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K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran

Year 2022, Volume: 65 Issue: 1, 79 - 92, 10.01.2022
https://doi.org/10.25288/tjb.1010636

Abstract

Due to the efficiency of data mining science for analyzing and reviewing extensive data, especially geochemical data, essential methods and techniques such as the hierarchical method, K-Means method, density-based methods, Cohennon method, and so forth, have been developed and utilized by numerous researchers for clustering. One of the most notable and widely used algorithms in the field of clustering is the K-Means algorithm. This algorithm divides the data into K clusters by emphasizing the distance criterion. This study focuses on applying this method according to lithogeochemical data taken from the 1:100,000 scale map of Chahar-Farsakh in South Khorasan province for the elements of copper, cobalt and nickel to the sampling coordinates. The optimal value of K was classified according to the desirability of the selection and the data, and thus the relationships between these elements in the range were determined. This was analyzed by changing the value of K from 3 to 15 criteria mentioned in each class to reveal the optimal K. According to the observations, the existence of a quadratic relationship with negative concavity between copper and cobalt elements, as well as a special exponential relationship between copper and nickel and a positive linear relationship between nickel and cobalt, were reported. Finally, considering the coordinates of the samples and the concentration of cobalt and nickel, the quantity of copper was predicted using a General Regression Neural Network (GRNN). The accuracy of this method was estimated to be 0.99 on training data and 0.76 on test data. Therefore, using the proposed method (K-means Clustering and GRNN) in this paper, it is possible to examine the extent of changes in other elements in the analysis. Also, it is possible to make deeper and broader explorations via determining the relationship between the elements.

