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Application Of Artificial Neural Network Systems To Ore Grade Estimation From Exploration Data
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Book Synopsis Application of Artificial Neural Network Systems to Ore Grade Estimation from Exploration Data by : Ioannis K. Kapageridis
Download or read book Application of Artificial Neural Network Systems to Ore Grade Estimation from Exploration Data written by Ioannis K. Kapageridis and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimating Ore Grade Using Evolutionary Machine Learning Models by : Mohammad Ehteram
Download or read book Estimating Ore Grade Using Evolutionary Machine Learning Models written by Mohammad Ehteram and published by Springer. This book was released on 2023-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the abilities of new machine learning models for predicting ore grade in mining engineering. A variety of case studies are examined in this book. A motivation for preparing this book was the absence of robust models for estimating ore grade. Models of current books can also be used for the different sciences because they have high capabilities for estimating different variables. Mining engineers can use the book to determine the ore grade accurately. This book helps identify mineral-rich regions for exploration and exploitation. Exploration costs can be decreased by using the models in the current book. In this book, the author discusses the new concepts in mining engineering, such as uncertainty in ore grade modeling. Ensemble models are presented in this book to estimate ore grade. In the book, readers learn how to construct advanced machine learning models for estimating ore grade. The authors of this book present advanced and hybrid models used to estimate ore grade instead of the classic methods such as kriging. The current book can be used as a comprehensive handbook for estimating ore grades. Industrial managers and modelers can use the models of the current books. Each level of ore grade modeling is explained in the book. In this book, advanced optimizers are presented to train machine learning models. Therefore, the book can also be used by modelers in other fields. The main motivation of this book is to address previous shortcomings in the modeling process of ore grades. The scope of this book includes mining engineering, soft computing models, and artificial intelligence.
Book Synopsis Application of Artificial Neural Network Systems to Grade Estimation from Exploration Data by : Ioannis Konstantinou Kapageridis
Download or read book Application of Artificial Neural Network Systems to Grade Estimation from Exploration Data written by Ioannis Konstantinou Kapageridis and published by . This book was released on 1999 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural-network Modelling of Placer Ore Grade Spatial Variability by : Jinchuan Ke
Download or read book Neural-network Modelling of Placer Ore Grade Spatial Variability written by Jinchuan Ke and published by . This book was released on 2002 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Traditional geostatistical methods have been used in ore reserve estimation for decades. Research in the last two decades or so has added a number of other statistical methodologies for ore reserve estimation procedures. Recent advances in neural networks have provided a new approach to solve this problem. This thesis is focused on the Neural-network modeling for the estimation of placer ore reserve. Due to the spatial variability, multiple dimensional inputs and very noisy drill hole sample data from the selected region, it requires that the neural-network be organized in a multiple-layers to handle the non-linearity and hidden slabs for smoothing the predicted results. Various neural-network architectures are investigated and the Back-propagation is selected for modeling the ore reserve estimation problem. Sensitivity analysis is performed for the following parameters: the type of neural-network architecture, number of hidden layers and hidden neurons, type of activation functions, learning rate and momentum factors, input pattern schedule, weight updated, and so on. The influences of these parameters on the predicted output are analyzed in details and the optimal parameters are determined. To investigate the accuracy and promise of neural network modeling as a tool for ore reserve estimation, the ore grade and tonnage of Neural-network output is compared with those estimated by geostatistical methods under various cut-off grades. In addition, the overall performance is also validated by the analysis of R-squared (R2), Root-Mean-Squared (RMS), and the comparison between predicted values and 'actual' values. As the final part of this study, the optimized Neural Network was used to esimate the distribution of placer gold grade and volume of gold resource in offshore Nome. The predicted results for all the mining blocks in the lease area are validated by checking the values of RMS, R2, and Scatter plots. The estimated gold grades are also presented as contour maps for visualization"--Leaf iii.
Book Synopsis Computer Applications in Resource Estimation by : G. Gaal
Download or read book Computer Applications in Resource Estimation written by G. Gaal and published by Elsevier. This book was released on 2013-10-22 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative resource assessment methods play an increasing role in exploration for petroleum, water and minerals. This volume presents an international review on the state-of-the-art of the computerized methodology in resource exploration. The papers taken from those presented at the symposium are classified to either techniques, i.e., trend analysis; classification techniques; geostatistics; image analysis; expert systems/artificial intelligence; inventories; tomography and others, or to resources, i.e., petroleum, water, metals and non-metals.
