Journal of Jilin University(Earth Science Edition) ›› 2016, Vol. 46 ›› Issue (3): 855-864.doi: 10.13278/j.cnki.jjuese.201603207

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Prediction Models of Deep Hydrothemal Geothermal Potential Areas Based on GIS: A Case Study of Western Anatolia, Turkey

Zhang Yanjun1,2, Yu Hai1, Li Jianming1, Yu Ziwang1, Zhang jianing1   

  1. 1. College of Construction Engineering, Jilin University, Changchun 130026, China;
    2. Key Lab of Groundwater Resource and Environment Ministry of Education, Jilin University, Changchun 130026, China
  • Received:2015-10-11 Online:2016-05-26 Published:2016-05-26
  • Supported by:

    Supported by National High-Tech R&D Program of China ("863" Program)(2012AA052801), National Natural Science Foundation of China(41372239) and Research Fund for the Doctoral Program of Higher Education of China(20110061110055)

Abstract:

Geothermal exploration involves a high degree of uncertainty and financial risk, and requires reliable prediction model to identify geothermal potential areas. Mathematical prediction models were established by integrating five factors that closely related to geothermal, including earthquake epicenter, lineament, Bouger gravity anomaly, magnetic anomaly and infrared remote sensing; and the advantages and disadvantages of models were compared by an example of western Anatolia, Turkey. Index overlay(IO) and weights of evidence(WofE) were applied to establish geothermal resource potential maps, respectively. Success indices suggest that the predicting outcomes of WofE are more reliable than IO. The very favorable area and the favorable area are about 26 529 km2, accounting for 31.14% of total area, including 39 geothermal resource potential sites, which is 50.65% of the total sites.The potential areas in the WofE map are Ayd?n, Denizli, Manisa and Bal?kesir, of which the former two have been explored and exploited. This study is helpful to locate geothermal potential areas and reduce financial risk in the process of geothermal exploration.

Key words: hydrothemal geothermal resource potential, geophysical characteristics, multicriteria decision analysis, prediction models based on GIS

CLC Number: 

  • P314.2

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