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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.

Cash flows generated from mining projects are typically highly volatile and significantly influenced by a number of exogenous factors including commodity price as one of the most influential uncertainties. In addition, mining projects are complex and many of their executed investment decisions are irreversible. Therefore, management needs to address this potential risk exposure before making an investment decision. Due to the deterioration and fluctuation of mineral commodity prices for a successful mining project acquisition or development, an important and appropriate investment strategy should include a hedging strategy for reducing potential losses suffered by a company. The discounted cash flow methods, which are commonly used to calculate mining project values, often fail to respond to this identified economic uncertainty and also to incorporate de-risking hedging strategies. Therefore, this study approximates the numerical value or value ranges of a mining project considering the combination of a mean reverting commodity price and hedging strategies using continuous time modeling. A novel time-dependent partial differential equation has been proposed using a continuous time, mean reverting model, and hedging strategy to approximate the mining project value. Application of a new real options valuation technique demonstrated its superiority by providing the advantage of mitigating financial losses and procuring financial gains. In this study, some key results are deferral option and expansion option enhanced the maximum values of the project which are, respectively, 2.51 % and 4.4 % compared to the base case. Furthermore, the country risk has a great impact on project values, as when we considered the country risk premium is zero in our model, the project value increases up to 0.97 %.

Mining is an important activity in Mexico; however, despite its economic benefits, it carries potential environmental risks, including mine spills. On August 6, 2014, ~ 40,000 cubic meters of copper sulfate acid solution was spilled from the Tinajas 1 dam of the Buenavista del Cobre mine in Cananea, Sonora, northwestern Mexico. The solution was directly spilled into the Tinajas creek, which is a tributary of the Sonora River. This event had regional socioeconomic and environmental consequences. That is because the Sonora River provides water for agricultural and livestock activities in the region, as well as to human consumption of products derived from these activities. In an attempt to assess the influence of this spill along the Sonora River, samples of watercress (Nasturtium officinale), an edible aquatic plant known for its ability to uptake metals, were collected along: (a) the 2014 spill route (zones 1 and 2); (b) a zone that experienced a spill in the 1980s (zone 3); and (c) a reference zone (zone 4). The watercress samples were analyzed for concentrations of Cr, Sb, Ba, U, Cu, Cd, Zn, Ni, Fe and Pb to evaluate the effects of the spills along the riverbed. The study was supported with measurements of Pb isotopic ratios to evaluate watercress samples in a two-end-member mixing scenario. The results indicate that no significant statistical differences were detected when concentrations in watercress samples from the 2014 spill route were compared with those from reference zone. Significant statistical differences and relatively higher concentrations for Cu, Zn and Cd were found when comparing watercress samples from the 1980s spill route with those from reference zone. Concentrations of Ba, U and Sb were relatively higher along the 2014 spill route, possibly associated with the highly differentiated Laramide intrusive rocks of the studied area. Concentrations of Cu and Zn along zone 3 exceeded the FAO/WHO values, as well as the geochemical baseline levels of the Sonora River basin. Concentrations of Fe and Pb exceeded the maximum FAO/WHO values in both cases, but did not exceed the geochemical baseline levels of the Sonora River basin. Concentration of Cd in watercress samples exceeded the geochemical baseline, and it was five to nine times higher than the FAO/WHO value. Regarding Pb isotope ratios, a linear arrangement was observed between the two end-members, which were comprised by a geogenic component defined by the Pb isotope signatures of rocks representing the study area, and the other member (anthropogenic) was defined by isotope ratio obtained from a sample collected from the spilled copper sulfate solution in 2014. Watercress samples collected on the spill route yielded Pb isotopes signatures that suggest an influence from the spill of ~ 65 and ~ 42%, for zones 1 and 2, respectively. Pb isotope ratios in watercress samples from zone 3 were closer to the anthropogenic end-member. These Pb signatures reflect a mine spill that occurred in the 1980s, when Buenavista del Cobre mine was operated by a previous company. Finally, in watercress samples from zone 4, the Pb signatures were more likely acquired from the geogenic component.

Sampling for microfossils in exploratory wells in basins with hydrocarbon potential is subject to considerable uncertainty, mainly because the samples usually are small and subject to caving. Biostratigraphic events defined on fossil taxa include their last occurrences of which the depths along the wells generally can be measured with precision. The RASC method for ranking and scaling of stratigraphic events produces an average basin-wide optimum sequence and zonation that can be used for correlation of strata between wells. In this optimum sequence the fossil events are ordered according to their occurrences in geological time. Depth differences between successive events in the optimum sequence satisfy a frequency distribution that is of interest for potentially increasing stratigraphic resolution. In this article the depth difference frequency distribution is modeled for three large Cenozoic microfossil data sets consisting of 30 wells in the North Sea Basin, 27 wells on the Labrador Shelf and Grand Banks, and 11 wells in the western Barents Sea. The shapes of the three frequency distributions satisfy bilateral gamma distributions with similar parameters. These distributions are fitted by the construction of straightlines on normal Q–Q plots of square root transformed average-corrected depth differences. The gamma distribution model is approximately satisfied except for small negative and positive depth differences, which have anomalous frequencies because of the discrete sampling method used in exploratory well-drilling to collect microfossils. It implies not only comparable average stratigraphic order of events, but also comparable average sedimentation rates in the three Cenozoic basins selected for study.

The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km2 as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.

