基于三维地质模型的河南韩梁铝土矿成矿分析及资源量估算

依据河南省鲁山—宝丰韩梁地区铝土矿的地质特征,利用Surpac 6.3矿业软件建立了该矿区的地质数据库及地层、矿体、断层和地表模型,形象表达了矿区的地形地貌和矿体的产状及空间关系。推断出矿床形成的环境和成因,应用块体模型和距离幂次反比法对矿体的品位分布和资源量进行了动态估算,表明块体估算资源量的方法可靠准确,提高了储量估算的效率,为勘查工作提供了可靠的参考和科学依据。     

基于SD法估算东安金矿床5号矿体资源储量

资源储量估算是矿产地质勘查工作中的一项重要任务,是估算矿床经济价值和矿山设计的基本依据。SD资源储量估算和审定方法（SD法）,诞生于20世纪80年代,它是一种以SD动态分维几何学为理论,以最佳结构地质变量为基础的资源储量估算方法,适于不同矿种及矿产勘查开采各个阶段,它应用SD精度法,成功量化矿产资源储量的地质可靠程度,...     

Interpretation of variability of rock mass rating by geostatistical analysis: a case study in Western Turkey

Geotechnical interpretation of drillholes for rock mass characterization is commonly based on the engineering judgment. This might result in misrepresentation of the rock mass at unsampled locations. This paper investigates the reproducibility of the spatial variability of rock mass quality (rock mass rating, RMR) using geostatistics. Geotechnical data from an exploratory phase mining project in Western Turkey that covers 2.7 km depth with a total of 14 drillholes composed of 700 samples was used to perform a geostatistical analysis. RMR values of positions, where geotechnical logging was missing, were estimated by applying inverse distance weighting (IDW) and the ordinary kriging (OK) methods. The validation process showed that the OK method is consistently estimating the RMR values better than IDW. The spatial variability of RMR values by geostatistical analysis could provide valuable information in the design and development of underground or surface structures.     

Some limitations in the geostatistical evaluation of ore deposits

Conclusions The foregoing discussion indicates that geostatistical estimation of ore deposits is not local; it is not objective; it is not sensitive to local data trends; and it is not unrestrained by the range of data values.Kriging, as an interpolation method, is a variant of IDW least squares linear fit. As such, it suffers from the limitations of all IDW linear interpolation methods that employ only data values.The estimation variance, currently used to calculate the confidence limits of values for individual mining blocks, is hypothetical and globally derived. It is more closely related to sampling density than to local variation in the data set.Geostatistical methods, of course, have a real place in ore deposit assessment, e.g. global, comparative evaluation to assist decisions on development and investment. What is questioned here is the validity of employing a global method to assess detail (mining blocks) within an ore deposit.     

Investigation of general regression neural network architecture for grade estimation of an Indian iron ore deposit

The mineral resource estimation requires accurate prediction of the grade at location from limited borehole information. It plays the dominant role in the decision-making process for investment and development of various mining projects and hence become an important and crucial stage. This paper evaluvates the use of two distinct artificial neural network (ANN)-based models, general regression neural network (GRNN) and multilayer perceptron neural network (MLP NN), to improve the grade estimation from Koira iron ore region in Sundargarh district, Odisha. ANN-based models capture the inherent complex structure of mineral deposits and provide a reliable generalization of the iron grade. The ANN-based approach does not require any preliminary geological study and is free from any statistical assumption on the raw data before its application. The GRNN is a one-pass learning algorithm and does not require any iterative procedure for training less complex structure and requires only one learning parameter for optimization. In this investigation, the spatial coordinates and multiple lithological units were taken as input variables and the iron grade was taken as the output variable. The comparative analysis of these models has been carried out and the results obtained were validated with traditional geostatistical method ordinary kriging (OK). The GRNN model outperforms the other methods, i.e. MLP and OK, with respect to generalization and predictability of the grades at an un-sampled location.     

3D Positional and Quantitative Prediction of the Xiaoqinling Gold Ore Belt in Tongguan,Shaanxi, China

Quantitative prediction of deep orebody based on 3D visualization technology is of great significance in mineral exploration. Based on the 2D traditional quantitative predicting method, the geoanomaly theory and the mineral exploration model idea, we constructed 3D models of the topography, strata, structure, magmatite and prospecting engineering of the study area using the commercial 3D modeling software Micromine, delineated eight prospective areas and estimated the gold resources amount with methods of Abundance Estimation and Volume Estimation. Then, we compared and counted the known ore blocks and the predicted blocks, which quantitatively explains this prediction’s validity. The results show that Xiaoqinling gold belt in Tongguan has convincing potential for gold development and utilization and the prediction method based on 3D visualization technology proves to be effective.     

