Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning

Yongjie Ma (), Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan and Yanjun Zhang
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Yongjie Ma: PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China
Lin Tian: PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China
Fuhang Hu: PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China
Jingyong Wang: PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China
Echuan Yan: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Yanjun Zhang: College of Construction Engineering, Jilin University, Changchun 130026, China

Energies, 2025, vol. 18, issue 15, 1-17

Abstract: With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R 2 increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R 2 reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development.

Keywords: geothermal energy; thermal conductivity prediction; data augmentation; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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