Incorporating Spatial Variability of Lithological Units into Ore Grade Estimation of an Indian Limestone Deposit

Jump To References Section

Authors

  • Agam Goswami
     Department of Mining Engineering, NIT Rourkela-769008 ,IN
  • M. K. Mishra
     Department of Mining Engineering, NIT Rourkela-769008 ,IN
  • Dipti Patra
     Department of Electrical Engineering, NIT Rourkela-769008 ,IN
  • S. Choudhury
     Sr Manager, Corporate Mineral Resource Dept., ACL, Mumbai ,IN

Keywords:

Grade Estimation, Multi-Layer Perceptron Neural Network (MLP NN), Ordinary Kriging (OK), Spatial Uncertainty.

Abstract

The ore grade estimation requires accurate prediction of the grade at location from limited borehole information. It plays the dominant role in the decision-making process for investment and development of various mining projects and hence become an important and crucial stage.This paper evaluates the application of multi-layer perceptron neural network (MLP NN) architecture to improve the predictability in grade estimation from west coast limestone deposit, Chandrapur district, Maharashtra. The spatial variability of lithological information is incorporated as secondary information in the model for grade estimation. In this investigation the three dimensional spatial coordinates along with four underlying lithological units are taken as input variables and, the four grade attribute of limestone deposit such as CaO, Al2O3, Fe2O3, and SiO2 are taken as the output variable. The comparative analysis of these models have been carried out and the results obtained, are validated with geostatistical method ordinary Kriging (OK). The observed value of various performance criteria viz. regression coefficient and mean square error revealed that the MLP NN performed well as compared to OK in terms of generalization and predictability of ore grades.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2022-10-23

How to Cite

Goswami, A., Mishra, M. K., Patra, D., & Choudhury, S. (2022). Incorporating Spatial Variability of Lithological Units into Ore Grade Estimation of an Indian Limestone Deposit. Journal of Mines, Metals and Fuels, 66(10), 780–786. Retrieved from https://informaticsjournals.com/index.php/jmmf/article/view/31798

Issue

Volume 66, Issue 10, October 2018

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

References

Chatterjee S., Bhattacherjee A., Samanta B., Pal S.K. (2006): “Ore grade estimation of a limestone deposit in India using an artificial neural network”, Applied GIS, Vol. 2:1, pp. 03.1–03.20

Chatterjee S., Bandopadhyay S., Ganguli R., Bhattacherjee A., Samanta B., Pal S.K. (2007): “General regression neural network residual estimation for ore grade prediction of limestone deposit”, Mining Technology, Vol. 116:3, pp. 89–99

Chatterjee S., Bandopadhyay S., Machuca D. (2010): “Ore grade prediction using a genetic algorithm and clustering based ensemble neural network model”, Mathematical Geosciences, Vol. 42, pp. 309–326

David M. (1977): “Geostatistical Ore Reserve Estimation”, Amsterdam, Elsevier Scientific Publishing, pp. 384

Dutta S., Bandopadhyay S., Ganguli R., Misra D. (2010): “Machine learning algorithms and their application to ore reserve estimation of sparse and imprecise data”, Journal of Intelligent Learning Systems & Applications, Vol. 2, pp. 86–96

Dutta S., Mishra D., Ganguli R., Samanta B., Bandopadhyay S. (2006): “A hybrid ensemble model of kriging and neural network for ore grade estimation”, International Journal of Mining, reclamation, and Environment, Vol.20:1, pp. 33–45

Goswami D.A., Mishra K.M., Patra D. (2017): “Investigation of general regression neural network architecture for grade estimation of an Indian iron ore deposit”, Arabian Journal of Geosciences, Vol. 10:4, pp. 1–14

Goswami D.A., Mishra K.M., Patra D. (2017): “Adapting pattern recognition approach for uncertainty assessment in the geologic resource estimation for Indian iron ore mines”, In Signal Processing, Communication, Power and Embedded System (SCOPES), 2016 International Conference on (pp. 1816-1821) IEEE

Haykins S. (1999): “Neural Networks: A comprehensive foundation”, New Jersy, Prentice Hall, pp.824

Hyun J., Saro L. (2010): “Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea”, Natural Resources Research, Vol. 19:2, pp. 103–124.

Kapageridis I.K. (2005): “Input space configuration effects in neural network-based grade estimation”, Computers & Geoscience, Vol. 31, pp.704–717

Koike K., Matsuda S., Suzuki T., Ohmi M., (2002): “Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District, Northern Japan, for Exploring Kuroko-Type Deposits”, Natural Resources Research, Vol. 11:2, pp. 135–156

Mahmoudabadi H., Mohammad I., Mohammad B.M. (2009): “A hybrid method for grade estimation using genetic algorithm and neural networks”, Computers & Geosciences, Vol.13, pp. 91–101

Samanta, B., Bandopadhyay, S. and Ganguli, R., (2004): “Sparse Data Division Using Data Segmentation and Kohonen Network for Neural Network and Geostatistical Ore Grade Modeling in Nome Offshore Placer Deposit”, Natural Resources Research, Vol. 13: 3, pp.189–200

Samanta B., Ganguli R., Bandopadhyay S. (2005): “Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit”, Mining Technology: Transactions of the Institutions of Mining and Metallurgy: Section A, Vol. 114, pp.129–139

Samanta B., Bandopadhyay S., Ganguli R., (2006): “Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation”, Mathematical Geology, Vol. 38:2, pp. 175–197

Samanta B., Bandopadhyay S. (2009): “Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit”, Computers & Geoscience, Vol.19:2, pp.1592–1602

Samanta B. (2010): “Radial basis function network for ore grade estimation”, Natural Resources Resaerch, Vol.19:2, pp. 91–102

Shahoo M., Ramazia H.R., Moradi S. (2014): “Estimation of Iron concentration by using a support vector machine and an artificial neural network - the case study of the Choghart deposit southeast of Yazd, Yazd, Iran”, Geopersia, Vol.4:2, pp. 201–212

Singh J., Verma A.K., Banka H., Singh T. N., Maheshwar S., (2016): “A study of soft computing models for prediction of longitudinal wave velocity”, Arabian Journal of geosciences, Vol.9:224, pp.3-11

Tahmasebi P., Hezarkhani A., (2010): “Comparison of optimized neural network with fuzzy logic for ore grade estimation, Australian Journal of Basic and Applied Sciences, Vol. 4:5, pp.764–772

Tahmasebi P., Hezarkhani A. (2011), “Application of a Modular Feed forward Neural Network for Grade Estimation”, Natural Resources Research, Vol.20:1, pp. 25–32

Tahmasebi P., Hezarkhani A. (2011): “Application of adaptive neuro-fuzzy inference system for grade estimation; case study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran”, Australian Journal of Basic and Applied Sciences, Vol.4:3, pp. 408–420

Tahmasebi P., Hezarkhani A. (2012): “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation”, Computers & Geosciences, Vol.42, pp. 18–27

Wu X., Zhou Y. (1993): “Reserve estimation using neural network techniques”, Computer and geosciences, Vol.19:4, pp. 567–575.