Discriminating among tectonic settings of spinel based on multiple machine learning algorithms

References

• Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185.
   Web of Science ®Google Scholar
• Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016). Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(5), 971–989.
   PubMed Web of Science ®Google Scholar
• Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012). The K’in k-fold cross validation. ESANN 2012 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 441–446). Bruges, Belgium.
   Google Scholar
• Arai, S. (1994). Characterization of spinel peridotites by olivine-spinel compositional relationships: Review and interpretation. Chemical Geology, 113(3–4), 191–204.
   Web of Science ®Google Scholar
• Arai, S., Okamura, H., Kadoshima, K., Tanaka, C., Suzuki, K., & Ishimaru, S. (2011). Chemical characteristics of chromian spinel in plutonic rocks: Implications for deep magma processes and discrimination of tectonic setting. Island Arc, 20(1), 125–137.
   Web of Science ®Google Scholar
• Barnes, S. J., & Roeder, P. L. (2001). The range of spinel compositions in terrestrial mafic and ultramafic rocks. Journal of Petrology., 42(12), 2279–2302.
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
   Web of Science ®Google Scholar
• Dharma Rao, C. V., Santosh, M., Kim, S. W., & Li, S. (2013). Arc magmatism in the Delhi fold belt: Shrimp u-pb zircon ages of granitoids and implications for neoproterozoic convergent margin tectonics in nw India. Journal of Asian Earth Sciences, 78(12), 83–99.
   Google Scholar
• Franz, L., & Wirth, R. (2000). Spinel inclusions in olivine of peridotite xenoliths from TUBAF seamount (Bismarck Archipelago/Papua New Guinea): Evidence for the thermal and tectonic evolution of oceanic lithosphere. Contributions to Mineralogy and Petrology, 140, 283–295.
   Web of Science ®Google Scholar
• Gavankar, S., & Sawarkar, S. (2015). Decision tree: Review of techniques for missing values at training, testing and compatibility. Artificial Intelligence, Modelling and Simulation (AIMS), 2015 3rd International Conference on Kota Kinabalu, State of Sabah, Island of Borneo, Malaysia.
   Google Scholar
• Gönen, M. (2006). Receiver operating characteristic (ROC) curves. SAS Users Group International, 31, 210–231.
   Google Scholar
• Ishwar-Kumar, C., Rajesh, V. J., Windley, B. F., Razakamanana, T., Itaya, T., Babu, E. V. S. S. K., & Sajeev, K. (2016). Petrogenesis and tectonic setting of the Bondla mafic-ultramafic complex, western India: Inferences from chromian spinel chemistry. Journal of Asian Earth Sciences, 130, 192–205.
   Web of Science ®Google Scholar
• Jan, M. Q., & Windley, B. F. (1990). Chromian spinel-silicate chemistry in ultramafic rocks of the Jijal Complex, northwest Pakistan. Journal of Petrology, 31, 67–71.
   Web of Science ®Google Scholar
• Kamenetsky, V., Crawford, A. J., & Meffre, S. (2001). Factors controlling chemistry of magmatic spinel: An empirical study of associated olivine, Cr-spinel and melt inclusions from primitive rocks. Journal of Petrology, 42, 655–671.
   Web of Science ®Google Scholar
• Leterrier, J., Maury, R. C., Thonon, P., Girard, D., & Marchal, M. (1982). Clinopyroxene composition as a method of identification of the magmatic affinities of paleo-volcanic series. Earth and Planetary Science Letters, 59(1), 139–154.
   Web of Science ®Google Scholar
• Li, C., Arndt, N. T., Tang, Q., & Ripley, E. M. (2015). Trace element indiscrimination diagrams. Lithos, 232, 76–83.
   Web of Science ®Google Scholar
• Li, M. C., Miao, L., & Shi, J. (2014). Analyzing heating equipment’s operations based on measured data. Energy and Buildings, 82, 47–56.
   Web of Science ®Google Scholar
• Luo, J. M., Wang, X. W., Song, B. T., Yang, Z. M., Zhang, Q., Zhao, Y. Q., & Liu, S. Y. (2018). Discussion on the method for quantitative classification of magmatic rocks: Taking it’s application in West Qinling of Gansu Province for example. Acta Petrologica Sinica, 34(2), 326–332.
   Web of Science ®Google Scholar
• Maurel, C., & Maurel, P. (1982). Étude experimental de la distribution de l’aluminium entre bain silicate basique et spinel chromifère. Implications pétrogenetiques: Tenore en chrome des spinelles. Bull Minéral, 105, 197–202.
   Google Scholar
• Oh, C. W., Seo, J., Choi, S. G., Rajesh, V. J., & Lee, J. H. (2012). U-Pb SHRIMP zircon geochronology, petrogenesis, and tectonic setting of the Neoproterozoic Baekdong ultramafic rocks in the Hongseong Collision belt, South Korea. Lithos, 128(1), 100–112.
   Google Scholar
• Pearce, J., & Cann, J. (1971). Ophiolite origin investigated by discriminant analysis using Ti, Zr and Y. Earth and Planetary Science Letters, 12(3), 339–349.
   Web of Science ®Google Scholar
• Pearce, J. A., & Cann, J. (1973). Tectonic setting of basic volcanic rocks determined using trace element analyses. Earth and Planetary Science Letters, 19(2), 290–300.
   Web of Science ®Google Scholar
• Pearce, J. A., & Norry, M. J. (1979). Petrogenetic implications of Ti, Zr, Y, and Nb variations in volcanic rocks. Contributions to Mineralogy and Petrology, 69(1), 33–47.
   Web of Science ®Google Scholar
• Petrelli, M., & Perugini, D. (2016). Solving petrological problems through machine learning: The study case of tectonic discrimination using geochemical and isotopic data. Contributions to Mineralogy and Petrology, 171(10), 81.
   Web of Science ®Google Scholar
• Polikar, R. (2012). Ensemble learning. In C. Zhang & Y. Ma (Eds.), Ensemble machine learning (pp. 1–34). Springer.
   Google Scholar
• Swets, J. A. (1986). Indices of discrimination or diagnostic accuracy: Their ROCs and implied models. Psychological Bulletin, 99(1), 100–117.
   PubMed Web of Science ®Google Scholar
• Tamura, A., & Arai, S. (2006). Harzburgite-dunite-orthopyroxenite suite as a record of supra-subduction zone setting for the Oman ophiolite mantle. Lithos, 90, 43–56.
   Web of Science ®Google Scholar
• Ueki, K., Hino, H., & Kuwatani, T. (2018). Geochemical discrimination and characteristics of magmatic tectonic settings: A machine‐learning‐based approach. Geochemistry, Geophysics, Geosystems, 19(4), 1327–1347.
   Web of Science ®Google Scholar
• Vermeesch, P. (2006). Tectonic discrimination of basalts with classification trees. Geochimica et Cosmochimica Acta, 70(7), 1839–1848.
   Web of Science ®Google Scholar
• Wang, J. R., Chen, W. F., Zhang, Q., Jiao, S. T., Yang, J., Pan, Z., & Wang, S. (2017). Preliminary research on data mining of n-morb and e-morb: Discussion on method of the basalt discrimination diagrams and the character of MORB’s mantle source. Acta Petrologica Sinica, 33(3), 993–1005.
   Web of Science ®Google Scholar
• Wang, J. R., Pan, Z. J., Zhang, Q., Chen, W. F., Yang, J., Jiao, S. T., & Wang, S. H. (2016). Intra-continental basalt data mining: The diversity of their constituents and the performance in basalt discrimination diagrams. Acta Petrologica Sinica, 32(7), 1919–1933.
   Web of Science ®Google Scholar
• Wang, Y. (2001). Th/Hf-Ta/Hf identification of tectonic setting of basalts. Acta Petrologica Sinica, 17, 413–421.
   Web of Science ®Google Scholar
• Welling, M. (2005). Fisher linear discriminant analysis. Department of Computer Science, University of Toronto . 3 (1)
   Google Scholar
• Zhang, Q. (1990). The correct use of the basalt discrimination diagram. Acta Petrologica Sinica, 2, 87–94.
   Google Scholar
• Zhao, Z. H. (2016). Discrimination of tectonic settings based on trace elements in igneous minerals. Geotectonica et Metallogenia, 40(5), 986–995.
   Google Scholar
• Zhou, E. B., Yang, Z. S., Jiang, W., Hou, Z. Q., Guo, F. S., & Hong, J. (2011). Study on mineralogy of Cr-spinel and genesis of Luobusha chromite deposit in South Tibet. Acta Petrologica Sinica, 27(7), 2060–2072.
   Web of Science ®Google Scholar
• Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361.
   Web of Science ®Google Scholar
• Zhou, Y. Z., Chen, S., Zhang, Q., Xiao, F., Wang, S. G., Liu, Y. P., & Jiao, S. T. (2018). Advances and prospects of big data and mathematical geoscience. Acta Petrologica Sinica, 34(2), 255–263.
   Web of Science ®Google Scholar