References

  • Abraham, A. (2005). Artificial neural networks. Handbook of measuring system design.
  • Alahgholi, S., Shirazy, A. & Shirazi, A. (2018). Geostatistical studies and anomalous elements detection, Bardaskan Area, Iran. Open Journal of Geology, 8(7), 697-710.
  • Artun, E., Mohaghegh, S. D., Toro, J., Wilson, T. & Sanchez, A. (2005). Reservoir characterization using intelligent seismic inversion. SPE Eastern Regional Meeting.
  • Cheung, Y.-M. (2003). k∗-Means: A new generalized k-means clustering algorithm. Pattern Recognition Letters, 24(15), 2883-2893.
  • Dayhoff, J. E. & DeLeo, J. M. (2001). Artificial neural networks: opening the black box. Cancer: Interdisciplinary International Journal of the American Cancer Society, 91(S8), 1615-1635.
  • Demuth, H. & Beale, M. (1993). Neural Network Toolbox For Use with Matlab Users’ Guide Version 3.0. Ghannadpour, S. S., Hezarkhani, A. & Farahbakhsh, E. (2013). An investigation of Pb geochemical behavior respect to those of Fe and Zn based on k-Means clustering method. Journal of Tethys, 1(4), 291-302.
  • Ghorbani, M. (2013a). Economic geology of Iran (Vol. 581). Springer.
  • Ghorbani, M. (2013b). A summary of geology of Iran. In: The economic geology of Iran (pp. 45-64). Springer. Hajnajafi, G., Jafarirad, A., Afzal, P. & Sheikh-Zakariaee, S.-J. (2021). Geological interpretation using multivariate K-means and robust factor analysis in Dezak area, SW Iran. Environmental Earth Sciences, 80(1), 1-13.
  • Hamerly, G. & Elkan, C. (2003). Learning the k in k-means. Advances in neural information processing systems, 16, 281-288.
  • Heil, J., Häring, V., Marschner, B. & Stumpe, B. (2019). Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils. Geoderma, 337, 11-21.
  • Hezarkhani, A. & Ghannadpour, S. S. (2015). Geochemical behavior investigation based on K-means clustering: basics, concepts and case study. LAP (Lambert Academic Publishing).
  • Khosravi, V., Shirazi, A., Shirazy, A., Hezarkhani, A., & Pour, A. B. (2022). Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran. Mining, 2(1), 1-12.‏
  • Khayer, K., Shirazy, A., Shirazi, A., Ansari, A., Nazerian, H. & Hezarkhani, A. (2021). Determination of Archie’s Tortuosity Factor from Stoneley Waves in Carbonate Reservoirs. International Journal of Science and Engineering Applications (IJSEA), 10, 107-110.
  • Krishna, K. & Murty, M. N. (1999). Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(3), 433-439.
  • Menard, S. (1995). An introduction to logistic regression diagnostics. Applied logistic regression analysis, 58-79. Moritz, R. (2016). The economic geology of Iran: mineral deposits and natural resources (M. Ghorbani). In: Society of Economic Geologists.
  • Murthy, C. A., & Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8), 825-832.
  • Pelleg, D. & Moore, A. W. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. Icml, Proceedings of the Seventeenth International Conference on Machine Learning.
  • Schalkoff, R. J. (1997). Artificial neural networks. McGraw-Hill Higher Education.
  • Shirazi, A., Hezarkhani, A., Shirazy, A. & Shahrood, I. (2018a). Exploration Geochemistry Data-Application for Cu Anomaly Separation Based On Classical and Modern Statistical Methods in South Khorasan, Iran. International Journal of Science and Engineering Applications, 7, 39-44.
  • Shirazi, A., Hezarkhani, A., Shirazy, A. & Shahrood, I. (2018b). Remote sensing studies for mapping of iron oxide regions, South of Kerman, Iran. International Journal of Science and Engineering Applications, 7(4), 45-51.
  • Shirazi, A., Shirazy, A. & Karami, J. (2018c). Remote sensing to identify copper alterations and promising regions, Sarbishe, South Khorasan, Iran. International Journal of Geology and Earth Sciences, 4(2), 36-52.
  • Shirazi, A., Shirazy, A., Saki, S. & Hezarkhani, A. (2018d). Geostatistics studies and geochemical modeling based on core data, sheytoor iron deposit, Iran. Journal of Geological Resource and Engineering, 6, 124-133.
  • Shirazi, A., Shirazy, A., Saki, S. & Hezarkhani, A. (2018e). Introducing a software for innovative neuro-fuzzy clustering method named NFCMR. Global Journal of Computer Sciences: theory and research, 8(2), 62-69.
  • Shirazy, A., Shirazi, A., Heidarlaki, S. & Ziaii, M. (2018a). Exploratory Remote Sensing Studies to Determine the Mineralization Zones around the Zarshuran Gold Mine. International Journal of Science and Engineering Applications, 7(9), 274-279.
  • Shirazy, A., Shirazi, A. & Hezarkhani, A. (2018b). Predicting gold grade in Tarq 1: 100,000 geochemical map using the behavior of gold, Arsenic and Antimony by K-means method. Journal of Mineral Resources Engineering, 2(4), 11-23.
  • Shirazy, A., Shirazi, A., Ferdossi, M. H. & Ziaii, M. (2019). Geochemical and geostatistical studies for estimating gold grade in tarq prospect area by k-means clustering method. Open Journal of Geology, 9(6), 306-326.
  • Shirazy, A., Ziaii, M. & Hezarkhani, A. (2020a). Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, Iran. Iranian Journal of Mining Engineering, 14(45), 96-112.
  • Shirazy, A., Ziaii, M., Hezarkhani, A. & Timkin, T. (2020b). Geostatistical and remote sensing studies to identify high metallogenic potential regions in the Kivi area of Iran. Minerals, 10(10), 869.
  • Shirazy, A., Ziaii, M., & Hezarkhani, A. (2021a). Geochemical behavior investigation based on k-means and artificial neural network prediction for titanium and zinc, Kivi region, Iran. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(3), 113-125.
  • Shirazy, A., Hezarkhani, A., Timkin, T. & Shirazi, A. (2021b). Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN)(Iron Ore Deposit, Yazd, IRAN). Minerals, 11(12), 1304.
  • Shirazy, A., Shirazi, A. & Nazerian, H. (2021c). Application of Remote Sensing in Earth Sciences–A Review. International Journal of Science and Engineering Applications, 10, 45-51.
  • Shirazy, A., Shirazi, A., Nazerian, H. & Hezarkhani, A. (2021d). Investigation of Geochemical Sections in Exploratory Boreholes of Mesgaran Copper Deposit in Iran. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 9(8), 2364-2368.
  • Shirazy, A., Shirazi, A., Nazerian, H. & Khayer, K. (2021e). Geophysical study: Estimation of deposit depth using gravimetric data and Euler method (Jalalabad iron mine, kerman province of IRAN). Open Journal of Geology, 11, 340-355.
  • Specht, D. F. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576. Tarkian, M. & Stribrny, B. (1999). Platinum-group elements in porphyry copper deposits: a reconnaissance study. Mineralogy and Petrology, 65(3-4), 161-183.
  • Ullman, J. (1984). A review of: “Pattern Recognition: A Statistical Approach”. By PA Devuver and J. Kittler. (London: Prentice Hall International, 1982.) [pp. 448.]. Remote Sensing, 5(2), 464-464.
  • Xu, Z. (2012). Priority weight intervals derived from intuitionistic multiplicative preference relations. IEEE Transactions on Fuzzy Systems, 21(4), 642-654.
  • Yaghini, M., & Gereilinia, N. (2013). Genetic TKM: A hybrid clustering method based on genetic algorithm, tabu search and k-means. International Journal of Applied Metaheuristic Computing (IJAMC), 4(1), 67-77. Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.

K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran

Year 2022, Volume: 65 Issue: 1, 79 - 92, 10.01.2022
https://doi.org/10.25288/tjb.1010636

Abstract

Due to the efficiency of data mining science for analyzing and reviewing extensive data, especially geochemical data, essential methods and techniques such as the hierarchical method, K-Means method, density-based methods, Cohennon method, and so forth, have been developed and utilized by numerous researchers for clustering. One of the most notable and widely used algorithms in the field of clustering is the K-Means algorithm. This algorithm divides the data into K clusters by emphasizing the distance criterion. This study focuses on applying this method according to lithogeochemical data taken from the 1:100,000 scale map of Chahar-Farsakh in South Khorasan province for the elements of copper, cobalt and nickel to the sampling coordinates. The optimal value of K was classified according to the desirability of the selection and the data, and thus the relationships between these elements in the range were determined. This was analyzed by changing the value of K from 3 to 15 criteria mentioned in each class to reveal the optimal K. According to the observations, the existence of a quadratic relationship with negative concavity between copper and cobalt elements, as well as a special exponential relationship between copper and nickel and a positive linear relationship between nickel and cobalt, were reported. Finally, considering the coordinates of the samples and the concentration of cobalt and nickel, the quantity of copper was predicted using a General Regression Neural Network (GRNN). The accuracy of this method was estimated to be 0.99 on training data and 0.76 on test data. Therefore, using the proposed method (K-means Clustering and GRNN) in this paper, it is possible to examine the extent of changes in other elements in the analysis. Also, it is possible to make deeper and broader explorations via determining the relationship between the elements.

References

  • Abraham, A. (2005). Artificial neural networks. Handbook of measuring system design.
  • Alahgholi, S., Shirazy, A. & Shirazi, A. (2018). Geostatistical studies and anomalous elements detection, Bardaskan Area, Iran. Open Journal of Geology, 8(7), 697-710.
  • Artun, E., Mohaghegh, S. D., Toro, J., Wilson, T. & Sanchez, A. (2005). Reservoir characterization using intelligent seismic inversion. SPE Eastern Regional Meeting.
  • Cheung, Y.-M. (2003). k∗-Means: A new generalized k-means clustering algorithm. Pattern Recognition Letters, 24(15), 2883-2893.
  • Dayhoff, J. E. & DeLeo, J. M. (2001). Artificial neural networks: opening the black box. Cancer: Interdisciplinary International Journal of the American Cancer Society, 91(S8), 1615-1635.
  • Demuth, H. & Beale, M. (1993). Neural Network Toolbox For Use with Matlab Users’ Guide Version 3.0. Ghannadpour, S. S., Hezarkhani, A. & Farahbakhsh, E. (2013). An investigation of Pb geochemical behavior respect to those of Fe and Zn based on k-Means clustering method. Journal of Tethys, 1(4), 291-302.
  • Ghorbani, M. (2013a). Economic geology of Iran (Vol. 581). Springer.
  • Ghorbani, M. (2013b). A summary of geology of Iran. In: The economic geology of Iran (pp. 45-64). Springer. Hajnajafi, G., Jafarirad, A., Afzal, P. & Sheikh-Zakariaee, S.-J. (2021). Geological interpretation using multivariate K-means and robust factor analysis in Dezak area, SW Iran. Environmental Earth Sciences, 80(1), 1-13.
  • Hamerly, G. & Elkan, C. (2003). Learning the k in k-means. Advances in neural information processing systems, 16, 281-288.
  • Heil, J., Häring, V., Marschner, B. & Stumpe, B. (2019). Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils. Geoderma, 337, 11-21.
  • Hezarkhani, A. & Ghannadpour, S. S. (2015). Geochemical behavior investigation based on K-means clustering: basics, concepts and case study. LAP (Lambert Academic Publishing).
  • Khosravi, V., Shirazi, A., Shirazy, A., Hezarkhani, A., & Pour, A. B. (2022). Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran. Mining, 2(1), 1-12.‏
  • Khayer, K., Shirazy, A., Shirazi, A., Ansari, A., Nazerian, H. & Hezarkhani, A. (2021). Determination of Archie’s Tortuosity Factor from Stoneley Waves in Carbonate Reservoirs. International Journal of Science and Engineering Applications (IJSEA), 10, 107-110.
  • Krishna, K. & Murty, M. N. (1999). Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(3), 433-439.
  • Menard, S. (1995). An introduction to logistic regression diagnostics. Applied logistic regression analysis, 58-79. Moritz, R. (2016). The economic geology of Iran: mineral deposits and natural resources (M. Ghorbani). In: Society of Economic Geologists.
  • Murthy, C. A., & Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8), 825-832.
  • Pelleg, D. & Moore, A. W. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. Icml, Proceedings of the Seventeenth International Conference on Machine Learning.
  • Schalkoff, R. J. (1997). Artificial neural networks. McGraw-Hill Higher Education.
  • Shirazi, A., Hezarkhani, A., Shirazy, A. & Shahrood, I. (2018a). Exploration Geochemistry Data-Application for Cu Anomaly Separation Based On Classical and Modern Statistical Methods in South Khorasan, Iran. International Journal of Science and Engineering Applications, 7, 39-44.
  • Shirazi, A., Hezarkhani, A., Shirazy, A. & Shahrood, I. (2018b). Remote sensing studies for mapping of iron oxide regions, South of Kerman, Iran. International Journal of Science and Engineering Applications, 7(4), 45-51.
  • Shirazi, A., Shirazy, A. & Karami, J. (2018c). Remote sensing to identify copper alterations and promising regions, Sarbishe, South Khorasan, Iran. International Journal of Geology and Earth Sciences, 4(2), 36-52.
  • Shirazi, A., Shirazy, A., Saki, S. & Hezarkhani, A. (2018d). Geostatistics studies and geochemical modeling based on core data, sheytoor iron deposit, Iran. Journal of Geological Resource and Engineering, 6, 124-133.
  • Shirazi, A., Shirazy, A., Saki, S. & Hezarkhani, A. (2018e). Introducing a software for innovative neuro-fuzzy clustering method named NFCMR. Global Journal of Computer Sciences: theory and research, 8(2), 62-69.
  • Shirazy, A., Shirazi, A., Heidarlaki, S. & Ziaii, M. (2018a). Exploratory Remote Sensing Studies to Determine the Mineralization Zones around the Zarshuran Gold Mine. International Journal of Science and Engineering Applications, 7(9), 274-279.
  • Shirazy, A., Shirazi, A. & Hezarkhani, A. (2018b). Predicting gold grade in Tarq 1: 100,000 geochemical map using the behavior of gold, Arsenic and Antimony by K-means method. Journal of Mineral Resources Engineering, 2(4), 11-23.
  • Shirazy, A., Shirazi, A., Ferdossi, M. H. & Ziaii, M. (2019). Geochemical and geostatistical studies for estimating gold grade in tarq prospect area by k-means clustering method. Open Journal of Geology, 9(6), 306-326.
  • Shirazy, A., Ziaii, M. & Hezarkhani, A. (2020a). Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, Iran. Iranian Journal of Mining Engineering, 14(45), 96-112.
  • Shirazy, A., Ziaii, M., Hezarkhani, A. & Timkin, T. (2020b). Geostatistical and remote sensing studies to identify high metallogenic potential regions in the Kivi area of Iran. Minerals, 10(10), 869.
  • Shirazy, A., Ziaii, M., & Hezarkhani, A. (2021a). Geochemical behavior investigation based on k-means and artificial neural network prediction for titanium and zinc, Kivi region, Iran. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(3), 113-125.
  • Shirazy, A., Hezarkhani, A., Timkin, T. & Shirazi, A. (2021b). Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN)(Iron Ore Deposit, Yazd, IRAN). Minerals, 11(12), 1304.
  • Shirazy, A., Shirazi, A. & Nazerian, H. (2021c). Application of Remote Sensing in Earth Sciences–A Review. International Journal of Science and Engineering Applications, 10, 45-51.
  • Shirazy, A., Shirazi, A., Nazerian, H. & Hezarkhani, A. (2021d). Investigation of Geochemical Sections in Exploratory Boreholes of Mesgaran Copper Deposit in Iran. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 9(8), 2364-2368.
  • Shirazy, A., Shirazi, A., Nazerian, H. & Khayer, K. (2021e). Geophysical study: Estimation of deposit depth using gravimetric data and Euler method (Jalalabad iron mine, kerman province of IRAN). Open Journal of Geology, 11, 340-355.
  • Specht, D. F. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576. Tarkian, M. & Stribrny, B. (1999). Platinum-group elements in porphyry copper deposits: a reconnaissance study. Mineralogy and Petrology, 65(3-4), 161-183.
  • Ullman, J. (1984). A review of: “Pattern Recognition: A Statistical Approach”. By PA Devuver and J. Kittler. (London: Prentice Hall International, 1982.) [pp. 448.]. Remote Sensing, 5(2), 464-464.
  • Xu, Z. (2012). Priority weight intervals derived from intuitionistic multiplicative preference relations. IEEE Transactions on Fuzzy Systems, 21(4), 642-654.
  • Yaghini, M., & Gereilinia, N. (2013). Genetic TKM: A hybrid clustering method based on genetic algorithm, tabu search and k-means. International Journal of Applied Metaheuristic Computing (IJAMC), 4(1), 67-77. Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
There are 37 citations in total.

