Classification of Volcanic Rocks based on Rough Set Theory
Classification of volcanic rocks is a fundamental task in the geologic studies. Volcanic rocks are igneous rocks that cooled rapidly above the surface of the Earth's crust. They are classified according to their oxide chemical content. Furthermore, volcanic rocks can also be classified numerically by statistical means. But these methods are mostly dependent on human expert decision making and have a high cost. In this paper, a novel approach in the classification of volcanic rocks is proposed. This method is based on the rough set mathematical theory. The continuous data of the information system are firstly discretized using the information loss method. Secondly, the discretized decision table is reduced and the decision rule sets are extracted. The results are consistent with previous methods and show that the proposed method reduces time and calculation costs.
Keywords:decision rules, information loss-discretization, rough set, volcanic rocks
M. Meschede, “A method of discriminating between different types of midocean ridge basalts and continental tholeiites with the Nb-Zr-Y diagram”, Chemical Geology, Vol. 56, No. 3-4, pp. 207-218, 1986 DOI: https://doi.org/10.1016/0009-2541(86)90004-5
M. J. Le Bas, A. L. Streckeisen, “The IUGS systematics of igneous rocks”, Journal of the Geological Society, Vol. 148, No. 5, pp. 825-833, 1991 DOI: https://doi.org/10.1144/gsjgs.148.5.0825
B. R. Frost, C. D. Frost, “A geochemical classification for feldspathic igneous rocks”, Journal of Petrology, Vol. 49, No. 11, pp. 1955-1969, 2008 DOI: https://doi.org/10.1093/petrology/egn054
F. Tiecher, M. E. B. Gomes, D. C. C. Dal Molin, “Alkali-aggregate reaction: A study of the influence of the petrographic characteristics of volcanic rocks”, Engineering, Technology & Applied Science Research, Vol. 8, No. 1, pp. 2399-2404, 2018 DOI: https://doi.org/10.48084/etasr.1731
Z. Wei, H. Hu, H. W. Zhou, A. Lau, “Characterizing rock facies using machine learning algorithm based on a convolutional neural network and data padding strategy”, Pure and Applied Geophysics, Vol. 176, No. 8, pp. 3593-3605, 2019 DOI: https://doi.org/10.1007/s00024-019-02152-0
G. Cheng, J. Yang, Q. Huang, Y. Liu, “Rock image classification recognition based on probabilistic neural networks”, Science Technology and Engineering, Vol. 13, pp. 9231-9235, 2013
G. Cheng, W. Guo, P. Fan, “Study on rock image classification based on convolution neural network”, Journal of Xi'an Shiyou University (Natural Science Edition), Vol. 32, No. 4, pp. 116-122, 2017
Y. Pu, D. B. Apel, B. Lingga, “Rockburst prediction in kimberlite using decision tree with incomplete data”, Journal of Sustainable Mining, Vol. 17, pp. 158-165, 2018 DOI: https://doi.org/10.1016/j.jsm.2018.07.004
Y. Pu, D. B. Apel, B. Lingga, “Regression analysis and neural network fitting of rock mass classification systems”, Journal of Science and Engineering, Vol. 20, No. 59, pp. 354-368, 2018 DOI: https://doi.org/10.21205/deufmd.2018205929
R. W. Le Maitre, “A new approach to the classification of igneous rocks using the basalt-andesite-dacite-rhyolite suite as an example”, Contributions to Mineralogy and Petrology, Vol. 56, pp. 191-203, 1976 DOI: https://doi.org/10.1007/BF00399604
T. Miranda, L. R. Sousa, A. T. Gomes, J. Tinoco, C. Ferreira, “Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques”, Journal of Rock Mechanics and Geotechnical Engineering, Vol. 10, No. 1, pp. 138-150, 2018 DOI: https://doi.org/10.1016/j.jrmge.2017.11.003
C. A. Ozturk, E. Nasuf , “Strength classification of rock material based on textural properties”, Tunnelling and Underground Space Technology, Vol. 37, pp. 45-54, 2013 DOI: https://doi.org/10.1016/j.tust.2013.03.005
Z. Pawlak, Rough sets: Theoretical aspects of reasoning about data, Kluwer Academic Publishers, 1991 DOI: https://doi.org/10.1007/978-94-011-3534-4
S. M. Shaaban, “Application rough set theory and decision network as a new approach to simplify airline hubs network location”, International Journal of Intelligent Engineering and Systems, Vol. 9, No. 2, pp. 1-7, 2016 DOI: https://doi.org/10.22266/ijies2016.0630.01
S. M. Shaaban, H. A. Nabwey, “Rehabilitation and reconstruction of asphalts pavement decision making based on rough set theory”, Computational Science and its Applications–ICCSA 2012, Lecture Notes in Computer Science, Vol. 7334, pp. 316-330, Springer, 2012 DOI: https://doi.org/10.1007/978-3-642-31075-1_24
W. M. A. W. Ahmad, R. A. A. Rohim, N. H. Ismail, “Forecasting parameter estimates: A modeling approach using exponential and linear regression”, Engineering, Technology & Applied Science Research, Vol. 8, No. 4, pp. 3162-3167, 2018 DOI: https://doi.org/10.48084/etasr.2150
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