GIS中数字化数据误差的分布检验与处理

对ＧＩＳ中手工地图数字化中的数据误差进行了系统分析和各种分布检验,认为数字化数据误差可能服从ｐ≈１．６的ｐ－范分布。在此基础上,探讨了数字化数据误差处理的ｐ－范平差,并与最小二乘平差进行了比较。     

分量质子旋进磁力仪观测误差分析

分析了影响质子旋进磁力仪和分量质子旋进磁力仪观测质量的因素 ,给出了分量质子旋进磁力仪补偿线圈电流允许误差、补偿线圈定向 (特别是线圈磁南北方向 )应满足的关系式和定向允许误差。提出了分量质子旋进磁力仪的使用要点     

多投影间地图投影变换实现的途径与优化

针对多种地图投影间自由变换的需求 ,综合对比地图投影变换的主要方法及其特点 ,该文认为反解变换是解决多种投影间投影变换的最佳途径。以此为基础 ,具体探讨了地图投影变换计算机实现的主要过程 ,讨论了地图投影体系、投影参数、算法优化和误差控制等投影变换计算机实现中的关键问题     

关于地震丛集特征、成因及临界状态的讨论

给出了研究地震临界丛集现象的最新方法,即时间变异诊断方法。讨论了不同类型的地震丛集活动特征和成因,结合中国大陆及邻区M≥ 8. 0大震序列和华北地区自1966年以来M≥6. 0地震序列,研究了丛集状态的自相似性及临界时间分支现象。对发震系统的内部时间给出了定义,并研究了地震丛集的非线性时间结构特征。研究认为: ① 事件的发生意味着系统现存的能量一次跳跃式的释放和状态的一次转换。只有当系统以某种足够随机方式动作时,系统状态才会转换,事件才会发生;② 涨落是系统状态的探测器,在临界状态时系统出现很大的涨落,时间变异系数W= 1。临界丛集是由系统内在随机性决定的,事件发生时间是随机地出现的,未来可能出现不止一次的W= 1的临界点;③ 相继发生的事件(M≥ M₀) 可以反映系统的演化过程和特征,丛集是系统演化过程中的有序性和无序性的综合反映;④ 事件丛集和丛集的事件具有自相似性和分形结构,也具有耗散系统的自组织和映射特点;⑤ 根据时间变异系数,系统内部时间(年龄),时间间隔与所经历时间非线性关系式以及映射和迭代方法可以用来预测未来地震趋势和未来M≥ M₀ 地震发生的可能时间点。     

分量核旋仪偏置电流对磁偏角测量的影响分析

从理论上定量地分析了FHD-1型分量核旋仪的偏置电流对磁偏角观测值的影响,结果表明偏置电流的漂移对磁偏角观测值的影响随偏置电流的漂移量的增大而增大、也随磁偏角观测值的增大而增大,该结果对指导台站提高该仪器磁偏角观测值的精度有一定意义。     

固结问题有限元法时步自适应研究

关于提高有限元法解的精度和稳定性问题已受到不少学者的重视。基于Biot固结理论,为提高有限元法计算的精度和稳定性,研究了固结过程中的时步自适应。采用基本原理推导出时步控制公式,从而全面控制应力和孔隙压力场。研究结果有助于准确模拟应力应变的发展,对粘土心墙土石坝等建筑物的设计及施工有重要意义。     

含有粗差的起始数据对水下地形测量的影响及修正

针对含有粗差的起始数据导致的错误碎部点成果,对粗差产生的途径进行了分析,给出了进行改正计算的必备条件,最后结合实际例子进行了计算验证。结果表明改正计算方法在理论上是严密的,在实际工作中也具有可操作性。     

Revised empirical garnet–biotite–muscovite–plagioclase geobarometer in metapelites

The garnet–biotite–muscovite–plagioclase (GBMP) barometer was empirically revised for P–T conditions of 1–14 kbar and 450–840 °C, using 263 metapelitic rock samples from all over the world. This barometer is based on activity models for garnet, biotite and plagioclase identical to those of the well‐calibrated garnet–biotite thermometer and the garnet–aluminosilicate–plagioclase–quartz (GASP) barometer. The GBMP barometer is less temperature dependent than the GASP barometer and can be applied to either Al₂SiO₅‐absent or Al₂SiO₅‐bearing metapelites. The total error of the GBMP barometer is estimated to be about ±1.2 kbar on considering input temperature error and analytical errors of chemical compositions of the phases involved. The random error of the GBMP barometer is evenly distributed with respect to pressure, temperature and mineral composition. Simultaneous application of the GBMP barometer and the garnet–biotite thermometer identifies the correct stability field for Al₂SiO₅‐bearing metapelites. Application of the GBMP barometer to metapelitic rocks within the same geological terranes or thermal contact aureoles yielded similar pressures within error. A spreadsheet for implementing the proposed GBMP geobarometer is supplied on the journal's website.     

On trend detection

A main obstacle to trend detection in time series occurs when they are autocorrelated. By reducing the effective sample size of a series, autocorrelation leads to decreased trend significance. Numerous recipes attempt to mitigate the effect of autocorrelation, either by adjusting for the reduced effective sample size or by removing the autocorrelated components of a series. This short note deals with the latter, also called prewhitening (PW). It is known that removal of autocorrelation also removes part of the trend, which may affect the signal‐to‐noise ratio. Two popular methods have dealt with this problem, the trend‐free prewhitening (TFPW) and the iterative prewhitening. Although it is generally accepted that both methods reduce the adverse effects of PW on the trend magnitude, corresponding effects on statistical significance have not been clearly stated for TFPW. Using a Monte Carlo approach, it is demonstrated that both methods entail quite different Type‐I error rates. The iterative prewhitening produces rates that are generally close to the nominal significance level. The TFPW, however, shows very high Type‐I error rates with increasing autocorrelation. The corresponding rate of false trend detections is unacceptable for applications, so that published trends based on TFPW need to be reassessed.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.