Predictive Performance Of Machine Learning Algorithms For Ore Reserve Estimation In Sparse And Imprecise Data

Thesis (Ph.D.) University of Alaska Fairbanks, 2006Traditional geostatistical estimation techniques have been used predominantly in the mining industry for the purpose of ore reserve estimation. Determination of mineral reserve has always posed considerable challenge to mining engineers due to geological complexities that are generally associated with the phenomenon of ore body formation. Considerable research over the years has resulted in the development of a number of state-of-the-art methods for the task of predictive spatial mapping such as ore reserve estimation. Recent advances in the use of the machine learning algorithms (MLA) have provided a new approach to solve the age-old problem. Therefore, this thesis is focused on the use of two MLA, viz. the neural network (NN) and support vector machine (SVM), for the purpose of ore reserve estimation. Application of the MLA have been elaborated with two complex drill hole datasets. The first dataset is a placer gold drill hole data characterized by high degree of spatial variability, sparseness and noise while the second dataset is obtained from a continuous lode deposit. The application and success of the models developed using these MLA for the purpose of ore reserve estimation depends to a large extent on the data subsets on which they are trained and subsequently on the selection of the appropriate model parameters. The model data subsets obtained by random data division are not desirable in sparse data conditions as it usually results in statistically dissimilar subsets, thereby reducing their applicability. Therefore, an ideal technique for data subdivision has been suggested in the thesis. Additionally, issues pertaining to the optimum model development have also been discussed. To investigate the accuracy and the applicability of the MLA for ore reserve estimation, their generalization ability was compared with the geostatistical ordinary kriging (OK) method. The analysis of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Error (ME) and the coefficient of determination (R2) as the indices of the model performance indicated that they may significantly improve the predictive ability and thereby reduce the inherent risk in ore reserve estimation

Assessment of Ore Grade Estimation Methods for Structurally Controlled Vein Deposits - A Review

Resource estimation techniques have upgraded over the past couple of years, thereby improving resource estimates. The classical method of estimation is less used in ore grade estimation than geostatistics (kriging) which proved to provide more accurate estimates by its ability to account for the geology of the deposit and assess error. Geostatistics has therefore been said to be superior over the classical methods of estimation. However, due to the complexity of using geostatistics in resource estimation, its time-consuming nature, the susceptibility to errors due to human interference, the difficulty in applying it to deposits with few data points and the difficulty in using it to estimate complicated deposits paved the way for the application of Artificial Intelligence (AI) techniques to be applied in ore grade estimation. AI techniques have been employed in diverse ore deposit types for the past two decades and have proven to provide comparable or better results than those estimated with kriging. This research aimed to review and compare the most commonly used kriging methods and AI techniques in ore grade estimation of complex structurally controlled vein deposits. The review showed that AI techniques outperformed kriging methods in ore grade estimation of vein deposits.   Keywords: Artificial Intelligence, Neural Networks, Geostatistics, Kriging, Mineral Resource, Grad

Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simula-tions. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can in-corporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p \u3c 10 × 10−5), with correlation coeffi-cients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency

Comparison between the performance of four metaheuristic algorithms in training a multilayer perceptron machine for gold grade estimation

Reserve evaluation is a very difficult and complex process. The most important and yet most challenging part of this process is grade estimation. Its difficulty derived from challenges in obtaining required data from the deposit by drilling boreholes, which is a very time consuming and costly act itself. Classic methods which are used to model the deposit are based on some preliminary assumptions about reserve continuity and grade spatial distribution which are not true about all kind of reserves. In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) is applied to solve the problem of ore grade estimation of highly sparse data from zarshouran gold deposit in Iran. The network is trained using four metaheuristic algorithms in separate stages for each algorithm. These algorithms are artificial bee colony (ABC), genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). The accuracy of predictions obtained from each algorithm in each stage of experiments were compared with real gold grade values. We used unskillful value to check the accuracy and stability of each network. Results showed that the network trained with ABC algorithm outperforms other networks that trained with other algorithms in all stages having least unskillful value of 13.91 for validation data. Therefore, it can be more suitable for solving the problem of predicting ore grade values using highly sparse data

Mine evaluation optimisation

The definition of a mineral resource during exploration is a fundamental part of lease evaluation, which establishes the fair market value of the entire asset being explored in the open market. Since exact prediction of grades between sampled points is not currently possible by conventional methods, an exact agreement between predicted and actual grades will nearly always contain some error. These errors affect the evaluation of resources so impacting on characterisation of risks, financial projections and decisions about whether it is necessary to carry on with the further phases or not. The knowledge about minerals below the surface, even when it is based upon extensive geophysical analysis and drilling, is often too fragmentary to indicate with assurance where to drill, how deep to drill and what can be expected. Thus, the exploration team knows only the density of the rock and the grade along the core. The purpose of this study is to improve the process of resource evaluation in the exploration stage by increasing prediction accuracy and making an alternative assessment about the spatial characteristics of gold mineralisation. There is significant industrial interest in finding alternatives which may speed up the drilling phase, identify anomalies, worthwhile targets and help in establishing fair market value. Recent developments in nonconvex optimisation and high-dimensional statistics have led to the idea that some engineering problems such as predicting gold variability at the exploration stage can be solved with the application of clusterwise linear and penalised maximum likelihood regression techniques. This thesis attempts to solve the distribution of the mineralisation in the underlying geology using clusterwise linear regression and convex Least Absolute Shrinkage and Selection Operator (LASSO) techniques. The two presented optimisation techniques compute predictive solutions within a domain using physical data provided directly from drillholes. The decision-support techniques attempt a useful compromise between the traditional and recently introduced methods in optimisation and regression analysis that are developed to improve exploration targeting and to predict the gold occurrences at previously unsampled locations.Doctor of Philosoph

Development of methods based on neural networks in the estimation of mineral resources

Debido a limitaciones económicas y físicas, nuestra comprensión de los recursos minerales en un área de interés específica es limitada y fragmentada. Tradicionalmente, este problema se ha resuelto utilizando el método geoestadístico de Kriging, donde la ley del mineral se estima en ubicaciones sin mediciones utilizando valores conocidos de la ley en puntos circundantes. La ventaja de este método radica en el cálculo de pesos a través de un modelo de variabilidad espacial conocido como variograma. Sin embargo, su desventaja es que se basa en el supuesto de estacionariedad, aditividad, linealidad y de cierta forma el modelado variográfico es subjetivo. Este estudio propone abordar el problema de estimación de recursos minerales como un problema de regresión utilizando redes neuronales, que no están sujetas a las restricciones de estacionariedad, aditividad, linealidad y modelado espacial de los métodos geoestadísticos. Se han comparado el Kriging y una red neuronal de función de base radial y un perceptrón multicapa utilizando distintas métricas de validación. Los resultados muestran que un modelo de red neuronal adecuadamente entrenado, con un etiquetado apropiado de la ley mineral y sus características de entrada, logra resultados similares al enfoque geoestadístico con una reducción de tiempo significativo, evitando a su vez todos los supuestos antes señalados. Sin embargo, las redes neuronales no consideran la correlación espacial de la ley del mineral ni la reproducen en los lugares donde se midió, características que han marcado desconfianza en su implementación industrial y que se discuten en este artículo proponiendo finalmente un ajuste entre ambos enfoques a un mínimo sacrificio del coste temporal y de mano de obra

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences