Quality analysis of machine learning methods applied to the geothermal potential assessment: a case study

ABSTRACT

Accurately determining the favorable areas of geothermal resources and selecting the target positions of exploration wells are extremely important for exploration and efficient development. This study used the Pearson correlation coefficient and Gini gain to analyze five influencing factors related to the presence of economically viable geothermal potential. The evaluation model of the favorable areas was constructed by using different Machine Learning (ML) methods: Bayesian classifier (Bayes), Support Vector Machine, Bootstrap Aggregating (Bagging), BP neural network, Decision Tree and Logistic Regression classification. The quality of each model was verified by statistical evaluation indicators: Accuracy (ACC), F₁ score (F₁) and Receiver Operating Characteristic curve (ROC curve). The methodology was applied to the case study of Xinjiang Uygur Autonomous Region, China. Due to the results obtained, all ML models showed strong prediction and classification performance on the target area selection of geothermal exploration, as evidenced by each model’s metrics: the ACC was above 80%, the F₁ was above 0.8, and the Area Under the ROC Curve (AUC) was greater than 0.85. The metrics obtained by the Bagging method were the highest. Finally, the results of the six ML models were combined to classify the study area’s geothermal potential, which was consistent with the available information. This study provides a specific basis and technical support for applying the method in further surveys and campaigns.

KEYWORDS:

• Geothermal resource
• geothermal exploration
• machine learning
• Xinjiang geothermal potential
• bootstrap aggregating

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contribution

Conceptualization, software and methodology, X. C. (Xianggang Cheng); Data analysis and guide, W. Q. (Wei Qiao) and D. H. (Dongqiang Hu); Data collection and curation, D. H. and Z. Q. (Zhilong Qi); Drawing and revise, Peichao Feng (P. F.) and X. C., Supervise, review and revise, W. Q. and F. T. (Francesco Tinti).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Additional information

Funding

The Fundamental Research Funds of the National Natural Science Foundation of China (Gran 41772302) and the China Scholarship Council (Grant 202206420039) supported the research. The author thanks the many scholars was cited in our study for their research achievements and the reviewers for their helpful comments.

Notes on contributors

Xianggang Cheng

Xianggang Cheng currently studying at the University of Bologna and pursuing as a Ph.D. candidate student at China University of Mining and Technology. He is mainly engaged in energy and engineering geology research such as rock burst, geothermal resource exploration, mining-induced seismicity, in-situ stress inversion and machine learning, etc.

Wei Qiao

Wei Qiao is a professor at China University of Mining and Technology, deputy director of the Geothermal Resources Research Center of CUMT. His interest areas include engineering geology and hydrogeology, and mine water disaster prevention theory and technology, geothermal resource development and exploration, geological disaster intelligent early warning and monitoring, etc.

Dongqiang Hu

Dongqiang Hu is an associate professor at the Xinjiang Institute of Engineering, mainly engaged in research on the mineralization rules and mineralization prediction of mineral resources.

Zhilong Qi

Zhilong Qi is a senior engineer of of the Xinjiang Bureau of Geology and Mineral Resources Exploration and Development, mainly engaged in research on geothermal resource development and exploration.

Peichao Feng

Peichao Feng is a Ph.D. candidate student at China University of Mining and Technology. He is mainly engaged in research on geothermal resource development and exploration, rock mechanics and engineering geology.

Francesco Tinti

Francesco Tinti is a senior assistant professor (fixed-term) at University of Bologna. He is PhD in Geoengineering, Georesources and Geotechnical Engineering. His main research activity deals with georeosurces and geothermal sectors. He has worked in several projects of international cooperation for the development and use of geothermal energy.