锚固站对BDS自主导航精度的影响分析     

龚晓颖  蔡诗响 《测绘通报》2020,(4):68-75

介绍了自主导航的轨道确定及时间同步观测方程。以北斗仿真全星座为对象，通过采用仿真星间及卫星与地面锚固站间观测值，进行了60 d自主导航解算，分别探讨了锚固站数量及锚固站观测连续性对北斗卫星导航系统（BDS）3类卫星自主导航精度的影响。结果表明：锚固站数量及观测连续性对RERR及CERR无影响；加入1个锚固站即可显著改进URE结果精度，继续增加锚固站数量虽然可进一步提高URE精度但其改进效果有限；锚固站观测中断时间越长，其对应自主导航精度越低。因此，在BDS自主导航运行模式下应保持较高的锚固站观测频次以保证自主导航精度；另外，锚固站数量及观测连续性对北斗系统3类卫星自主导航精度的影响无显著差异。  相似文献   

基于遗传算法优化BP神经网络的玉米遥感估产方法     

于海洋  陈圣波  杨北萍  安秦 《世界地质》2020,39(1):208-214

以内蒙古自治区开鲁县玉米作物为研究对象,将生育期内玉米遥感影像所提取的多种植被指数和实地采样点的测产数据作为训练值,利用BP(back propagation)神经网络和遗传算法优化BP(GA-BP)神经网络估产模型,得出网络预测的玉米产量数值。通过决定系数R 2和均方根误差RMSE,比较实测产量与预测产量之间的精度,BP神经网络模型R^2为0.8452,RMSE(%)为28.37;遗传算法优化BP神经网络模型R^2为0.9850,RMSE(%)为6.70,表明遗传算法优化BP神经网络估产模型具有一定可行性和可信度。  相似文献   

Some remarks on Q-compensated sparse deconvolution without knowing the quality factor Q     

Xintao Chai  Ronghua Peng  Genyang Tang  Wei Chen  Jingnan Li 《Geophysical Prospecting》2019,67(8):2003-2021

The subsurface media are not perfectly elastic, thus anelastic absorption, attenuation and dispersion (aka Q filtering) effects occur during wave propagation, diminishing seismic resolution. Compensating for anelastic effects is imperative for resolution enhancement. Q values are required for most of conventional Q-compensation methods, and the source wavelet is additionally required for some of them. Based on the previous work of non-stationary sparse reflectivity inversion, we evaluate a series of methods for Q-compensation with/without knowing Q and with/without knowing wavelet. We demonstrate that if Q-compensation takes the wavelet into account, it generates better results for the severely attenuated components, benefiting from the sparsity promotion. We then evaluate a two-phase Q-compensation method in the frequency domain to eliminate Q requirement. In phase 1, the observed seismogram is disintegrated into the least number of Q-filtered wavelets chosen from a dictionary by optimizing a basis pursuit denoising problem, where the dictionary is composed of the known wavelet with different propagation times, each filtered with a range of possible values. The elements of the dictionary are weighted by the infinity norm of the corresponding column and further preconditioned to provide wavelets of different values and different propagation times equal probability to entry into the solution space. In phase 2, we derive analytic solutions for estimates of reflectivity and Q and solve an over-determined equation to obtain the final reflectivity series and Q values, where both the amplitude and phase information are utilized to estimate the Q values. The evaluated inversion-based Q estimation method handles the wave-interference effects better than conventional spectral-ratio-based methods. For Q-compensation, we investigate why sparsity promoting does matter. Numerical and field data experiments indicate the feasibility of the evaluated method of Q-compensation without knowing Q but with wavelet given.  相似文献   

细粒含量对冰碛土抗剪强度影响的实验研究   总被引：1，自引：1，他引：0  

蒋德旺  崔鹏  王姣  李战鲁  殷延洲  雷鸣宇 《冰川冻土》2019,41(1):129-139

为研究细粒组颗粒在浸水/降雨条件下产生迁移对冰碛土抗剪强度的影响，从西藏林芝市帕隆藏布嘎隆寺沟流域内采取冰碛土样品，进行7种不同细粒（粒径小于2 mm）含量情况下的冰碛土比重和相对密度的测试，开展了不同围压下的大型饱和固结不排水三轴（CU）实验。结果表明：（1）细粒含量对干密度的影响较小，不同细粒含量的冰碛土孔隙比大致相同，细颗粒对孔隙具有改造作用，不同细粒含量会引起孔隙结构的差异，从而导致冰碛土的结构差异，最终导致抗剪强度的不同；（2）细颗粒迁移导致冰碛堤坡脚和内部一定深度细粒含量较高，达到一定的量值时，抗剪强度明显降低，冰碛堤容易发生剪切破坏；（3）细粒含量对抗剪强度参数的变化具有重要影响，从中还可以反映出冰碛土结构控制的变化：粗颗粒控制→粗细颗粒共同控制→细颗粒控制。研究结果对于评价冰碛堤的稳定性具有重要意义。  相似文献   

