Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling

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.     

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

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.     

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

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.     

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

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.     

A parallel algorithm for coverage optimization on multi-core architectures

Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.     

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

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.     

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

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.     

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

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.     

Bitumen Prices and Structural Changes in North American Crude Oil Markets

In an earlier report, changes in bitumen prices at Hardesty, Alberta, Canada, were modeled as the responses to changes in monthly prices of Hardesty light/medium crude oil for the period 2000–2006 with a simple error correction econometric model. This note re-examines that price relationship for the period 2009–2014. Over the period 2006–2014, there was also rapid growth in North American light oil production from low-permeability carbonate, sandstone, and shale reservoirs. During that period, Canadian raw bitumen production grew by more than 12% per year and there was significant geographical diversification in its markets. Results of the statistical analysis showed that the change in the dynamic relationships between bitumen prices and Hardesty light oil prices probably reflected, in part, the maturation of bitumen markets and closer integration with North American light oil markets. The analysis also examines the dynamic relationships between bitumen prices and West Texas Intermediate and Brent international benchmark crude oil prices. Ideally, if bitumen prices are found to be closely related to a widely traded benchmark crude oil, the benchmark crude oil price forecasts could be used as a basis for predicting bitumen prices. However, neither of international benchmark crude oils tested had high explanatory power.