The triangular distribution is a popular choice when it comes to modeling bounded continuous random variables. Its wide acceptance derives mostly from its simple analytic properties and the ease with which modelers can specify its three parameters through the extremes and the mode. On the negative side, hardly any real process follows a triangular distribution, which from the outset puts at a disadvantage any model employing triangular distributions. At a time when numerical techniques such as the Monte Carlo method are displacing analytic approaches in stochastic resource assessments, easy specification remains the most attractive characteristic of the triangular distribution. The beta distribution is another continuous distribution defined within a finite interval offering wider flexibility in style of variation, thus allowing consideration of models in which the random variables closely follow the observed or expected styles of variation. Despite its more complex definition, generation of values following a beta distribution is as straightforward as generating values following a triangular distribution, leaving the selection of parameters as the main impediment to practically considering beta distributions. This contribution intends to promote the acceptance of the beta distribution by explaining its properties and offering several suggestions to facilitate the specification of its two shape parameters. In general, given the same distributional parameters, use of the beta distributions in stochastic modeling may yield significantly different results, yet better estimates, than the triangular distribution.

Feature extraction is an important part for coal and gangue recognition, which has direct impact on accuracy of recognition. However, the existing feature extraction methods for coal and gangue are not ideal, and so a feature extraction method with multi-fractal is proposed based on energy field normalization for target recognition of coal and gangue in this paper. In the method, the concept of target energy field is established based on 3D grey surface, and the normalized target energy is calculated by using pixels. Then, after analysis of the feature extraction process, a feature extraction algorithm with multi-fractal is proposed based on energy field, in which 3D grey surface is divided by different grid forms, and the pixels in grid are counted to obtain the probability density distribution matrix of pixels. The results of multiple feature extraction is observed visually from probability density distribution, spatial feature distribution, and multi-fractal spectrum to illustrate the measurability of the method for target textures, which is the quantitative attribute of feature extraction. In the experiment, this method is used to quantitatively measure grey texture, and the effectiveness of measured features in coal and gangue recognition is compared with other methods. The experiment results show that the method can achieve effective quantitative measurement for coal and gangue texture, and the recognition accuracy is higher than other methods.

Well log analysis and production testing in coal are the initial requirements to judge the prospectivity of a coalbed methane (CBM) reservoir. The process of prospect identification through laboratory studies is accurate although it is time-consuming and expensive. Therefore, we developed a methodology to identify prospective coal seam by establishing multiple regression models of geophysical well log parameters vs. organic/inorganic contents from laboratory-tested core samples for one seam. The Langmuir’s equation and methane adsorption isotherm were used to estimation of gas and saturation content by developing a regression model from organic content. Gas and coal contents (ash, moisture, fixed carbon, and volatile matter) were obtained from the subsequent propagation of the established equations to other wells. Gas saturation increased with depth from 60 to 69%. Mapped seam thickness and gas content were in the ranges of 10.0–54.0 m and 6.1–28.2 cc/g, respectively. Overlaying of seam thickness and gas content identified the sweet spots in releasing potential future well locations. Errors within the permissible limit between the predicted and observed values indicate the gas estimation to be reliable. Another application for electro-facies classification was demonstrated by applying multi-resolution graph-based clustering architecture to capture texture parameters from histogram and auto-covariance function in resistivity image log. Determination of lithology by correlation of resistivity image and geophysical well log corroborated with the depositional environment having fining upward formational sequence. Thus, this study helps in estimating proximate components, gas content, and saturation with depth in coal seam for production optimization to better understand its implications on the dewatering and gas production periods in the Bokaro CBM reservoir situated in India.

Contamination of soils with heavy metals may pose long-term risk to groundwater quality leading to health implications. Bioavailability of heavy metals, like cadmium (Cd) is strongly affected by sorption and desorption processes. The release of heavy metals from contaminated soils is a major contamination risks to natural waters. The release of Cd from contaminated soils is strongly influenced by its mobility and bioavailability. In this study, the kinetics of Cd desorption from ten samples of contaminated calcareous soils, with widely varying physicochemical properties, were studied using 0.01 M EDTA extraction. The median percentage of Cd released was about 27.7% of the total extractable Cd in the soils. The release of Cd was characterized by an initial fast release rate (of labile fractions) followed by a slower release rate (of less labile fractions) and a model of two first-order reactions adequately describes the observed release of Cd from the studied soil samples. There was positive correlation between the amount of Cd released at first phase of release and Cd in exchangeable fraction, indicating that this fraction of Cd is the main fraction controlling the Cd in the kinetic experiments. There was strongly negative correlation between the amount of Cd released at first and second phases of release and residual fraction, suggesting that this fraction did not contribute in Cd release in the kinetic experiments. The results can be used to provide information for evaluation of Cd potential toxicity and ecological risk from contaminated calcareous soils.

Weights-of-Evidence (WofE) and Radial Basis Function Link Net (RBFLN) were applied to soil group mapping in eastern Finland. The data consisted of low altitude airborne geophysical measurements, Landsat 5 TM-satellite image, and digital elevation model (DEM) and slope information derived from it. Probability maps were constructed for each soil group one by one and combined into a prediction map of soil groups using maximum posterior probability (WofE) or pattern membership (RBFLN). Self-Organizing Map (SOM) and Sammon’s Mapping were applied for selecting the data sets for modeling and visualizing the data. The soil types belonging to each soil group used in the Arc-SDM modeling were defined by clusters revealed by the SOM and Sammon’s Mapping algorithms. The soil types with similar characters were collected in the same cluster. Numerical evaluation of the models’ performance was performed using the confusion matrix. The Ratio of Correct Classifications (RCC) for the best WofE model was 0.64 in the training area and 0.61 in the testing area. The RCC for the best RBFLN model was 0.62. Modeling of soil groups using Arc-SDM is time consuming because models need to be constructed for each soil group before combining them into a final prediction map. In this study a simple method was tested for combining the maps. In the future, more attention should be paid to combining the posterior probability models and also to selecting data sets used for modeling.