Error reduction in long-term mine planning estimates using deep learning models

[1]  Huanlai Xing,et al.  RTFN: A Robust Temporal Feature Network for Time Series Classification , 2020, Inf. Sci..

[2]  Abiodun Ismail Lawal,et al.  Re-examination of Itakpe iron ore deposit for reserve estimation using geostatistics and artificial neural network techniques , 2020, Arabian Journal of Geosciences.

[3]  E. Gloaguen,et al.  Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data , 2019, Journal of Geochemical Exploration.

[4]  Onur Avci,et al.  1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.

[5]  Bharath Kalyan,et al.  Spatial Modeling of Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks , 2018, IEEE Journal of Oceanic Engineering.

[6]  N. Fathianpour,et al.  Comparison of machine learning methods for copper ore grade estimation , 2018, Computational Geosciences.

[7]  Rahul Kumar Singh,et al.  Recurrent neural network approach to mineral deposit modelling , 2018, 2018 4th International Conference on Recent Advances in Information Technology (RAIT).

[8]  Beata Czarnacka-Chrobot,et al.  An effective approach for software project effort and duration estimation with machine learning algorithms , 2018, J. Syst. Softw..

[9]  Nader Fathianpour,et al.  A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation , 2017, Neurocomputing.

[10]  E. Gloaguen,et al.  Machine learning as a tool for geologists , 2017 .

[11]  Agam Das Goswami,et al.  Investigation of general regression neural network architecture for grade estimation of an Indian iron ore deposit , 2017, Arabian Journal of Geosciences.

[12]  Dipti Patra,et al.  Adapting pattern recognition approach for uncertainty assessment in the geologic resource estimation for Indian iron ore mines , 2016, 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES).

[13]  Yaguba Jalloh,et al.  Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study , 2016 .

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  Gerald Penn,et al.  Convolutional Neural Networks for Speech Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[16]  Xiao-li Li,et al.  Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation , 2013, Neurocomputing.

[17]  Cheng Wu,et al.  Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit , 2013, Acta Oceanologica Sinica.

[18]  H M Parker,et al.  Reconciliation principles for the mining industry , 2012 .

[19]  Javad Gholamnejad,et al.  Grade estimation of ore stockpiles by using Artificial Neural Networks: case study on Choghart Iron Mine in Iran , 2012 .

[20]  Pejman Tahmasebi,et al.  A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation , 2012, Comput. Geosci..

[21]  John C. Duchi,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011 .

[22]  Masoud Shariat Panahi,et al.  The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit , 2011, Comput. Geosci..

[23]  William W. Guo.  A novel application of neural networks for instant iron-ore grade estimation , 2010, Expert Syst. Appl..

[24]  Xiaoli Li,et al.  Adaptive ore grade estimation method for the mineral deposit evaluation , 2010, Math. Comput. Model..

[25]  Sukumar Bandopadhyay,et al.  Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data , 2010, J. Intell. Learn. Syst. Appl..

[26]  Biswajit Samanta,et al.  Radial Basis Function Network for Ore Grade Estimation , 2010 .

[27]  Snehamoy Chatterjee,et al.  Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model , 2010 .

[28]  Mohammad Bagher Menhaj,et al.  A hybrid method for grade estimation using genetic algorithm and neural networks , 2009 .

[29]  Snehamoy Chatterjee,et al.  General regression neural network residual estimation for ore grade prediction of limestone deposit , 2007 .

[30]  Sukumar Bandopadhyay,et al.  Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation , 2006 .

[31]  Sukumar Bandopadhyay,et al.  A hybrid ensemble model of kriging and neural network for ore grade estimation , 2006 .

[32]  S. Samanta,et al.  A comparative study of the performance of single neural network vs. Adaboost algorithm based combination of multiple neural networks for mineral resource estimation , 2005 .

[33]  Rajive Ganguli,et al.  Sparse Data Division Using Data Segmentation and Kohonen Network for Neural Network and Geostatistical Ore Grade Modeling in Nome Offshore Placer Deposit , 2004 .

[34]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[35]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[36]  Xiping Wu,et al.  Reserve estimation using neural network techniques , 1993 .

[37]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[38]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[39]  Isabel Margarida Horta Ribeiro Antunes,et al.  Predicting ore content throughout a machine learning procedure – An Sn-W enrichment case study , 2020 .

[40]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[41]  Bülent Tütmez,et al.  Use of hybrid intelligent computing in mineral resources evaluation , 2009, Appl. Soft Comput..

[42]  Sukumar Bandopadhyay,et al.  Data Segmentation and Genetic Algorithms for Sparse Data Division in Nome Placer Gold Grade Estimation Using Neural Network and Geostatistics , 2002 .

[43]  A. E. Annels,et al.  Errors and Uncertainty in Mineral Resource and Ore Reserve Estimation: The Importance of Getting it Right , 2002 .