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Studying petrophysical properties of micritic limestones using machine learning methods

Nguyen-Sy, T. and Vu, M.-N. and Tran-Le, A.-D. and Tran, B.-V. and Nguyen, T.-T.-N. and Nguyen, T.-T. (2021) Studying petrophysical properties of micritic limestones using machine learning methods. Journal of Applied Geophysics, 184: 104226. ISSN 9269851

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Abstract

It is important in geophysical applications to relate the compressional and shear ultrasonic wave velocities of micritic limestone to its porosity, volume fraction and density of micrite grains as well as the effective confining pressure. In this paper, this difficulty task is successfully realized by using the most relevant machine learning methods: The artificial neural network method, the support vector machine method and the extreme gradient boosting method (XGB). A relevant dataset available in literature is considered to train and test the models. It is observed that the XGB method significantly outperform the other methods in term of accuracy and training time. It allow obtaining a very high R-squared value of 0.96 and a very small relative root mean squared error of 3% while predicting the sonic velocities from other petrophysical properties. The robustness of the models is also confirmed by studying the sensitivity of the random splittings between the training and the testing sets © 2020 Elsevier B.V.

Item Type: Article
Divisions: Institutes > Institute of Techniques for Special Engineering
Identification Number: 10.1016/j.jappgeo.2020.104226
Uncontrolled Keywords: Adaptive boosting; Lime; Limestone; Mean square error; Neural networks; Petrophysics; Shear flow; Statistical tests; Support vector machines; Artificial neural network methods; Confining pressures; Geophysical applications; Gradient boosting; Machine learning methods; Petrophysical properties; Root mean squared errors; Support vector machine method; Learning systems; artificial neural network; carbonate rock; data set; limestone; machine learning; micrite; support vector machine
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8822

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