Effective resource estimates for primary diamond deposits from the EKATI Diamond Mine™, Canada

Primary economic diamond deposit modelling has rarely been documented in the public domain. This paper presents information collected from significantly diamondiferous kimberlite pipes located near Lac de Gras in the Arctic region of Canada's Northwest Territories. The resource estimation process is widely accepted as a cyclical iteration of data collection and evaluation processes. A resource database is typically assembled from a large inventory of exploration data. These data must be methodically quality checked before accepting the information for interpretive analysis. The foundation of a mineral resource model is based on clear understanding of the geology model along with subsidiary grade, volume, and density models. Defining these models is an iterative process of statistical analyses and interpretation. As a deposit progresses along a path towards development, reducing risk to acceptable levels is critical for identifying and realizing its maximum value.     

Statewide Estimates of Undiscovered Deposits of Gold,Silver, Copper,Lead, and Zinc

Estimates of the number of undiscovered deposits on a statewide basis offer a different perspective on the nation's undiscovered resources of gold, silver, copper, lead, and zinc. Mean estimates of the number of undiscovered deposits statewide were extracted from the estimates of undiscovered deposits nationwide. More than 50 undiscovered deposits are estimated to occur in Alaska, Arizona, Nevada, and Wisconsin. Estimating the number of undiscovered deposits statewide serves as a measure of a state's total remaining mineral resources in known conventional deposit types.     

基于多传感器观测信息抗差估计的自适应融合导航

首先利用抗差估计原理构造了基于观测信息的融合导航解，再利用动力学模型信息进行自适应融合，最后利用模拟算例进行多种方案的计算与比较。     

基于方差分量估计的自适应融合导航

多源传感器观测信息的精度一般不一致,利用方差分量估计理论可以合理地顾及各传感器观测信息在融合导航解中的权比.首先给出基于多源观测信息的融合导航解算模式,进而基于方差分量估计讨论了各传感器观测信息在融合导航解中的合理平衡问题,然后应用自适应因子顾及动力学模型预报信息的贡献,给出了计算过程,并利用模拟数据进行了试算与比较.     

用L-曲线法确定岭估计中的岭参数

运用L 曲线法来确定岭估计中的岭参数 ,推导了用L 曲线法确定岭参数的基本公式 ,在此基础上实现了其算法 ,并用两个算例 ,比较了新方法和以往方法。结果表明 ,L 曲线法不仅容易收敛 ,而且其计算结果的精度较高     

Modelling the spatial and temporal distribution of rainfall: a case study in the wet and dry tropics of North East Australia

Rainfall regimes with strong spatial and temporal variation are characteristic of many coastal regions of north and eastern Australia. In coastal regions of north eastern Australia, regimes vary considerably over short distances. This occurs because of changes in local topography, including the height and orientation of mountain ranges and the direction of the coastline with respect to the prevailing moist south east air stream. Northern Australia experiences a tropical monsoon climate with rainfall occurring predominantly during the summer months. Areas with a closer proximity to the coast typically experience the heavier rainfalls. While networks of rainfall gauges have been established and continuous records are available for most of these stations from the 1890s, their low distribution density relative to the complexity of rainfall pattern they are required to represent means that there remains a poor understanding of the spatial and temporal distribution of rainfall in the wet tropics. An enhanced knowledge of rainfall distribution in both space and time has the potential to deliver significant economic and environmental benefits to managers of natural resources. This paper reports on the application of a technique for estimating mean annual and mean monthly rainfall across the Herbert River catchment of north east Australia's dry and wet tropics. The technique utilises thin plate smoothing splines to incorporate both location and elevation into estimates of rainfall distribution. We demonstrate that the method can be applied successfully at the meso scale and within the domain of routinely available data. As such, the method has broad relevance for decision making.     

On the influence of adequate Weddell Sea characteristics in a large-scale global ocean circulation model

Global ocean circulation models usually lack an adequate consideration of high-latitude processes due to a limited model domain or insufficient resolution. Without the processes in key areas of the global thermohaline circulation, the characteristics and flow of deep and bottom waters cannot be modeled realistically. In this study, a high-resolution (~20 km) ocean model focused on the Weddell Sea sector of the Southern Ocean is combined with a low-resolution (2^° × 2^°) global ocean model applying the state estimation technique. Temperature, salinity, and velocity data on two Weddell Sea sections from the regional model are used as constraints for the large-scale model in addition to satellite altimetry and sea-surface temperatures. The differences between the model with additional constraints and without document that the Weddell Sea circulation exerts significant influence on the course of the Antarctic Circumpolar Current with consequences for Southern Ocean water mass characteristics and the spreading of deep and bottom waters in the South Atlantic. Furthermore, a warming trend in the period 1993–2001 was found in the Weddell Sea and adjacent basins in agreement with float measurements in the upper Southern Ocean. Teleconnections to the North Atlantic are suggested but need further studies to demonstrate their statistical significance.     

探地雷达在岩土工程应用中的进展

总结了探地雷达在仪器设备、数据处理方法技术以及在岩土工程中应用的新进展.在硬件方面,主要以改进天线性能为主要发展方向;在数据处理方面,主要研究提高信号信噪比及目的体定位方法;在应用方面,开展了精确测试方面的研究.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.