IDW矿石品位估值结果的不确定性评价

作为一种常用的矿石品位估值方法, 距离幂次反比(inverse distance weighting, 简称IDW)计算结果的可信度难以评估.对此, 提出了一种基于统计学理论的解决途径. 将参与IDW估值的样品品位视为指示化阈值, 推导得出估值过程中其分配到的权重, 即为待估点取这样品值的概率. 基于这一结论构建出IDW估计值对应的条件累计分布函数(conditional cumulative distribution function, 简称CCDF), 从而完成待估位置矿石品位不确定性的建模. 以一典型矿区的实际矿体勘探数据为基础进行了对比测试. 针对每个估值点的CCDF进行了中位数、期望、方差等信息的提取, 验证表明这一方法的计算结果与样品数据的实际情况之间存在较好的吻合度. 在一定程度上说明了它在IDW品位估值结果不确定性评价方面的有效性.      

Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland

It is challenging to perform spatial geochemical modelling due to the spatial heterogeneity features of geochemical variables. Meanwhile, high quality geochemical maps are needed for better environmental management. Soil organic C (SOC) distribution maps are required for improvements in soil management and for the estimation of C stocks at regional scales. This study investigates the use of a geographically weighted regression (GWR) method for the spatial modelling of SOC in Ireland. A total of 1310 samples of SOC data were extracted from the National Soil Database of Ireland. Environmental factors of rainfall, land cover and soil type were investigated and included as the independent variables to establish the GWR model. The GWR provided comparable and reasonable results with the other chosen methods of ordinary kriging (OK), inverse distance weighted (IDW) and multiple linear regression (MLR). The SOC map produced using the GWR model showed clear spatial patterns influenced by environmental factors and the smoothing effect of spatial interpolation was reduced. This study has demonstrated that GWR provides a promising method for spatial geochemical modelling of SOC and potentially other geochemical parameters.     

Comparison of regression-based and combined versions of Inverse Distance Weighted methods for spatial interpolation of daily mean temperature data

This paper focuses on the performance of two regression-based and one Inverse Distance Weighted (IDW) and two combined versions of IDW methods for interpolation of daily mean temperature at the Black Sea Region of Turkey. Simple linear regression (SLR) and multiple linear regression (MLR) are used as regression-based methods. Combinations of IDW with TLR (temperature lapse rate) and gradient plus inverse distance squared (GIDS) are used as combined versions of IDW. This study targets to compare five spatial interpolation methods based on RMSE (root-mean-square error) statistics of interpolation errors for daily mean temperatures from 1981 to 2012. In order to compare the interpolation errors of the five methods, the leave-one-out cross-validation method was applied over long periods of 32 years on 52 different sites. The algorithms of the five interpolation methods’ codes were written in MATLAB by the authors of the paper.     

Normal and lognormal estimation

A comprehensive theoretical study of the problem of estimation of regionalized variables with normal or lognormal distribution is presented. Unbiased linear estimators are derived, under both assumptions that the population mean is known and unknown, and their error variance is calculated. The minimum variance kriging estimators are studied in more detail and are compared with the conditional expectations. The emphasis is on the study of lognormally distributed variates. The derived mathematical formulas are applicable to the optimal contouring of sample values with the appropriate distribution, as well as the optimal estimation of blocks of ore in mineral deposits.     

基于稳健有序独立成分分析(ROICA)的矿产预测

独立成分分析（ICA）充分考虑了以往主成份分析（PCA）没考虑到但又非常重要的数据高阶统计特性,但ICA分离后的信号具有顺序、符号、幅度的不确定性,而矿产资源预测又必须弄清分离后信号（地质变量）的地质意义;为此,提出了一种基于稳健有序独立成分分析(ROICA)的矿产预测方法。首先对地质数据进行稳健预处理,然后进行独立成分分析,借鉴因子分析的思想解决独立成分分析算法不确定性的局限,从而可以反映分离变量的有序性。将ROICA方法用于广东省肇庆双壬铜矿区进行矿产资源预测。实验结果表明：该区金银矿体的赋存空间位置与传统均值方差方法给出的异常位置明显不对应,没有指示意义;而经过稳健有序ICA处理后的Au、Ag独立成分异常与实际的Au、Ag矿体吻合度很高;Cu、Pb、Zn独立成分异常与Au、Ag矿关联性不大,反映研究区的Cu、Pb、Zn等其他元素或组合不具有Au、Ag矿的指示意义。ROICA方法可用于矿产资源预测。     

Incorporation of principal component analysis,geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district,NW Iran

Delineation of mineralization-related geochemical anomalies of stream sediment data is an essential stage in regional geochemical exploration. In this study, principal component analysis (PCA) was applied to 12 selected elements to acquire a multi-element geochemical signature associated with Cu-Au mineralization in Feizabad district, NE Iran. The spatial distribution of enhanced multi-element geochemical signature of the second component (PC2) was modeled by different geostatistical procedures including variogram calculation, ordinary kriging (OK) and inverse distance weighting (IDW) interpolation techniques. Concentration-area (C-A) fractal and U-spatial statistics models were then applied to the continuous-value interpolated models for delineation of geochemical anomalies. Quantitative comparison of results based on the known mineral occurrences in the study area was carried out using normalized density index and success-rate curves. All generated models represent a high positive relation with known Cu (±Au) deposits in the study area, although, comparison of the results revealed that the OK-based U-spatial statistics model was superior to the rest of models. Besides, the low, moderate and high-intensity anomalies are spatially associated with geological-structural features in the study area.     

