Indicator Simulation Accounting for Multiple-Point Statistics

Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine.     

Transformation of Residuals to Avoid Artifacts in Geostatistical Modelling with a Trend

Trend modelling is an important part of natural resource characterization. A common approach to account for a variable with a trend is to decompose it into a relatively smoothly varying trend and a more variable residual component. Then, the residuals are stochastically modelled independent of the trend. This decomposition can result in values outside the plausible range of variability, such as grades below zero or ratios that exceed 1.0. We transform the residuals conditional to the trend component to explicitly remove these complex features prior to geostatistical modelling. Back transformation of the modelled residual values allows the complex relations to be reproduced. A petroleum-related application shows the robustness of the proposed transformation. Furthermore, a mining application shows that when this conditional transformation is applied to the original variable, instead of the residual, simulated values are assured to be nonnegative.     

鹅湖岩体岩石谱系单位的建立及其就位机制分析

鹅湖岩体为S型花岗岩 ,具有结构和成分演化序列。运用岩石结构序列法 ,将其划分为 5个单元 ,归并为 1个超单元。按侵入体空间展布形式、岩体地质学、岩石学、构造等资料 ,探讨了岩体就位机制     

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Thermal impact of typical high‐density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and cool‐water streams. This is the first study of its kind that provides simple easy‐to‐use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009–2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90–0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.     

基于等价权抗差估计和线性规划的天线曲面拟合算法

现有天线曲面拟合算法均基于最小二乘法,难以消除多个粗差对拟合结果的影响。本文基于等价权抗差估计思想,利用线性规划计算残差初值,再进行选权迭代计算。通过算例,证明本文方法能较好地探查多个粗差,且计算结果精度更高。     

局部区域模型重力异常快速算法

地球重力场位系数模型可以用于计算局部重力扰动场元。然而随着地球重力场模型阶次的提高、局域重力场计算范围的增大,其计算速度往往不能满足工程需求。针对这一问题,在对位系数模型泰勒级数展开的基础上提出了采用向量运算、混合编程的方法,同时对连带勒让德函数Belikov递推方法中与经纬度无关的量进行了预先计算,有效提高了计算速度。提出的方法对于利用超高阶次重力场模型快速解算大范围、高分辨率重力场元数据以及累加求和计算具有一定的参考与借鉴意义。     

BDS／GPS 组合 RTK 定位性能分析

针对单系统RTK存在可见卫星数少等问题,文中研究BDS/GPS站间单差的RTK算法模型,该模型采用二次型函数部分最小化及LAMBDA方法联合搜索模糊度。利用该模型分析BDS/GPS组合RTK的定位性能,通过短基线实测数据分析表明:站间单差RTK模型与双差模型是等价的;BDS/GPS组合系统相比于单一系统,明显提高定位的稳健性和精度,改善模糊度固定的成功率。     

耦合长期预报的跨流域引水受水水库调度模型

为提高跨流域引水工程受水水库引水有效性,研究了耦合长期径流预报信息的跨流域引水受水水库调度模型。首先选取汛期径流预报信息,采用径流预报概率修正先验概率来描述径流的不确定性,建立了贝叶斯随机动态规划模型(BSDP-LTF)。然后将模型应用于碧流河水库,并与仅考虑径流相关的随机动态规划模型(SDP-I)、仅考虑长期预报信息的随机动态规划模型(SDP-LTF)进行比较。比较结果得出在供水保证率基本一致且不增加调度风险的情况下,BSDP-LTF模型相比SDP-I、SDP-LTF模型,可分别减少引水8.2%、4.1%。表明贝叶斯随机动态规划模型BSDP-LTF有效改进了径流描述,提高了跨流域引水的有效性。     

Copper Speciation in a Collection of Geochemical Reference Materials Using Sequential Extraction and Evaluation of the Validity Using XANES Spectroscopy

Copper speciation in a collection of Japanese geochemical reference materials (JSO‐1, JLk‐1, JSd‐1, ‐2, ‐3 and ‐4, JMs‐1 and JMs‐2) was achieved by sequential extraction and characterised using X‐ray absorption near‐edge structure spectroscopy. In the first step of the extraction, referred to as the acid fraction, between 1% and 20% total Cu within the reference materials was extracted. Such a result is typically accounted for by absorption of Cu onto clay minerals. However, the presence of Cu sulfate (an oxidation product of chalcopyrite) was observed in some of the stream sediments affected by mining activity (JSd‐2 and JSd‐3) instead. Copper was extracted in the reducible fraction (targeting Fe hydroxide and Mn oxide) (2–49% total Cu). Between 2% and 51% Cu was extracted in the oxidised fraction (targeting sulfides and organic matter). X‐ray absorption near‐edge structure spectroscopy clarified that the reducible fraction consisted of Cu bound to Fe hydroxide, whereas the oxidised fraction was a mixture of Cu bound to humic acid (HA) and Cu sulfide. In the oxidisable fraction, chalcopyrite was the predominant species identified in JSd‐2, and Cu bound to HA was the major species identified in JSO‐1 (a soil sample).     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.