Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China
The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant, but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms, six impact factors, name...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2061055 |
| _version_ | 1827811143456915456 |
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| author | Zhe Chen Ruichun Chang Wenbo Zhao Sijia Li Huadong Guo Keyan Xiao Lin Wu Dong Hou Lu Zou |
| author_facet | Zhe Chen Ruichun Chang Wenbo Zhao Sijia Li Huadong Guo Keyan Xiao Lin Wu Dong Hou Lu Zou |
| author_sort | Zhe Chen |
| collection | DOAJ |
| description | The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant, but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms, six impact factors, namely land surface temperature, fault density, Gutenberg–Liszt B value, formation combination entropy, distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF), weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF), the geothermal potential in the study area were predicted quantitatively. Based on the ECF results, the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%, area ratio is 0.867); (2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan, Panzhihua–Liangshan, Dali–Chuxiong, Nujiang–Baoshan, Diqing–Dali, and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt. |
| first_indexed | 2024-03-11T23:00:59Z |
| format | Article |
| id | doaj.art-dfed28d1b18b4235b00eb5544cbd05c9 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:59Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-dfed28d1b18b4235b00eb5544cbd05c92023-09-21T14:57:11ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552022-12-0115174876910.1080/17538947.2022.20610552061055Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful ChinaZhe Chen0Ruichun Chang1Wenbo Zhao2Sijia Li3Huadong Guo4Keyan Xiao5Lin Wu6Dong Hou7Lu Zou8Chengdu University of TechnologyChengdu University of TechnologyAerospace Information Research InstituteChengdu University of TechnologyChengdu University of TechnologyChengdu University of TechnologyChengdu University of TechnologyChengdu University of TechnologyChengdu University of TechnologyThe geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant, but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms, six impact factors, namely land surface temperature, fault density, Gutenberg–Liszt B value, formation combination entropy, distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF), weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF), the geothermal potential in the study area were predicted quantitatively. Based on the ECF results, the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%, area ratio is 0.867); (2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan, Panzhihua–Liangshan, Dali–Chuxiong, Nujiang–Baoshan, Diqing–Dali, and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt.http://dx.doi.org/10.1080/17538947.2022.2061055mid-spine belt of beautiful chinacertainty factorgeothermal resource areaweights of evidence |
| spellingShingle | Zhe Chen Ruichun Chang Wenbo Zhao Sijia Li Huadong Guo Keyan Xiao Lin Wu Dong Hou Lu Zou Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China International Journal of Digital Earth mid-spine belt of beautiful china certainty factor geothermal resource area weights of evidence |
| title | Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China |
| title_full | Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China |
| title_fullStr | Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China |
| title_full_unstemmed | Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China |
| title_short | Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China |
| title_sort | quantitative prediction and evaluation of geothermal resource areas in the southwest section of the mid spine belt of beautiful china |
| topic | mid-spine belt of beautiful china certainty factor geothermal resource area weights of evidence |
| url | http://dx.doi.org/10.1080/17538947.2022.2061055 |
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