A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation     

Arianne Ford  John M. Miller  Augusto G. Mol 《Natural Resources Research》2016,25(1):19-33

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.  相似文献   

Metallic Mineral Resources in the Twenty-First Century. I. Historical Extraction Trends and Expected Demand     

Alberto E. Patiño Douce 《Natural Resources Research》2016,25(1):71-90

Industrial, technological, and economic developments depend on the availability of metallic raw materials. As a greater fraction of the Earth’s population has become part of developed economies and as developed societies have become more affluent, the demand on metallic mineral resources has increased. Yet metallic minerals are non-renewable natural resources, the supply of which, even if unknown and potentially large, is finite. An analysis of historical extraction trends for eighteen metals, going back to the year 1900, demonstrates that demand of metallic raw materials has increased as a result of both increase in world population and increase in per-capita consumption. These eighteen metals can be arranged into four distinct groups, for each of which it is possible to identify a consistent pattern of per-capita demand as a function of time. These patterns can, in turn, be explained in terms of the industrial and technological applications, and in some cases conventional uses as well, of the metals in each group. Under the assumption that these patterns will continue into the future, and that world population will grow by no more than about 50% by the year 2100, one can estimate the amount of metallic raw materials that will be required to sustain the world’s economy throughout the twenty-first century. From the present until the year 2100, the world can be expected to require about one order of magnitude more metal than the total amount of metal that fueled technological and economic growth between the age of steam and the present day. For most of the metals considered here, this corresponds to 5–10 times the amount of metal contained in proven ore reserves. The two chief driving factors of this expected demand are growth in per-capita consumption and present-day absolute population numbers. World population is already so large that additional population growth makes only a small contribution to the expected future demand of metallic raw materials. It is not known whether or not the amount of metal required to sustain the world’s economy throughout this century exists in exploitable mineral resources. In the accompanying paper, I show that it is nevertheless possible to make statistical inferences about the size distribution of the mineral deposits that will need to be discovered and developed in order to satisfy the expected demand. Those results neither prove nor disprove that the needed resources exist but can be used to improve our understanding of the challenges facing future supply of metallic raw materials.  相似文献   

Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling     

K. Daniel Khan  Clayton V. Deutsch 《Natural Resources Research》2016,25(1):51-70

There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations.  相似文献   

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)     

Emmanuel John M. Carranza  Alice G. Laborte 《Natural Resources Research》2016,25(1):35-50

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.  相似文献   

Managing Depleting Gold Revenues in Mali: An Assessment of Policy Options     

Fousseini Traore  Calvin Djiofack Zebaze 《Natural Resources Research》2016,25(2):183-195

This paper analyzes the impact of medium-term policy options in the context of gold resources depletion in Mali. Using a recursive-dynamic computable general equilibrium model calibrated to a 2006 Malian Social Accounting Matrix, we assess the impact of gold resources depletion in Mali and two policy options: the adoption of the permanent income hypothesis (PIH) and a “borrow and invest” scenario consisting at boosting public investment by 5% points of GDP. The depletion of gold resources in Mali would cause a substantial fall in GDP growth, and lead to unsustainable fiscal path if the government were to keep its current pattern of spending. Adopting either the “borrow and invest” fiscal approach or the PIH is likely to generate higher growth and a more sustainable fiscal framework compared to the status quo.  相似文献   

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran     

Hamid Geranian  Seyed Hassan Tabatabaei  Hooshang H. Asadi  Emmanuel John M. Carranza 《Natural Resources Research》2016,25(2):145-159

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.  相似文献   

Comment on “Metallic Mineral Resources in the Twenty-First Century: I. Historical Extraction Trends and Expected Demand” by Alberto E. Patino Douce,in Natural Resources Research DOI: 10.1007/s11053-015-9266-z     

Donald A. Singer  W. David Menzie 《Natural Resources Research》2016,25(1):91-91

  相似文献   

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study     

Jared L. Deutsch  Kevin Palmer  Clayton V. Deutsch  Jozef Szymanski  Thomas H. Etsell 《Natural Resources Research》2016,25(2):161-181

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.  相似文献   

Environmental Geochemistry and Acid Mine Drainage Evaluation of an Abandoned Coal Waste Pile at the Alborz-Sharghi Coal Washing Plant,NE Iran     

Behshad Jodeiri Shokri  Faramarz Doulati Ardejani  Hamidreza Ramazi 《Natural Resources Research》2016,25(3):347-363

In this paper, an abandoned waste coal pile, which is resulted from Alborz-Sharghi coal washing plant, NE of Iran was mineralogically and geochemically characterized to evaluate pyrite oxidation, acid mine drainage (AMD) generation, and trace element mobility. After digging ten trenches and vertical sampling, a quantitative method including the atomic absorption test, and the quality-based methods including optical study were carried out for determination of pyrite fractions in the waste pile. The geochemical results revealed that the fraction of remaining pyrite increased with depth, indicating that pyrite oxidation is limited to the shallower depths of the pile which were confirmed by variations of sulfate, pH, EC, and carbonate with depth of the pile. To evaluate the trend of trace elements and mineralogical constituents of the waste particles, the samples were analyzed by using XRD, ICP-MS, and ICP-OES methods. The results showed the secondary and neutralizing minerals comprising gypsum have been formed below the oxidation zone. Besides, positive values of net neutralization potential indicated that AMD generation has not taken in the waste pile. In addition, variations of trace elements with depth reveal that Pb and Zn exhibited increasing trends from pile surface toward the bottom sampling trenches while another of them such as Cu and Ni had decreasing trends with increasing depth of the waste pile.  相似文献   

Optimizing Ore–Waste Dig-Limits as Part of Operational Mine Planning Through Genetic Algorithms     

Julian Ramirez Ruiseco  Jacob Williams  Mustafa Kumral 《Natural Resources Research》2016,25(4):473-485

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