L1范数与IQR统计量组合的GNSS坐标序列粗差探测算法

GNSS坐标时间序列中不可避免地含有粗差,未剔除的粗差将会导致参数估计有偏。因此,粗差探测与剔除是GNSS坐标序列分析中一项重要的数据预处理工作。针对GNSS坐标时间序列特点,提出了一种将L1范数(L1-norm)估计与四分位距统计量IQR(interquartile range)组合的移动开窗粗差探测算法,称之为L1_Mod IQR。该方法的主要思想是,首先利用L1范数估计得到较"真实"的残差,然后再对残差采用IQR统计量进行粗差探测。将L1_Mod IQR法与"3σ"法、基于最小二乘的τ检验法等粗差探测算法进行了模拟计算与对比,验证了该算法的有效性。进一步采用L1_Mod IQR算法对中国区域10个IGS站的高程时间序列进行了分析,结果表明中国区域IGS站高程序列的粗差剔除率最小为0.1%,最大为2.6%。并且以WUHN站为例与SOPAC提供的结果进行了对比,结果表明SOPAC提供的"Clean"数据仍含有大量的粗差,而L1_Mod IQR算法能够有效地剔除粗差。     

求解GM(1,1)模型新总体最小二乘算法

在GM(1,1)模型中系数矩阵和观测向量都是由原始序列组成的。系数矩阵中同样是有误差的,与观测向量中的误差一样,亦来源于原始序列,即它们误差同源。不同位置的相同元素应该有相同的改正数,采用传统总体最小二乘求解则不能达到此目的。针对这一缺陷,推导了一种新的总体最小二乘算法;并且通过算例验证了新方法的可行性和有效性。     

The Molí del Baró-1 site,a diverse fossil assemblage from the uppermost Maastrichtian of the southern Pyrenees (north-eastern Iberia)

The Molí del Baró-1 site (north-eastern Iberia) has yielded one of the richest fossil assemblages of the continental upper Maastrichtian from western Europe. It includes abundant plant (charophytes, sporomorphs, angiosperm leaves, seeds and logs), invertebrate (mollusc shells, partial insect exoskeletons and eggs) and vertebrate (teeth, bones, eggshells) fossils. The fossil assemblage is composed of both parautochthonous and allochthonous remains that mostly deposited in a pool formed at the edge of a meandering river. Plant megafossil evidence suggest that at least three angiosperm species (a willow-like dicot, palms and a likely typhacean monocot) formed the plant community growing in this fluvial environment. Tooth remains indicate that crocodylomorphs were diverse (including atoposaurids, ziphosuchians and eusuchians) which together with small theropod dinosaurs likely consumed prey carcasses or scavenged near the depositional setting. Prismatoolithid eggshells also suggest the presence of nearby nesting area of theropods. On the other hand, hadrosauroids apparently dominated phytophagous faunas including lambeosaurines as well as more basal forms. The taxonomic composition of the Molí del Baró-1 site matches the expected for the timespan and environment and represents a relevant locality to know the diversity of the latest Cretaceous terrestrial ecosystems in south western Europe.     

An efficient quasi-Newton approximation-based SORM to estimate the reliability of geotechnical problems

The first order reliability method (FORM) is efficient, but it has limited accuracy; the second order reliability method (SORM) provides greater accuracy, but with additional computational effort. In this study, a new method which integrates two quasi-Newton approximation algorithms is proposed to efficiently estimate the second order reliability of geotechnical problems with reasonable accuracy. In particular, the Hasofer–Lind–Rackwitz–Fiessler–Broyden–Fletcher–Goldfarb–Shanno (HLRF–BFGS) algorithm is applied to identify the design point on the limit state function (LSF), and consequently to compute the first order reliability index; whereas the Symmetric Rank-one (SR1) algorithm is nested within the HLRF–BFGS algorithm to compute good approximations, yet with a reduced computational effort, of the Hessian matrix required to compute second order reliabilities. Three typical geotechnical problems are employed to demonstrate the ability of the suggested procedure, and advantages of the proposed approach with respect to conventional alternatives are discussed. Results show that the proposed method is able to achieve the accuracy of conventional SORM, but with a reduced computational cost that is equal to the computational cost of HLRF–BFGS-based FORM.     

青海省祁漫塔格地区1∶5万水系沉积物测量衬值异常圈定实例

青海省祁漫塔格地区位于柴达木盆地西南缘,是青海省重要的铁多金属成矿基地之一。利用88442个野外采样点1∶5万水系沉积物测量数据,采用Geo Expl软件圈定了该地区的衬值异常。衬值是指各元素0.5 km×0.5 km网格化数据与其园域搜索半径5km、移动步长0.5km移动平均值的比值。选用自定义累频"90、95、98、100"确定了异常的外、中、内带。通过常规方法和衬值圈定的异常的对比,表明衬值异常能够削弱不同地质背景差异,使异常形态更为规整,浓集中心更为明显,并且可能发现新异常。     

朝不楞多金属矿外围1∶5万地电化学勘查应用

运用地电化学技术在内蒙古半干旱草原风成砂浅覆盖区朝不楞多金属矿外围开展了1∶50000面积性勘查工作,并与常规土壤测量开展了对比研究。结果表明:(1)从元素异常强度、衬度、规模及与已知矿体空间对应性等方面对比,地电化学测量比常规土壤测量具有显著优越性;(2)地电化学测量在区域上显现出Ag-Pb-Zn-Bi-Cd及Cu-Co-Cr-Fe-La-Mo-Ni两套元素异常组合,与已知见矿钻孔中探测到的矽卡岩型铁矿及热液型铅锌矿均有出现具有较好的对应性;(3)在工区东部及东南部未知区发现多处异常强度、规模较大的多元素组合异常,建议开展进一步的勘查工作,以实现该矿区找矿突破。     

天绘一号03星三线阵/多光谱载荷影像质量评价

2015年10月26号发射的天绘一号03星是中国传输型立体测绘卫星天绘一号系列的第3颗卫星。三线阵和多光谱影像数据是天绘一号卫星的重要数据,用于立体测绘、彩色融合、遥感观测等多种用途。因此,为了推动天绘一号03卫星影像数据早日为相关领域提供服务,本文采用客观评价的方法,对天绘一号03星的三线阵和多光谱影像质量进行了全面的评价分析。评价结果显示(天绘一号)03星的清晰度、对比度、细节能量、边缘能量、功率谱、信息容量指标值远高于天绘一号01、02星。说明天绘一号03星三线阵/多光谱影像在地物细节和边缘纹理特征的描述上优于天绘一号01、02星,同时载荷接收的信息量远高于天绘一号01、02星。天绘一号03星较高的信噪比指标说明其载荷抑制噪声的能力优于天绘一号01、02星。边缘辐射畸变和增益调整畸变表明在辐射均匀程度上,天绘一号03星三线阵/多光谱影像介于天绘一号01和02星之间。因此,与同类天绘一号01,02星的三线阵与多光谱影像相比,天绘一号03星影像质量有着显著的提高。     

K-GM（1，1）模型在岩体变形监测中的应用

传统GM （1,1）模型存在着长期预测效果差、模型精度不高等问题,卡尔曼滤波能够排除建模过程中随机干扰因素,滤波值能够反映更真实的数据情况。为了能更好地提高变形监测的预测精度,基于传统GM （1,1）模型和卡尔曼滤波,提出K‐GM （1,1）模型,利用该模型对岩体变形监测数据进行建模预测,并与传统GM （1,1）模型预测结果进行对比分析,结果表明,K‐GM （1,1）模型具有较高的预测精度,可作为变形监测的一种新方法。     

改进后灰色神经网络及短期基坑沉降预测研究

在短期基坑沉降监测中,由于数据量少且呈非线性变化,沉降模型很难准确建立。灰色GM(1,1)对数据少、趋势性强、波动小的数据有较高的预测精度,但不能模拟复杂的非线性函数;BP神经网络可以对非线性数据进行学习训练,具有自学习、自适应能力;通过将GM(1,1)与BP神经网络组合,并优化网络部分的学习率、权值和阈值等,建立一种改进的灰色神经网络模型,该模型具有对非线性数据自学习、自适应能力和预测精度更高等优点。通过某基坑沉降监测分析,验证改进的灰色神经网络模型预测精度更高,适合短期建模,具有很好的实用性。     

太湖地区HJ-1卫星CCD数据反演气溶胶及大气校正

针对HJ-1卫星CCD数据,利用改进的暗像元法反演气溶胶光学厚度(AOD),再利用反演的AOD对其进行大气校正。将反演的气溶胶与地基太阳光度计数据进行对比验证,发现当反演的AOD值大于0.2时,反演值与地基观测值的相关系数为0.964,符合MODIS业务化反演AOD的精度要求。再将反演得到的气溶胶带入6S辐射传输模型中,对HJ-1卫星CCD数据进行大气校正实验。结果表明,该方法能有效提高HJ-1卫星CCD数据大气校正的精度,更好地复原地物的真实光谱信息。