Book Synopsis Geostatistical Ore Reserve Estimation by : M. David
Download or read book Geostatistical Ore Reserve Estimation written by M. David and published by Elsevier. This book was released on 2012-12-02 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developments in Geomathematics, 2: Geostatistical Ore Reserve Estimation focuses on the methodologies, processes, and principles involved in geostatistical ore reserve estimation, including the use of variogram, sampling, theoretical models, and variances and covariances. The publication first takes a look at elementary statistical theory and applications; contribution of distributions to mineral reserves problems; and evaluation of methods used in ore reserve calculations. Concerns cover estimation problems during a mine life, origin and credentials of geostatistics, precision of a sampling campaign and prediction of the effect of further sampling, exercises on grade-tonnage curves, theoretical models of distributions, and computational remarks on variances and covariances. The text then examines variogram and the practice of variogram modeling. Discussions focus on solving problems in one dimension, linear combinations and average values, theoretical models of isotropic variograms, the variogram as a geological features descriptor, and the variogram as the fundamental function in error computations. The manuscript ponders on statistical problems in sample preparation, orebody modeling, grade-tonnage curves, ore-waste selection, and planning problems, the practice of kriging, and the effective computation of block variances. The text is a valuable source of data for researchers interested in geostatistical ore reserve estimation.
Book Synopsis Predictive Performance of Machine Learning Algorithms for Ore Reserve Estimation in Sparse and Imprecise Data by : Sridhar Dutta
Download or read book Predictive Performance of Machine Learning Algorithms for Ore Reserve Estimation in Sparse and Imprecise Data written by Sridhar Dutta and published by . This book was released on 2006 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Traditional 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"--Leaf iii.
Book Synopsis Artificial Neural Network Applications for Predictive Mining Tasks by : Babu R. Yama
Download or read book Artificial Neural Network Applications for Predictive Mining Tasks written by Babu R. Yama and published by . This book was released on 1998 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of Neural Networks to Mineral Reserve Estimation by : Christopher C. H. Burnett
Download or read book Application of Neural Networks to Mineral Reserve Estimation written by Christopher C. H. Burnett and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of Computers and Operations Research in the Mineral Industry by :
Download or read book Application of Computers and Operations Research in the Mineral Industry written by and published by . This book was released on 1995 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applications of Geostatistics in Ore Reserve Estimation and Grade Control by : Roger A. Blais
Download or read book Applications of Geostatistics in Ore Reserve Estimation and Grade Control written by Roger A. Blais and published by . This book was released on 1967 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applications of Artificial Neural Networks for Representation of Drilling by : J. B. Cheatham
Download or read book Applications of Artificial Neural Networks for Representation of Drilling written by J. B. Cheatham and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To investigate methods of arriving at the minimum in situ stress profile in drilled formations using data collected during normal drilling operations.
Book Synopsis Mining Intelligence and Knowledge Exploration by : Adrian Groza
Download or read book Mining Intelligence and Knowledge Exploration written by Adrian Groza and published by Springer. This book was released on 2018-12-18 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 6th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2018, held in Cluj-Napoca, Romania, in December 2018. The 33 full papers presented were carefully reviewed and selected from 93 submissions. The papers were grouped into various subtopics including evolutionary computation, knowledge exploration in IoT, artificial intelligence, machine learning, image processing, pattern recognition, speech processing, information retrieval, natural language processing, social network analysis, security, and fuzzy rough sets.
Book Synopsis Application of a Geographic Information System to Mineral Exploration in the Goldfield Mining District, Nevada, Incorporating Geologic and Remote Sensing Data by : Joseph Anthony Martinetti
Download or read book Application of a Geographic Information System to Mineral Exploration in the Goldfield Mining District, Nevada, Incorporating Geologic and Remote Sensing Data written by Joseph Anthony Martinetti and published by . This book was released on 1997 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mineral Resource and Ore Reserve Estimation by : A. C. Edwards
Download or read book Mineral Resource and Ore Reserve Estimation written by A. C. Edwards and published by . This book was released on 2001 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mine Planning and Equipment Selection 2000 by : T.N. Michalakopoulos
Download or read book Mine Planning and Equipment Selection 2000 written by T.N. Michalakopoulos and published by CRC Press. This book was released on 2000-01-01 with total page 1000 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text looks at mine planning and equipment and covers topics such as: design and planning of surface and underground mines; geotechnical stability in surface and underground mines; and mining and the environment.
Book Synopsis Computing Reserves of Mineral Deposits by : Constantine C. Popoff
Download or read book Computing Reserves of Mineral Deposits written by Constantine C. Popoff and published by . This book was released on 1966 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
