Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria (original) (raw)
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Minerals
This study aimed to model the prospectivity for placer deposits using geomorphic and landscape parameters. Within a geographic information system (GIS), spatial autocorrelation analysis of 3709 geochemical samples was used to identify prospective and non-prospective targets for columbite-tantalite (Nb-Ta) placer deposits of Hana-Lobo (H-L) Geological Complex (West- Central Côte d’Ivoire, West Africa). Based on mineralization system analysis, hydrologic, geomorphologic and landscape parameters were extracted at the locations of the identified targets. Supervised automatic classification approaches were applied, including Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to find a prospectivity model complex enough to capture the nature of the data. Metrics such as cross-validation accuracy (CVA), Receiver Operating Characteristic (ROC) curves, Area Under Curve (AUC) values and F-score values were used to evaluate the performance and robustness of output ...
Arabian Journal of Geosciences, 2016
The development in the emerging technologies of information and communications requires more rare metals. The existing resources, insufficient to assume this progress, require further investigations to discover new rare metal deposits. The traditional methods, based on manual overlay, are unsuitable and expensive. Thus, mineral exploration requires updated methods to easily, quickly, and cost effectively delineate new promising exploration zones. Geographical Information System (GIS) and applied geomatics provide and perfect various modeling techniques implemented in GIS software. In recent years, two spatial modeling techniques were developed and widely applied in mineral exploration, data-driven methods, and knowledge methods. Weight of evidence (WofE) is a data-driven method based on the Bayesian theorem and its fundamental concept of prior and posterior probabilities. The method combines statistically diverse geodata that represent ore-controlling factors by weighting their evidence using "control points" to create a "posterior probability map." Our study area, located at the southern part of Hoggar in the south of Algeria, is potential for Sn, W, and rare metals and encloses several deposits related to peraluminous post-orogenic rare metal granitoïds (RMGs). In this work, "weights of evidence" modeling is applied to map mineral potential of this style of mineralization. Seventeen predictor maps, representing the deposit recognition criterion model, were generated from multi-source geodata (lithology, geochemistry, tectonic, magmatism, and geophysics). These data were used as "input data" and the known deposits (48 mineral occurrences) as "training sites." The WofE modeling gets the following results: (1) generate an output map called "mineral potential map" (MPM), where potential zones are reduced to small areas; (2) the MPM efficiently predicts the well-known deposits of Nahda, Sedis, Rechla, and Tit N'Enir; and (3) highlights some unrecognized areas such as Tedjrine, Monts de Tessalit, and Gara Akeboum. (4) The control model demonstrates the possibility to extend the WofE method to the adjacent regions enclosing a small number of known mineral deposits.
Journal of Applied Geophysics, 2012
This paper describes the application of a multicriteria decision-making (MCDM) technique called ELECTRE III, which is well-known in operations research, to mineral prospectivity mapping (MPM), which involves representation and integration of evidential map layers derived from geological, geophysical, and geochemical geo-data sets. In a case study, thirteen evidential map layers are used for MPM in the area containing the Now Chun copper prospect in the Kerman province of Iran. The ELECTRE III technique was applied for MPM, and the outputs are validated using 3D models of Cu and Mo concentrations from 21 drill hole data. This proposed method shows high performance for MPM.
Mineral predictive mapping-from intuition to quantitative hybrid 3D modelling
2017
For many years, mineral predictive mapping was guided by intuition and knowledge based approaches using maps and exploration models. The development of powerful affordable computers, together with the broad availability of various large data sets has provided the base for development of various computer based mineral predictive mapping technologies. These advances when coupled with easy to use software products (e.g. Beak`s advangeo® Prediction Software) is enhancing the introduction of AI technologies into daily practical work making them available to GIS users and 3D modelers.
MINERAL POTENTIAL PREDICTIVE MODELING USING GIS
Mineral Potential modeling produces maps showing areas that are most likely to contain economic concentrations of minerals to be explored. The maps can also be used to serve other purposes like showing where the most prospective areas are relative to residential areas, existing mine sites, historical exploration, or processing facilities. These maps are also known as predictive or posterior probability maps because they show the statistical probability of the metal or mineral of interest occurring in a predetermined area. The Maps present probability data ranked in terms of high probability of occurrence to the least, which are interpreted as a relative measure of favourability by (e.g. high, moderate, low, or poor). These classified and ranked maps are then used by explorers to prioritize their investment targeting highly prospective areas and place lesser importance or even completely ignore land that is not prospective. The objective of this paper is to introduce Geospatial correlative integration (GCI) modeling methodology, a framework of conflating different GIS based tasks and, using GIS as a basic tool to create maps and applications for use as prediction documents in different disciplines of geosciences. GCI has successfully been used in different countries globally for different geo-scientific applications and the paper explains data requirements, validation techniques, and finally a discussion / conclusion.
Elsevier, 2019
Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning methods, including support vector machine (SVM), artificial neural networks (ANN) and random forest (RF), were employed to conduct GIS-based mineral prospectivity mapping of the Tongling ore district, eastern China. The mineral systems approach was used to translate our understanding of the skarn Cu mineral system into mappable exploration criteria, resulting in 12 predictor maps that represent source, transport, physical trap and chemical deposition processes critical for ore formation. Predictive SVM, ANN and RF models were trained by way of predictor maps, and corroborated using a 10-fold cross-validation. The overall performance of the resulting predictive models was assessed in both training and test datasets using a confusion matrix, set of statistical measurements, receiver operating characteristic curve, and success-rate curve. The assessment results indicate that the three machine learning models presented in this study achieved satisfactory performance levels characterized by high pre-dictive accuracy. In addition, all models exhibited well interpretability that provided consistent ranking information about the relative importance of the evidential features contributing to the final predictions. In comparison, the RF model outperformed the SVM and ANN models, having achieved greater consistency with respect to variations in the model parameters and better predictive accuracy. Importantly, the RF model exhibited the highest predictive efficiency capturing most of the known deposits within the smallest prospective tracts. The above results suggest that the RF model is the most appropriate model for Cu potential mapping in the Tongling ore district, and, therefore, was used to generate a prospectivity map containing very-high, high, moderate, and low potential areas in support of follow-up exploration. The prospective areas delineated in this map occupy 13.97% of the study area and capture 80.95% of the known deposits. The fact that two newly discovered deposits occur within the prospective areas predicted by the prospectivity model indicates that the model is robust and effective regarding exploration target generation.
A Spatial Data-Driven Approach for Mineral Prospectivity Mapping
Remote Sensing
Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence ...
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping
Nonrenewable Resources, 2003
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the “model” and “validation” base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.
Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox
Natural Resources Research
With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanichosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.