A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone

CHEN Yan; YUAN Jing; TANG Chunhua; SUN Chao; TANG Xiao; WANG Mingyou

doi:10.6046/zrzyyg.2023003

2024 Vol. 36, No. 2

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CHEN Yan, YUAN Jing, TANG Chunhua, SUN Chao, TANG Xiao, WANG Mingyou. 2024. A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone. Remote Sensing for Natural Resources, 36(2): 27-38. doi: 10.6046/zrzyyg.2023003

Citation:

CHEN Yan, YUAN Jing, TANG Chunhua, SUN Chao, TANG Xiao, WANG Mingyou. 2024. A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone. Remote Sensing for Natural Resources, 36(2): 27-38. doi: 10.6046/zrzyyg.2023003

A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone

1. Basic Geological Survey Institute of Jiangxi Geological Survey and Exploration Institute, Nanchang 330030, China
;2. Jiangxi Non-ferrous Geology and Mineral Exploration and Development Institute, Nanchang 330030, China
;3. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China

More Information

Corresponding author: TANG Chunhua

Received Date: 03 January 2023

Revised Date: 17 August 2023

Publish Date: 14 June 2024

Abstract

Abstract

Based on thermal infrared and multispectral remote sensing data, this study analyzed the thermal spring-related structures interpreted from remote sensing images. Thermal springs crop out at the intersections of asterisk- and lambda-shaped structures, with asterisk-shaped structures exhibiting more favorable conditions. By delving into remote sensing characteristics related to thermal springs, this study presented remote sensing factors like surface temperature, hydroxyl anomaly, soil moisture, hydrographic net, and elevation. Using mathematical geostatistics and prediction methods based on geographical information system (GIS), including the weight of evidence, prospecting information content method, and feature factor method, this study analyzed the geological, remote sensing, and geophysical factors related to thermal springs for mathematical geostatistics and prediction. The comprehensive analysis reveals 57 favorable geothermal areas, including 8 in category A, 18 in category B, and 31 in category C. All the category-A favorable geothermal areas include known geothermal sites, and one category-B favorable area reveals a 51.6 ℃ thermal spring, suggesting reliable prediction results. The methodology of this study provides a new approach for geothermal resource prediction.
- surface temperature /
- hydroxyl anomaly /
- soil moisture /
- GIS /
- mathematical geostatistics