共查询到20条相似文献，搜索用时 31 毫秒
1.
Application of geostatistics in estimating recoverable reserves of beach sand deposit is rare. This paper made an attempt to estimate local recoverable reserves using disjunctive kriging and discrete Gaussian model considering support and information effects for a beach sand deposit located in the eastern part of India. The dependence of different selective mining unit (SMU) sizes and different production sampling strategies on the estimated tonnage, metal quantity, and the ore tonnage versus metal quantity relationships has been examined. The results of the study show that nonlinear geostatistics should be used for more precise assessment of the grade, ore tonnage, and metal quantity and their relationships, which are necessary for recoverable reserve estimation. In selective mining operation, both support and information effects have significant influence on recoverable reserve. Recoverable reserve estimation based on SMU involves estimating grade distributions of mining unit with much bigger support than the available drill core sample data. Information effect comes into picture from the real scenario where the actual grades of the blocks remain unknown even during mining. At the mining stage, discrimination of ore and waste blocks is carried out based on estimated grades of the production samples and it is likely that the blocks might be misclassified as either ore or waste and thus sent to wrong destination. Information effect modeling makes the estimation more reliable by taking care of misclassification. 相似文献
2.
Jennifer Miller 《The Professional geographer》2005,57(2):169-184
Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over‐ and underpredicted observations based on local factors. 相似文献
3.
Mineral resource evaluation requires defining grade domains of an ore deposit. Common practice in mineral resource estimation
consists of partitioning the ore body into several grade domains before the geostatistical modeling and estimation at unsampled
locations. Many ore deposits are made up of different mineralogical ensembles such as oxide and sulfide zone: being able to
model the spatial layout of the different grades is vital to good mine planning and management. This study addresses the application
of the plurigaussian simulation to Sivas (Turkey) gold deposits for constructing grade domain models that reproduce the contacts
between different grade domains in accordance with geologist’s interpretation. The method is based on the relationship between
indicator variables from grade distributions on the Gaussian random functions chosen to represent them. Geological knowledge
is incorporated into the model by the definition of the indicator variables, their truncation strategy, and the grade domain
proportions. The advantages of the plurigaussian simulation are exhibited through the case study. The results indicated that
the processes are seen to respect reproducing complex geometrical grades of an ore deposit by means of simulating several
grade domains with different spatial structure and taking into account their global proportions. The proposed proportion model
proves as simple to use in resource estimation, to account for spatial variations of the grade characteristics and their distribution
across the studied area, and for the uncertainty in the grade domain proportions. The simulated models can also be incorporated
into mine planning and scheduling. 相似文献
4.
Spatial uncertainty analysis is a complex and difficult task for orebody estimation in the mining industry. Conventional models (kriging and its variants) with variogram-based statistics fail to capture the spatial complexity of an orebody. Due to this, the grade and tonnage are incorrectly estimated resulting in inaccurate mine plans, which lead to costly financial decision. Multiple-point geostatistical simulation model can overcome the limitations of the conventional two-point spatial models. In this study, a multiple-point geostatistical method, namely SNESIM, was applied to generate multiple equiprobable orebody models for a copper deposit in Africa, and it helped to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by sequential Gaussian simulation within each equiprobable orebody models. The results were validated by reproducing the marginal distribution and two- and three-point statistics. The results show that deviations of volume of the simulated orebody models vary from ? 3 to 5% compared to the training image. The grade simulation results demonstrated that the average grades from the different simulation are varied from 3.77 to 4.92% and average grade 4.33%. The results also show that the volume and grade uncertainty model overestimates the orebody volume as compared to the conventional orebody. This study demonstrates that incorporating grade and volume uncertainty leads to significant changes in resource estimates. 相似文献
5.
Huang Jixian Mao Xiancheng Chen Jin Deng Hao Dick Jeffrey M. Liu Zhankun 《Natural Resources Research》2020,29(1):439-458
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
相似文献6.
马关都龙曼家寨锡锌多金属矿床经济评价研究 总被引：1，自引：0，他引：1
在对马关都龙曼家寨锡锌多金属矿床地质特征进行分析、研究的基础上,应用地质统计学的理论和方法,运用大型矿业专用软件Surpac建立了该矿体的原始资料数据库和三维数学模型.根据不同边界品位多方案圈定矿体,分别计算矿体的盈亏平衡品位、平均品位和综合品位.通过对比以平衡品位和综合品位圈定的矿体块体模型,对该矿床进行技术经济评价研究,得出了有利于矿产资源综合利用、减少资源浪费的以综合品位圈定矿体的结论. 相似文献
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GWR模型在土壤重金属高光谱预测中的应用 总被引：5，自引：0，他引：5
目前土壤重金属高光谱反演模型大多忽视了重金属与光谱变量间相关关系的空间异质性,这与实际情况不相吻合,而地理权重回归（GWR）模型能有效地揭示变量间关系的空间异质性。本文以福州市土壤重金属Cd、Cu、Pb、Cr、Zn、Ni为对象,构建土壤重金属预测的GWR高光谱模型,并将预测结果与普通最小二乘法回归（OLS）结果进行比较分析,探讨GWR模型在土壤重金属高光谱预测中的适用性及局限性。结果表明：① GWR模型在土壤重金属高光谱预测中适用与否取决于重金属对光谱变量影响的空间异质性程度：对于Cr、Cu、Zn、Pb等对光谱变量影响空间异质性大的元素,其GWR预测精度较OLS提高明显,表现为GWR模型的调节R2较OLS模型有了明显提高,分别为OLS模型的2.69倍、2.01倍、1.87倍和1.53倍;而AIC值以及残差平方和较OLS模型却明显降低,AIC值减少量均大于3个单位,残差平方和则仅分别为OLS模型的25.33%、30.09%、47.22%和86.84%;对于Cd和Ni等对光谱变量影响空间异质性小的元素,相较于OLS模型,GWR模型的调节R2分别提高了0.015和0.007,残差平方和分别减少了5.97%和4.18%,但AIC值却分别增加了2.737和2.762,GWR预测效果改善不明显;② 光谱变换可以有效增强土壤重金属的光谱特征,其中以光谱的倒数变换效果最好,而且该变换及其微分形式可以很好地提高模型的预测效果;③ GWR模型的应用前提是变量间关系的空间非平稳性,适合在与土壤光谱变量间关系具有显著空间异质性的重金属高光谱预测中推广。 相似文献
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空间数据挖掘的地理案例推理方法及试验 总被引：2，自引：0，他引：2
从空间数据挖掘的角度谈地理案例推理方法,认为地理案例推理是面向问题的一种空间数据挖掘方法。针对这一思想进行了基于地理案例的空间数据挖掘具体算法介绍。首先在明确地理案例具体定义的基础上,给出了面向问题的空间数据挖掘地理案例界定和组织方法;其次,鉴于地理空间的自然地带性和区域分异性规律的影响,深入探讨了地理案例自身或其间所可能存在的相互依赖和相互制约关系,并给出了采用粗糙集方法进行地理案例内蕴空间关系的定量挖掘方法;第三,针对地理案例表达时考虑的空间特征和空间关系的不同,给出了三种状况下的空间相似性计算模型;最后,以土地利用这一典型的地学现象为例,给出具体实例,一方面进行土地利用问题的定量分析与推测;另一方面,通过实例展示地理案例推理方法在地学问题求解以及空间数据定量分析上的特点和优势。 相似文献
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Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects 总被引：1，自引：0，他引：1
Wenhao Yu Tinghua Ai Yakun He Shiwei Shao 《International journal of geographical information science》2017,31(2):280-296
The aim of mining spatial co-location patterns is to find the corresponding subsets of spatial features that have strong spatial correlation in the real world. This is an important technology for the extraction and comprehension of implicit knowledge in large spatial databases. However, existing methods of co-location mining consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. Furthermore, previous works do not take the ‘distance decay effect’ of spatial interactions into account, which may reduce the effectiveness of the result. Here we propose an improved spatial co-location pattern mining method, including the network-constrained neighborhood and addition of a distance-decay function, to find the spatial dependence between network phenomena (e.g. urban facilities). The underlying idea is to utilize a model function in the interest measure calculation to weight the contribution of a co-location to the overall interest measure instance inversely proportional to the separation distance. Our approach was evaluated through extensive experiments using facility points-of-interest data sets. The results show that the network-constrained approach is a more effective method than the traditional one in network-structured space. The proposed approach can also be applied to other human activities (e.g. traffic accidents) constrained by a street network. 相似文献
13.