冰川区不同气温估算方法评估 ——以藏东南帕隆4号冰川为例     

赵传熙  杨威  朱美林  王永杰 《冰川冻土》2019,41(6):1281-1291

冰川作为地表特殊的下垫面，冰川区内气温明显低于同高度非冰川区大气温度。如何利用低海拔非冰川区观测资料精确估算高海拔冰川区气温，直接关系着青藏高原冰川消融估算及其水文效应的评估。利用架设在藏东南帕隆藏布4号冰川不同高度带的四台自动气象站资料，分析了冰川区与非冰川区气温的波动特征，评估了迄今为止通用的线性递推模型（DT模型）、分段拟合模型（SM模型）和简化热力学模型（GB模型）三种方法在藏东南冰川区气温估算方面的应用效果。对比研究发现：SM模型在帕隆4号冰川上的模拟效果最为理想且操作相对简单；传统DT模型在消融区存在严重的高估，帕隆4号冰川表面夏季（6-8月）正积温的高估比例接近39%；GB模型由于受到诸如冰川风边界层厚度等不确定性的影响，降低了大范围温度估算的可操作性。  相似文献   

基于散射计风场数据的台风强度诊断方法——以海洋二号卫星数据为例   总被引：2，自引：2，他引：0  

杨典  宋清涛  蒋兴伟  刘宇昕  刘圆 《海洋学报》2019,41(1):151-159

本文提出了一种基于散射计风场数据的台风定强方法。该方法定义了台风所在海区的一个圆形区域上的风速平均值来对台风进行定强，并使用海洋二号卫星散射计历史数据对该方法进行了验证。验证结果表明，该台风定强方法可以克服散射计反演台风风速过低的问题。与台风强度监测的历史记录比较的结果显示，在台风强度低于"强台风"这一强度等级时，该方法能够有效估算台风强度。  相似文献   

一种基于M估计的Robust-ELM滑坡位移预测方法     

高彩云  高宁 《大地测量与地球动力学》2019,39(2):153-157

采用传统ELM算法进行滑坡位移预测时，其网络输出权值由最小二乘估计得出，导致ELM抗差能力较差，从而造成网络训练参数不准确。为此，将M估计与ELM相结合，提出一种基于M估计的Robust-ELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值，以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例，将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明，该方法能够有效降低粗差对预测的影响，具有良好的抗差能力。  相似文献   

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers     

J. Ignacio López-Moreno  Leena Leppänen  Bartłomiej Luks  Ladislav Holko  Ghislain Picard  Alba Sanmiguel-Vallelado  Esteban Alonso-González  David C. Finger  Ali N. Arslan  Katalin Gillemot  Aynur Sensoy  Arda Sorman  M. Cansaran Ertaş  Steven R. Fassnacht  Charles Fierz  Christoph Marty 《水文研究》2020,34(14):3120-3133

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.  相似文献   

基于AUKF的框架结构离线模型更新混合试验方法     

陈凡  郭玉荣 《地震工程与工程振动》2020,(1):162-170

模型更新混合试验在传统混合试验方法的基础上更新与试验构件具有相同恢复力特性的构件,扩展了混合试验方法的应用范围。本文旨在提高模型更新混合试验的精度,降低试验的成本并简化模型更新混合试验方法的流程。自适应UKF(AUKF)算法在传统UKF的基础上加入方差自适应模块,能够减轻初始参数设定对参数识别结果的影响,本文基于AUKF提出一种模型更新混合试验方法。对以Bouc-Wen为恢复力模型的防屈曲约束支撑(BRB)进行低周反复加载虚拟试验,通过Matlab编制AUKF算法程序进行参数识别,验证了AUKF算法的高效准确性。对一榀8层4跨带BRB的钢框架进行混合试验数值仿真,结果表明离线模型更新试验结果较在线模型更新更接近真实结果,且简化了试验流程。  相似文献   

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing     

《International Journal of Applied Earth Observation and Geoinformation》2020

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.  相似文献