Tectonic discrimination of olivine in basalt using data mining techniques based on major elements: a comparative study from multiple perspectives

References

• Abdel-Zaher, A. M., & Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139–144.
   Web of Science ®Google Scholar
• Arevalo, R., McDonough, W. F., & Luong, M. (2009). The K/U ratio of the silicate Earth: Insights into mantle composition, structure and thermal evolution. Earth and Planetary Science Letters, 278(3), 361–369.
   Web of Science ®Google Scholar
• Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.
   Google Scholar
• Bhatia, M. R., & Crook, K. A. (1986). Trace element characteristics of graywackes and tectonic setting discrimination of sedimentary basins. Contributions to Mineralogy and Petrology, 92(2), 181–193.
   Web of Science ®Google Scholar
• Bo, L., Wang, L., & Jiao, L. (2006). Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation. Neural Computation, 18(4), 961–978.
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
   Web of Science ®Google Scholar
• Di, P., Wang, J., Zhang, Q., Yang, J., Chen, W., Pan, Z., … Jiao, S. (2017). The evaluation of basalt tectonic discrimination diagrams: Constraints on the research of global basalt data. Bulletin of Mineralogy, Petrology and Geochemistry, 36(6), 891–896.
   Google Scholar
• Donders, A. R. T., Van Der Heijden, G. J., Stijnen, T., & Moons, K. G. (2006). A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087–1091.
   PubMed Web of Science ®Google Scholar
• Enciso-Maldonado, L., Dyer, M. S., Jones, M. D., Li, M., Payne, J. L., Pitcher, M. J., … Rosseinsky, M. J. (2015). Computational identification and experimental realization of lithium vacancy introduction into the olivine LiMgPO4. Chemistry of Materials, 27(6), 2074–2091.
   Web of Science ®Google Scholar
• Frane, J. W. (1976). Some simple procedures for handling missing data in multivariate analysis. Psychometrika, 41(3), 409–415.
   Web of Science ®Google Scholar
• García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: Methods and prospects. Big Data Analytics, 1(1), 9.
   Google Scholar
• Golub, G. H., Heath, M., & Wahba, G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215–223.
   Web of Science ®Google Scholar
• Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2), 83–90.
   Web of Science ®Google Scholar
• Han, S., Li, M., Ren, Q., & Liu, C. (2018). Intelligent determination and data mining for tectonic settings of basalts based on big data methods. Acta Petrologica Sinica, 34(11), 3207–3216.
   Web of Science ®Google Scholar
• Hannan, M. A., Arebey, M., Begum, R. A., Mustafa, A., & Basri, H. (2013). An automated solid waste bin level detection system using Gabor wavelet filters and multi-layer perception. Resources, Conservation and Recycling, 72, 33–42.
   Web of Science ®Google Scholar
• Howarth, G. H., & Harris, C. (2017). Discriminating between pyroxenite and peridotite sources for continental flood basalts (CFB) in southern Africa using olivine chemistry. Earth and Planetary Science Letters, 475, 143–151.
   Web of Science ®Google Scholar
• Jain, Y. K., & Bhandare, S. K. (2011). Min max normalization based data perturbation method for privacy protection. International Journal of Computer and Communication Technology, 2(8), 45–50.
   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., 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
• Longstaff, I. D., & Cross, J. F. (1987). A pattern recognition approach to understanding the multi-layer perception. Pattern Recognition Letters, 5(5), 315–319.
   Web of Science ®Google Scholar
• Luo, J., Wang, X., Song, B., Yang, Z., Zhang, Q., Zhao, Y., & Liu, S. (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
• Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., & Hamprecht, F. A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics, 10(1), 213.
   PubMedGoogle Scholar
• Mitchell, T. M. (2005). Generative and discriminative classifiers: Naive Bayes and logistic regression. Machine Learning. New York: McGraw Hill.
   Google Scholar
• Patil, T. R., & Sherekar, S. S. (2013). Performance analysis of Naive Bayes and J48 classification algorithm for data classification. International Journal of Computer Science and Applications, 6(2), 256–261.
   Google Scholar
• Pearce, J. A. (1996). A user’s guide to basalt discrimination diagrams. Trace element geochemistry of volcanic rocks: Applications for massive sulphide exploration. Geological Association of Canada, Short Course Notes, 12, 79–113.
   Google Scholar
• Pearce, J. A., & Cann, J. R. (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
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(10), 2825–2830.
   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
• Ren, Q., Wang, G., Li, M., & Han, S. (2019). Prediction of rock compressive strength using machine learning algorithms based on spectrum analysis of geological hammer. Geotechnical and Geological Engineering, 37(1), 475–489.
   Web of Science ®Google Scholar
• Sánchez-Muñoz, L., Müller, A., Andrés, S. L., Martin, R. F., Modreski, P. J., & de Moura, O. J. (2017). The P-Fe diagram for K-feldspars: A preliminary approach in the discrimination of pegmatites. Lithos, 272, 116–127.
   Web of Science ®Google Scholar
• Subasi, A., & Ercelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87–99.
   PubMed Web of Science ®Google Scholar
• Taalab, K., Cheng, T., & Zhang, Y. (2018). Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159–178.
   Google Scholar
• Townsend, J. T. (1971). Theoretical analysis of an alphabetic confusion matrix. Perception & Psychophysics, 9(1), 40–50.
   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., Chen, W., Zhang, Q., Jiao, S., 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
• Zhang, Q. (1990). The correct use of the basalt discrimination diagram. Acta Petrologica Sinica, 2, 87–94.
   Google Scholar
• Zhou, X., Chen, Z., & Chen, T. (1980). Composition and evolution of olivine and pyroxene in alkali basaltic rocks from Jiangsu Province. Geochimica, 33(3), 253–262.
   Google Scholar
• Zhou, Y., Chen, S., Zhang, Q., Xiao, F., Wang, S., Liu, Y., & Jiao, S. (2018). Advances and prospects of big data and mathematical geoscience. Acta Petrologica Sinica, 34(2), 255–263.
   Web of Science ®Google Scholar