Dig-limit optimization is an operational decision making problem that significantly affects the value of open-pit mining operations. Traditionally, dig-limits have been drawn by hand and can be defined as classifying practical ore and waste boundaries suiting equipment sizes in a bench. In this paper, an optimization approach based on a genetic algorithm (GA) was developed to approximate optimal dig-limits on a bench, given grade control data, equipment constraints, processing, and mining costs. A case study was conducted on a sample disseminated nickel bench, in a two destination and single ore-type deposit. The results from using the GA are compared to hand-drawn results. The study shows that GA-based approach can be effectively used for dig-limit optimization. 相似文献
14.
Dr Helen Mounsey 《International journal of geographical information science》2013,27(3):259-261
Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail. 相似文献
15.
The Athabasca oil sands deposit, Alberta, Canada, is one of the largest known hydrocarbon accumulations. The efficient exploitation of this deposit, as well as other oil sand accumulations throughout the world, is based onin situ recovery and surface mining methods. Quantitative modeling of deposit heterogeneity provides a valuable engineering tool. In the present study, conditional simulation was used to model oil-saturated zones in part of the Athabasca deposit. This technique generates equiprobable models of thein situ variability of essential deposit attributes that honor the available data and their spatial statistics. The application of the technique is based on the delineation of geologically homogeneous zones within the host McMurray Formation, their statistical validity, and the integration of geological interpretations. The geological framework is developed, and subsequently, high resolution conditionally simulated models of three identified hydrocarbon-bearing zones are generated, in terms of the zone boundaries and the percent weight of oil saturation. These models serve as “what-if” tools for risk assessment and future planning. 相似文献
16.
Given the uncertainty in grade at a mine location, a financially risk-averse decision-maker may prefer to incorporate this uncertainty into the ore selection process. A FORTRAN program risksel is presented to calculate local risk-adjusted optimal ore selections using a negative exponential utility function and three dominance models: mean-variance, mean-downside risk, and stochastic dominance. All four methods are demonstrated in a grade control environment. In the case study, optimal selections range with the magnitude of financial risk that a decision-maker is prepared to accept. Except for the stochastic dominance method, the risk models reassign material from higher cost to lower cost processing options as the aversion to financial risk increases. The stochastic dominance model usually was unable to determine the optimal local selection. 相似文献
17.
Frits Agterberg 《Natural Resources Research》2018,27(4):405-417
Earlier methods of fitting Pareto–lognormal distributions to large samples of worldwide metal deposit size data are improved by using a sliding window method for estimating upper-tail Pareto coefficients and constructing best-fitting lognormal Q–Q plots with their corresponding probability-density curves. Lower-tail Pareto distributions are fitted to some extent as well. Copper and Zn deposits of the world are taken as example in this paper. Three principal statistical laws resulting in the basic lognormal with two Pareto tails are thought to underlie the generation of Pareto–lognormals for amounts of metal in primarily hydrothermal ore deposits. Historical trends in mining and exploration are thought to create an excess of smaller deposits with respect to the basic lognormal that decreases steadily with increasing deposit size until it changes into a deficit slightly before median size is reached. This deficit decreases for the largest metal deposit sizes for which the upper-tail Pareto and extrapolated basic lognormal show similar size frequencies again. The Pareto–lognormal model can also be used to describe metal size-frequency distributions for smaller geographically coherent regions on the continents. A new version of the original model of de Wijs is considered to help explain why regional Pareto–lognormal distributions with lesser logarithmic variances and Pareto coefficients can be combined to form worldwide size-frequency distributions of the same type. 相似文献
18.
Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential,Baguio District,Philippines 总被引：2，自引：0，他引：2
An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the unknown gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere. 相似文献
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