Geothermal target detection integrating multi-source and multi-temporal thermal infrared data

• Published in: Ore geology reviews Vol. 167; p. 105991
• Main Authors: Chao, Jiangqin, Zhao, Zhifang, Xu, Shiguang, Lai, Zhibin, Liu, Jianyu, Zhao, Fei, Yang, Haiying, Chen, Qi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: Daytime and Nighttime land surface temperature; Dempster-Shafer evidence theory; Geothermal anomalies; Ruili city; Thermal infrared remote sensing; Time series; Daytime and Nighttime land surface temperature; Thermal infrared remote sensing; Dempster-Shafer evidence theory; Geothermal anomalies; Time series; Ruili city
• ISSN: 0169-1368
• DOI: 10.1016/j.oregeorev.2024.105991

[Display omitted] •The position of the magma heat source was firstly reported in the city of Ruili.•Geothermal anomalies are distributed along NE–SW oriented faults.•The geothermal anomalies on the NE side exhibited greater significance.•Day-Night LST integration improves geothermal anomaly detection. Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.