Details

Primary Language English
Subjects Geology (Other)
Journal Section Makaleler - Articles
Authors

Adel Shirazy 0000-0001-7756-3205

Ardeshir Hezarkhani 0000-0002-1149-3440

Aref Shirazi 0000-0001-7623-301X

Shayan Khakmardan 0000-0003-4359-9538

Reza Rooki This is me 0000-0002-4761-5518

Publication Date January 10, 2022
Submission Date October 16, 2021
Acceptance Date December 7, 2021
Published in Issue Year 2022 Volume: 65 Issue: 1

Cite

APA Shirazy, A., Hezarkhani, A., Shirazi, A., Khakmardan, S., et al. (2022). K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran. Türkiye Jeoloji Bülteni, 65(1), 79-92. https://doi.org/10.25288/tjb.1010636
AMA Shirazy A, Hezarkhani A, Shirazi A, Khakmardan S, Rooki R. K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran. Geol. Bull. Turkey. January 2022;65(1):79-92. doi:10.25288/tjb.1010636
Chicago Shirazy, Adel, Ardeshir Hezarkhani, Aref Shirazi, Shayan Khakmardan, and Reza Rooki. “K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization Probability in Chahar-Farsakh, Iran”. Türkiye Jeoloji Bülteni 65, no. 1 (January 2022): 79-92. https://doi.org/10.25288/tjb.1010636.
EndNote Shirazy A, Hezarkhani A, Shirazi A, Khakmardan S, Rooki R (January 1, 2022) K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran. Türkiye Jeoloji Bülteni 65 1 79–92.
IEEE A. Shirazy, A. Hezarkhani, A. Shirazi, S. Khakmardan, and R. Rooki, “K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran”, Geol. Bull. Turkey, vol. 65, no. 1, pp. 79–92, 2022, doi: 10.25288/tjb.1010636.
ISNAD Shirazy, Adel et al. “K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization Probability in Chahar-Farsakh, Iran”. Türkiye Jeoloji Bülteni 65/1 (January 2022), 79-92. https://doi.org/10.25288/tjb.1010636.
JAMA Shirazy A, Hezarkhani A, Shirazi A, Khakmardan S, Rooki R. K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran. Geol. Bull. Turkey. 2022;65:79–92.
MLA Shirazy, Adel et al. “K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization Probability in Chahar-Farsakh, Iran”. Türkiye Jeoloji Bülteni, vol. 65, no. 1, 2022, pp. 79-92, doi:10.25288/tjb.1010636.
Vancouver Shirazy A, Hezarkhani A, Shirazi A, Khakmardan S, Rooki R. K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Chahar-Farsakh, Iran. Geol. Bull. Turkey. 2022;65(1):79-92.

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