精度。研究结果表明,未经控制点校正的图像提取点与实测点存在较大误差,水平误差为5～8 m,垂直误差为几十米至上百米,但通过少数控制点校正后,点精度可达20 cm以内;6条实测剖面与提取剖面（提取自控制点校正后的图像）平均垂直精度总体为分米级,即0.16～0.65 m,标准差为0.13～0.69 m,略低于低起伏区的精度,对于测量条件恶劣的高起伏区,该精度是可接受的;异常高的垂直误差常出现在地形突变、低矮植被密集、行走困难等测量条件不理想位置。图像控制点中心点的准确识别、提取剖面线的修正准确性等因素也会影响精度评估的可靠性。     

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.     

影响四轨法D-InSAR形变测量精度误差的相关性分析

卫星成像的基线、视角、基线倾斜角、斜距、卫星距离地面径向距离以及地面分辨率等因素严重影响着合成孔径雷达差分干涉测量监测地面形变的能力和形变监测结果的精度。本文在分析四轨法D-InSAR基本原理和数据处理流程基础上,详细给出了相位测量误差对形变测量精度影响的定量关系式;分析讨论了基线长度误差、基线倾斜角误差、斜距误差、卫星高度误差和地形因素误差对形变测量精度的影响。从而在定量分析方面得出了这些误差对四轨法D-InSAR形变测量精度影响的结论。     

On the use of dimensioned measures of error to evaluate the performance of spatial interpolators

Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous.     

温度对DiNi12数字水准仪i角误差的影响及应对措施

i角误差的大小和稳定是保证水准测量精度的一个重要条件,在实际观测中发现．DiNi12数字水准仪的i角误差不稳定,有随温度的升高而变化的趋势。通过对不同温度下的i角误差的观测,积累一定的观测数据,找出i角误差的变化规律,并采取多种措施最大限度减小i角误差对观测资料的影响．提高观测精度。     

A regularized Wiener–Hopf filter for inverting models with magnetic susceptibility

For a magnetic target, the spatial magnetic signal can be expressed as a convolutional integral over Green's function of an assumed model with susceptibility as its parameter. A filter can be used to obtain the susceptibility by minimizing the mismatch between observed and the computed magnetic anomalies. In this perspective, we report the development of an advanced digital filter, which is efficient and can be used to map rock susceptibility from the acquired magnetic data. To design the new filter, we modified the space‐domain standard Wiener–Hopf filter by imposing two different constraints: (i) the filter energy constraint; and (ii) normalization of the filter coefficients. These constraints make it capable to characterize source bodies from their produced magnetic anomalies. We assume that the magnetic data are produced by induced magnetization only and interpretation can be as good as the subsurface model. Our technique is less sensitive to the data noise, which makes it efficient in enhancing the interpretation model. The modified filter demonstrates its applicability over the synthetic data with additive white Gaussian noise. In order to check the efficacy and adaptivity of this tool in a more realistic perspective, it is also tested on the real magnetic data acquired over a kimberlitic district adjoining to the western margin of the Cuddapah Basin in India to identify the source bodies from the anomalies. Our result shows that the modified Wiener–Hopf filter with the constraint for the magnetic data is more stable and efficient than the standard Wiener–Hopf filter.     

Adaptive integration of constitutive rate equations

In finite element calculations the constitutive model plays a key role. The evaluation of the stress response of the constitutive relation for a given strain increment, which is a time integration in the case of models of the rate type, is a typical sub task in such calculations. Adaptive behaviour of the time integration is essential to assure numerical stability and to control the accuracy of the solution. An adaptive second order semi-implicit method is developed in this paper. Its numerical behaviour is compared with an adaptive second order explicit scheme. The two proposed methods control the local error and guarantee numerical stability of the time integration. We include several numerical geotechnical element tests using hypoplasticity with intergranular strain. The element tests simulate the behaviour of a finite element method based on the displacement formulation.     

利用解析法有效快速估计将来GRACE Follow-On地球重力场的精度

本文首次利用解析法有效快速估计了将来GRACE（Gravity Recovery and Climate Experiment） Follow-On地球重力场的精度. 第一,基于功率谱原理分别建立了新的GRACE Follow-On卫星激光干涉星间测量系统星间速度、GPS接收机轨道位置和轨道速度以及加速度计非保守力误差影响累计大地水准面的单独和联合解析误差模型. 第二,利用提出的GRACE卫星关键载荷匹配精度指标和美国喷气推进实验室（JPL）公布的GRACE Level 1B实测精度指标的一致性,以及估计的GRACE累计大地水准面精度和德国波兹坦地学研究中心（GFZ）公布的EIGEN-GRACE02S地球重力场模型实测精度的符合性,验证了本文建立的解析误差模型是可靠的. 第三,论证了GRACE Follow-On卫星不同关键载荷匹配精度指标和轨道高度对地球重力场精度的影响. 在360阶处,利用轨道高度250 km、星间距离50 km、星间速度误差1×10^-9m/s、轨道位置误差3×10^-5m、轨道速度误差3×10^-8m/s和非保守力误差3×10^-13m/s²,基于联合解析误差模型估计累计大地水准面的精度为1.231×10^-1 m. 本文的研究不仅为当前GRACE和将来GRACE Follow-On地球重力场精度的有效快速确定提供了理论基础和计算保证,同时对国际将来GRAIL（Gravity Recovery and Interior Laboratory）月球卫星重力测量计划的成功实施具有重要的参考意义.     

行播作物地面方向性测量的视场不确定性分析

行播作物以其独特的几何结构介于离散与连续植被之间。地面测量此类地物的双向反射系数(Bidirectional Reflectance Factor,BRF)特征,不可回避视场变化所引起的不确定性问题。在Kimes垄行结构模型中加入等效视场的概念,对视场进行分解,从而建立了一个行结构多角度地面测量的视场不确定性分析模型,为定量分析视场变化所引起的BRF测量误差提供了可能。利用该模型较为全面地模拟分析了视场变化对视场内四组分比例及冠层BRF的影响。结果表明:①BRF误差基本独立于植被—土壤光谱对比度。②误差与观测天顶角之间的关系复杂,前向观测表现得尤为明显。③垂直观测视场满1个垄周期后,四组分比例及冠层BRF的误差可保持较小且稳定的状态;满2个垄周期,误差达到局部最小值(局部指垂直视场含2.5个垄周期以下,不排除视场更大,误差更小的可能性)。④垂直视场若仅含0.5个垄周期,BRF误差最大值一般可高达67.8%,最小值亦可达38.7%;满1个垄周期后,BRF误差极大值降至20%以下,极小值可控制在6%以内。其中视场为1个整周期,误差范围为6%~12%;2个整周期,误差范围为0.6%~3.9%。⑤垂直视场大小为1~2...