Investigation of 3D copper grade changeability by Neural Networks in metasomatic ore deposit

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1 Investigation of 3D copper grade changeability by Neural Networks in metasomatic ore deposit STANISLAV TOPALOV 1, VESSELIN HRISTOV 2, 1 Mine Surveying and Geodesy Dept., 2 Computer Science Dept. University of Mining and Geology St. Ivan Rilski, Sofia 1700, Studentski grad, BULGARIA stopalov@gmail.com, veso@mgu.bg Abstract: - The following neural networks (NN) types: Radial Basis Function (RBF), Generalized Regression Neural Networks (GRNN), and two/three layers Multilayer Perceptron (MLP 2, MLP 3) were examined and MLP2 and MLP3 ware determined as a suitable for the copper grade prognostication on one layer of ore deposit. In this paper following the same approach we attempt to determine the potentialities of the same NN types for copper grade prognostication to the deeper levels of the open pit mine. This approach of 3D training and testing of NN efficiency for prognostication is realized with raw exploration data in metasomatic ore deposit. Key words: ore deposit, exploration, 3D (spatial) exploration data, open pit mining method, neural networks, prognosis. 1 Introduction In this investigation STATISTICA Release 7 software is used. It is an advanced and comprehensive, integrated data analysis package. The system includes not only general-purpose statistical and graphics procedures, but also universal implementations of specialized modules (e.g., for social scientists, biomedical researchers, or engineers). One of its modules is STATISTICA Neural Networks (SNN) - powerful and extremely fast neural network data analysis package. It allows selection between several Neural Network models and testing their efficiency by parameters variation. SNN consists of two main tools. Intelligent Problem Solver (IPS) is a tool available to guide through the selection process. STATISTICA supports the most important classes of neural networks, including: Multilayer perceptron, Radial Basis Function Networks, Kohonen Self-Organizing Feature Maps, Probabilistic (Bayesian) Neural Networks, Generalized Regression Neural Networks, Linear Modeling. The IPS can create networks using data whose cases are independent (standard networks) as well as networks that predict future observations based on previous observations of the same variable (time series networks). A significant amount of time during the design of a neural network is spent on the selection of appropriate variables, and then optimizing the network architecture by heuristic search. IPS gives possibility to train automatically different types of NN using the same data and to make easy the best choice. StatSoft, Inc. The other tool is Custom Network Designer (CND), allowing to construct individual network architecture and to specify training algorithms. The best type of NN determined by IPS and its parameters can be used as a basis for network design in CND. The training process is able to be observed in real time and to be improved. An approach using these two SNN tools for prognostication of geological parameters value only in the limits of one production level (2D), is described in [5,6] 2. Problem Formulation The degree of the ore body exploration and its geological complexity are closely interrelated by the well-known reasons. The geological complexity and grade changeability determine the exploration methods [5]. Unfortunately, there is no normative standard for regulating of Bulgarian ore deposits geological complexity and changeability character. Similar standard would solve the problems associated to: the ratio between drilling and mining exploration activities, the geometry and the density of the exploration grid, the sampling methods and techniques, etc. [1, 3]. The difficulty in assessing quantitatively the geological complexity and changeability character of ore body (or its parts) arises out of the hypothetical character of the input geological data. The methods of interpolation and extrapolation, limited by the location of the sampling points are essential for the geological and mining solutions. ISBN: ISSN:

2 The Elatsite porphyry copper deposit is situated about km eastern from the city of Sofia and about 6 km southern from the town of Еtropole. The ore body of the deposit is an ore stock. In the ore are fixed 61 minerals generally. 48 of them are ore minerals and the others are non-metallic minerals. Basically they are essential, accessories and rare. The main ore minerals are chalcopyrite, pyrite, bornite and molybdenite. The ore body has been detailed prospected with exploratory holes. Now, during the mining process exploitational exploration is realized by systematic blast hole sampling. The samples are located in grid approximately m. Mining method is opencast mining. Most of the extracting levels are already worked-out. All the information about copper grade from these levels is available. 3. Problem Solution The information from exploration exploitational stage is organized in MS Excel format, which consist number of sample, the coordinates X, Y and H (3D), copper grade value, getting date etc. for every one extracting levels. As input variables the sample coordinates X, Y and H (three input parameters) are used. As an output variable from the neural network the copper grade (one output parameter) is required to be determined. All sampling data from four contiguous extract levels is divided of two main groups. The first contains the information (2540 sample points) from three consecutive levels and these data is served for neural network training. The data (690 sample points) from second group for the fourth extracting level (the deepest in our case) is served for testing of already trained neural networks. The IPS tool is used at the first stage of the investigation. The Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN) and Multi Layer Perception (MLP with two or three hidden layers) type neural networks have been trained and tested. To avoid unnecessary elaborate approximation, the nodes number in hidden layers is specified between five and ten [2, 4]. Some different type trained neural networks with different parameters were determined as a result of IPS performance. The best network is chosen by the criteria of lowest training error and testing error. After several IPS performances it makes an impression that the best results give us Multi Layer Perception with three hidden layers (MLP3) - 10 nodes in the first, 7 in the second, trained in 100 epochs by Back propagation algorithm in a first phase, and in 20 and 26 epochs by Conjugate gradient descent algorithm in the second and the third phase respectively. The parameters of this neural network are given in Table 1. Graphically its architecture is shown in Figure 1. Table 1 IPS Network Tests No Profile Train Error Test Error Training 11 MLP 3: : BP100,CG20,CG26b Profile : MLP 3: :1 x y H Cu Fig. 1. The Multi Layer Perceptron chosen by IPS as the best The second investigation stage involves of neural network construction using the STATISTICA Custom Network Design tool. All initial characteristics correspond to IPS recommended i.e. analogous of ISBN: ISSN:

3 these given in Figure 1. The transfer function chosen was a logistic, normal distribution of the initial values of weights, and three training phases. Back Propagation algorithm with 100 epochs was used in the first phase and the Conjugate Gradient Descent algorithm with 500 epochs in the second and the third phase. As a result of training process the value of mean square training error is equal to 0.07 and the mean square test error value is The graph on Figure 2 presents the dynamic of train error decrease during the training process. It is going down promptly in the first phase and then decreases smoothly. Table 2 gives the weights of the three layers trained perceptron. Error Training Graph Epochs Fig. 2. CND Training Process Table 2. The weights of the three layers trained perceptron Nodes Thresh Figure 3 shows the behavior of NN performance on the test data. Similarity between Cu (row data) variation and Cu (NN output) variation can be clearly noted. The correlation coefficients as a measure of relation between copper grade raw data and neural network output (predicted data) are estimated. The scaterplots with linear relationship between all raw copper grade data, train and test data and neural network predicted are given in Figure 4. The significance of the correlation coefficient in training stage (0.81) denotes that the quality of training is quite good. The value of ISBN: ISSN:

4 correlation coefficient between raw test data and neural network predicted is lower (0.67) but significant too, which guarantee reliable prognosis of copper grade Cu (Row Data) Cu (NN Output) Fig. 3. Behavior of NN performance on the test data a) R = 0.79 b) R = 0.81 c) R = 0.62 Fig. 4. Correlation scatterplots and coefficients: a) all Cu row data versus all NN output, b) train Cu row data versus train NN output, c) test Cu row data versus test NN output 4. Conclusion Three types of neural network (MLP, RBF and GRNN) were tried using the IPS software tool of STATISTICA on real data provided from the stage of porphiry copper deposit exploration. The information includes coordinates (X, Y and H) of prospect hole and the value of copper grade for four consecutive levels. As the best neural network model, IPS indicates the Multi Layer Perceptron (MLP) with two hidden layers - 10 nodes in the first and 7 in the second. Using the STATISTICA Custom Network Design tool with initial characteristics recommended from IPS a prognostication model for copper grade value in the deepest extracting level of open pit mine is constructed. The reliability of training process and testing is estimated by the correlation coefficients between raw copper grade data and neural network predicted. So far, neural network application has not been employed extensively in estimations and prognoses concerning mineral deposits. Besides, the unique character of natural conditions, varying even within the same type of deposit, requires experimenting with different types of prognostication model. Narrowing of the scope of different types of neural network (even such with different parameters) was fulfilled using the module of Intelligent Problem Solver of the STATISTICA 7.0 software. The explorer can choose the best type of model from those suggested by IPS, a model whose parameters are the basis of active prognostication model designing using the STATISTICA 7.0 module of Custom Network Design. The module can trace the quality of training in the process of prognostication problem solving. ISBN: ISSN:

5 References: [1] Denby B., C. C. H. Burnet, GEMNet - Using Neural Networks to approximate the Location - Grade Relationship in Mineral Deposits., AIMS Research Unit, Department of Mineral Resources Engineering, University of Nottingham, UK, [2] Kapageridis I. K., B. Denby, G. Hunter, GEMNet II - A Neural Ore Grade Estimation System. In: 29th Interation Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM '99), Denver, Colorado, [3] Kapageridis I. K., B. Denby, D. Scofield. GEMNet II - An Alternative Method for Grade Estimation. uk.geocities.com/adaptive_geoservices/mpes2000 _GEMNET.pdf [4] Johansson, E. M., Dowla, F. U., & Goodman D. M. "Backpropagation learning for multilayer feed - forward neural networks using the conjugate gradient method", International Journal of Neural Systems, Vol. 2, No. 4, pp , [5] Topalov S., K. Boev, I. Koshev. Experiment of Artificial Intelligence (Neural Network) use for geological parameter prognostication. X-th National Mine Surveying Conference, St. Konstantin and Helene, June, (Топалов Ст., К. Боев, Ив. Кошев, Опит за използване на изкуствен интелект (невронни мрежи) за прогнозиране на изучаван геоложки показател., Х-та Национална Маркшайдерска конференция., Св. Конст. и Елена, юни, 2003.) [6] Topalov S., V. Hristov, Prognostication of geological parameters value using Neural Networks., 13-th International Congress of the International Society for Mine Surveying, Budapest, Hungary, September 2007, (No 48). ISBN: ISSN: