Abstract
The identification of lithologies is a crucial task in continental scientific drilling research. In fact, in complex geological situations such as crystalline rocks, more complex nonlinear functional behaviors exist in well log interpretation/classification purposes; thus posing challenges in accurate identification of lithology using geophysical log data in the context of crystalline rocks. The aim of this work is to explore the capability of k-nearest neighbors classifier and to demonstrate its performance in comparison with other classifiers in the context of crystalline rocks. The results show that best classifier was neural network followed by support vector machine and k-nearest neighbors. These intelligence machine learning methods appear to be promising in recognizing lithology and can be a very useful tool to facilitate the task of geophysicists allowing them to quickly get the nature of all the geological units during exploration phase.
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References
Amirgaliev, E., Isabaev, Z., Iskakov, S., Kuchin, Y., et al.: Recognition of rocks at uranium deposits by using a few methods of machine Learning. Soft computing in machine learning. Advances in intelligent systems and computing 273, 33–40 (2014)
Bartetzko, A., Delius, H., Pechnig, R.: Effect of compositional and structural variations on log responses of igneous and metamorphic rocks. I: mafic rocks. In: Harvey, P.K., Brewer, T.S., Pezard, P.A., Petrov, V.A. (eds.) Petrophysical Properties of Crystalline Rocks, pp. 255–278. Geological Society, London, Special Publications (2005)
Cheriet, M., Kharma, N., Liu, C.L., Suen, C.Y.: Character recognition systems: a guide for students and practioners. Published by John Wiley & Sons Inc, Hoboken (2007)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Professional Inc, San Diego (1990)
Gelfort, R.: On classification of logging data. Ph.D. thesis, Clausthal University of Technology, Germany (2006)
Kassenaar, J.D.C.: An application of principal components analysis to borehole geophysical data. In: Proceedings of the Fourth International Symposium on Borehole Geophysics for Minerals, Geotechnical and Groundwater Applications, Toronto, Ontario, pp. 211–218 (1991)
Luo, M., Pan, H.P.: Well Logging Responses of UHP Metamorphic Rocks from CCSD Main Hole in Sulu Terrane, Eastern Central China. Journal of Earth Science 21(3), 347–357 (2010)
Maiti, S., Tiwari, R.K.: A hybrid Monte Carlo method based artificial neural networks approach for rock boundaries identification: a case study from KTB Borehole. Pure Appl Geophysics 166, 2059–2090 (2009)
Niu, X.Y., Pan, H.P., Wang, W.X., Zhu, L.F., Xu, D.H.: Geophysical Well Logging in Main Hole (0–2 000 m) of Chinese Continental Scientific Drilling. Acta petrologica Sinica 20(1), 109–118 (2004)
Pan, H.P., Luo, M., Zhao, Y.G.: Identification of metamorphic rocks in the CCSD main hole. In: IEEE Sixth International Conference on Natural Computation (ICNC), pp. 4049–4051 (2010)
Pechnig, R., Delius, H., Bartetzko, A.: Effect of compositional variations on log responses of igneous and metamorphic rocks. II: acid and intermediate rocks. In: Harvey, P.K., Brewer, T.S., Pezard, P.A., Petrov, V.A. (eds.) Petrophysical Properties of Crystalline Rocks, pp. 279–300. Geological Society, London, Special Publications (2005)
Stone, M.: Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society. 36(2), 111–147 (1974)
Saggaf, M.M., Nebrija, E.L.: Estimation of Lithologies and Depositional Facies fromWire-Line. Logs AAPG Bulletin. 84, 1633–1646 (2000)
Yazdani, A., Ebrahimi, T., Hoffmann, U.: Classification of EEG signals using dempster shafer theory and a K-nearest neighbor classifier. In: Proc of the 4th int IEEE EMBS Conf on Neural Engineering, pp. 327–30 (2009)
Bosch, D., Ledo, J., Queralt, P.: Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set. Surveys in Geophysics 34(4), 413–439 (2009)
Ji, S.C., Xu, Z.Q.: Drilling deep into the ultrahigh pressure (UHP) metamorphic terrane. Tectonophysics 475, 201–203 (2009)
Abe, S.: Support Vector Machines for Pattern Classification, Advances in Pattern Recognition, 2nd edn. Springer (2010). doi:10.1007/978-1-84996-098-4_7
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford Press (1995)
Ehret, B.: Pattern recognition of geophysical Data. Geoderma, 111–125 (2010)
Sandham, W., Leggett, M. (eds.): Geophysical Applications of Artificial Neural Networks and Fuzzy Logic. Series: Modern Approaches in Geophysics (2003)
Li, Y., Bian, Z., Yan, P., Chang, T.: Pattern recognition in geophysical signal processing and Interpretation. Handbook of Pattern Recognition and Computer Vision, 511–539 (1993)
Aminzadeh, F. (ed): Handbook of Geophysical Exploration: Section I. Seismic Exploration, 20, Pattern Recognition & Image Processing, Geophysical Press, London (1987)
Palaz, I., Sengupta, S.K. (eds.) Automated Pattern Analysis in Petroleum Exploration. Springer New York (1992)
Xu, Z.Q., Wang, Q., Tang, Z., Chen, F.: Fabric kinematics of the ultrahigh-pressure metamorphic rocks from the main borehole of the Chinese Continental Scientific Drilling Project: Implications for continental subduction and exhumation. Tectonophysics 475, 235–250 (2009)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 6, 525–533 (1993)
May, R.J., Maier, H.R., Dandy, G.C.: Data splitting for artificial neural networks using SOM- based stratified sampling. Neural Networks 23(2), 283–294 (2010)
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Konaté, A.A. et al. (2015). Machine Learning Interpretation of Conventional Well Logs in Crystalline Rocks. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_39
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