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Geothermal resource potential assessment of Fujian Province, China, based on geographic information system (GIS) -supported models

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  • Zhang, Yu
  • Zhang, Yanjun
  • Yu, Hai
  • Li, Jianming
  • Xie, Yangyang
  • Lei, Zhihong

Abstract

Potential geothermal areas were identified and classified according to geothermal, geological and geophysical spatial associations in Fujian province, China. Five publicly available datasets were used in this study: seismic activity, geomagnetism, fault distribution, intrusive rock distribution, and Bouguer gravity anomaly, which were digitally processed into five impact factors maps: Gutenberg-Richter b-value, magnetic anomaly, distance to faults, distance to intrusive rock and distance to major grabens, respectively. Based on the geographic information system supported weight-of-evidence model and fuzzy logic model, the geothermal prediction maps were established using the impact factors maps. The prediction maps divide the potential geothermal areas of Fujian province into four grades, where the extremely high potential geothermal area covers 14,000 km2, accounting for 11.54% of the total area and the developed geothermal area is in good agreement with the extremely high potential area. The results reveal that the weight-of-evidence model is more accurate, and the application of each fuzzy operator in the process of fuzzy synthesis of the fuzzy logic model has a certain subjectivity, which requires higher professional knowledge to drive. It is anticipated that this paper will guide further investigations in Fujian province and guide the study of delineating potential geothermal regions with limited information.

Suggested Citation

  • Zhang, Yu & Zhang, Yanjun & Yu, Hai & Li, Jianming & Xie, Yangyang & Lei, Zhihong, 2020. "Geothermal resource potential assessment of Fujian Province, China, based on geographic information system (GIS) -supported models," Renewable Energy, Elsevier, vol. 153(C), pages 564-579.
    • Handle: RePEc:eee:renene:v:153:y:2020:i:c:p:564-579
    DOI: 10.1016/j.renene.2020.02.044
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    References listed on IDEAS

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    3. Elkadeem, Mohamed R. & Younes, Ali & Mazzeo, Domenico & Jurasz, Jakub & Elia Campana, Pietro & Sharshir, Swellam W. & Alaam, Mohamed A., 2022. "Geospatial-assisted multi-criterion analysis of solar and wind power geographical-technical-economic potential assessment," Applied Energy, Elsevier, vol. 322(C).
    4. Yongzhu Xiong & Mingyong Zhu & Yongyi Li & Kekun Huang & Yankui Chen & Jingqing Liao, 2022. "Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning," Energies, MDPI, vol. 15(8), pages 1-29, April.
    5. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    6. Hu, Ziyuan & Gao, Zongjun & Xu, Xiqiang & Fang, Shaoyan & Zhou, Liangyu & Ji, Deshuai & Li, Fuquan & Feng, Jianguo & Wang, Min, 2022. "Suitability zoning of buried pipe ground source heat pump and shallow geothermal resource evaluation of Linqu County, Shandong Province, China," Renewable Energy, Elsevier, vol. 198(C), pages 1430-1439.

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