Development of Analytical Models for Estimating Ore Quantities Using Geological and Geophysical Data (original) (raw)

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Minerals

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mini...

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.

Mineral Potential Modelling for Gold Exploration

—This report provides an overview of the present status, domain problems and our future perspective of research in the fields of mineral prospectivity analysis and quantitative resource estimation. Challenges we discuss are using 2D data for creating 3D prediction models and decision making about size of the input cell. In this domain, there is a shift from knowledge-driven to data-driven methodologies and our perspective towards the problem as computer scientists will be proposing usage of Recurrent Neural Network with Long Short Term Memory (LSTM) architecture because of its success in sequence labelling.

Machine Learning Based Systems Application to Mineral Resource Estimation and Compliance with Reporting Codes for Mineral Resources

International Conference on Raw Materials and Circular Economy – RawMat2021, 2021

Machine learning algorithms have been used in various steps of mineral resource estimation in the last four decades - from regression algorithms in variogram model fitting to implicit geological modelling using radial basis functions, and grade estimation using artificial neural networks. In most cases reported in scientific literature, machine learning algorithms succeeded to some degree in completing a modelling task - part of a mineral resource estimation study, by outperforming conventional methods either in the time taken to complete the task or the accuracy of the produced results. It is a common claim in most machine learning applications in mineral resource estima-tion, that machine learning algorithms achieve this performance improvement against conven-tional methods, based on less assumptions on the input data distribution and requiring minimum expertise by those who apply them. The speed of current computing systems, personal or cloud based, has allowed for complex models to be built using machine learning algorithms within minutes, leading to a few commercial implementations becoming available to mineral resource estimation practitioners and gaining their acceptance as reliable systems. In the last decade, several mineral resource estimation reports, part of various levels of study from preliminary economic assessments to feasibility studies, were based on the results of machine learning algorithms ap-plication. These reports are commonly released as compliant with one of the internationally ac-ceptable reporting codes, such as JORC or NI 43-101. Therefore, it is important to examine how machine learning algorithms are applied to mineral resource estimation, and how this application complies with the guidelines of international reporting codes for mineral resources, particularly with the requirements for transparency and competence. This paper gives an overview of machine learning algorithms and systems used in mineral resource estimation and discusses possible compliance issues with international reporting codes for mineral resources.

GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

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.

Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data

Journal of Intelligent Learning Systems and Applications, 2010

Traditional geostatistical estimation techniques have been used predominantly by the mining industry for ore reserve estimation. Determination of mineral reserve has posed considerable challenge to mining engineers due to the geological complexities of ore body formation. Extensive research over the years has resulted in the development of several state-of-the-art methods for predictive spatial mapping, which could be used for ore reserve estimation; and recent advances in the use of machine learning algorithms (MLA) have provided a new approach for solving the problem of ore reserve estimation. The focus of the present study was on the use of two MLA for estimating ore reserve: namely, neural networks (NN) and support vector machines (SVM). Application of MLA and the various issues involved with using them for reserve estimation have been elaborated with the help of a complex drill-hole dataset that exhibits the typical properties of sparseness and impreciseness that might be associated with a mining dataset. To investigate the accuracy and applicability of MLA for ore reserve estimation, the generalization ability of NN and SVM was compared with the geostatistical ordinary kriging (OK) method.

Turning Geological Data into Reliable Mineral Resource Estimates1

2005

... 2 Principal Geologist-Geostatistician, Quantitative Geoscience Pty Ltd jv@quantitativegeoscience. com ... assumptions about the homogeneity of the zones over which estimation (eg kriging) is to ... In this case, the moving search neighbourhood used in estimation may be adequate ...