3D矿床建模技术在数字矿产勘查中的应用

为弥补现有数字矿床建模技术在地质矿产勘查处理和应用中的不足,从原始勘查数据建库及标准化、多指标单工程矿体自动圈定、基于语义识别的剖面矿体的连接与外推、矿体表面和品位建模、基于剖面矿体线框模型构建矿体表面模型及基于TIN+Octree数据结构和地质统计学理论建立矿体的空间属性模型等5个方面总结提出一套面向地质矿产勘查业务处理的矿床建模流程和总体技术解决方案.提高了地质矿产勘查研究精度,为进一步的矿山开采提供可靠的数据模型.      

矿产资源量估计法在金矿中的应用

大比例尺矿床统计预测中，矿产资源量的估计是矿体定位预测的前提，其中精度最高的方法是体积估计法。文章借助实例分析，对体积估计法的计算程度进行了阐述。以区间概率解析法对矿脉和矿化体在垂向空间的金品位进行分解，解析出其金品位统计分布模型；利用波形分布模型对深部盲区进行预测，对各中段的金品位、矿化体体积、矿石在矿化体中的体积分数进行解析和计算；并利用有效探矿指数进行合理的校正，最后得到近似于地质储量的金矿产资源量，为下一步的矿体定位预测提供定量数据。经计算该矿重点预测区（－290m～－810m标高）预测的有效金矿产资源量为70.28t，经部分工程控制，已获D＋E级金储量18t。     

LA-(MC)-ICPMS和(Nano)SIMS硫化物微量元素和硫同位素原位分析与矿床形成的精细过程

硫化物微区原位分析技术包括LA-ICPMS定点微量元素分析、LA-ICPMS和(Nano) SIMS微量元素面扫描分析,以及SIMS、Nano SIMS和LA-MC-ICPMS原位硫同位素点分析和面扫描。这些分析方法可以有效地获取不同期次硫化物微量元素含量、丰度分布图像、硫同位素比值和分布特征,结合微区时间分辨信号谱图、微量元素相关性分析等,在矿床学的成矿元素行为与赋存状态、成矿元素置换反应、成矿流体与硫的来源、矿石矿物的化学分带性、矿床成因模型等研究中有着重要的应用前景,以探讨矿床的精细成矿过程。硫化物原位微量元素和同位素LA-(MC)-ICPMS和(Nano) SIMS分析,需要降低仪器和分析方法的系统误差,克服严重的基体效应和同位素分馏效应。     

基于Micromine软件的固体矿产资源量估算原理与方法

文章阐述了Micromine软件地质统计法估算资源量的原理,叙述了基于该软件的资源量估算过程。采用封闭多边体法对资源量估算结果进行验证,得出Micromine资源量估算具有准确、可信、快速的特点。使用Micromine软件估算矿体资源量,避免了传统的手工计算繁琐冗杂的数据信息等工作。但三维可视化地质建模要基于地质资料的准确以及对地质成矿规律的正确认识,才能确保建模和资源量估算结果的正确、可靠。     

Application of K-means and PCA approaches to estimation of gold grade in Khooni district (central Iran)

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits. To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis (PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60 km northeast of the Anarak city and 270 km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold, arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.     

矿床储量规模分布的分形模型及其分维的数理分析

矿床储量规模预测在矿产勘查和评价中占有十分重要的位置,而其预测的最主要手段是建立矿床储量规模分布的数学模型.根据矿床储量的统计自相似性,应用概率统计方法建立了矿床储量规模的分形模型.同时,通过模型及其分维值的数理分析,得出不同分维值的地学解释,讨论了分维的线性与非线性最小二乘回归估计法.     

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.     

三维矿产资源评价软件MICROMINE在金属矿山资源储量估算中的应用——以吉林舒兰季德钼矿为例

描述了吉林舒兰季德钼矿的矿床地质特征、水文地质、工程地质、环境地质条件及选矿试验结果。以该钼矿为例,系统讨论了三维矿产资源评价软件MICROMINE 11.03版的工作原理及流程,并对该矿床进行了资源量估算和验证。采用距离反比加权法进行资源储量估算,获得矿石量31374.14万t,钼金属量245096t,其中,111b+122b矿石量占总矿石储量的80.99%,钼金属量占总资源储量的81.08%;露天开采设计利用资源储量占总矿石储量的91.64%,钼金属量占总资源储量的91.99%。地质块段法和三角网多边形估算法检验结果表明,用三维矿产资源评价软件MICROMINE中距离反比加权法进行资源量估算,方法合理,相对误差较小,结果可靠。