ISSN 0021-3454 (print version)
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vol 67 / February, 2024
Article

DOI 10.17586/0021-3454-2021-64-7-532-541

UDC 550.8.072

APPLYING MACHINE LEARNING METHODS TO PREDICT OR REPLACE MISSING LOGGING DATA

R. D. Akhmetsafin
MIPT Engineering Center for Minerals, Department of Digital Technologies in Industry; Associate Professor


R. Z. Akhmetsafina
National Research University “Higher School of Economicsˮ, Moscow;


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Abstract. Nine machine learning methods (ANN, ANFIS, ELM, FM, SVM, GPR, RF, RT, k-NN) are compared using the example of predicting acoustic logging data. With machine learning, the solution to the regression problem can be used not only for predicting geophysical fields, but also for filing in missing data. The constructed curve T(Р) of the P-wave interval time can be considered as a forecasted result, if acoustic logging is expected later; if additional acoustic logging is not possible, then the synthetic curve T(Р) replaces the log-derived one for further interpretation. The RF method is shown to provide the best test results.
Keywords: machine learning, regression problem, missing data replacement, acoustic logging

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