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.

中文翻译：

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融合多源多时相热红外数据的地热目标检测

热红外遥感（TIRS）技术由于其时间效率和成本效益而在地热异常检测方面引起了人们的兴趣。从 TIRS 得出的地表温度 (LST) 表明潜在的地热异常。然而，不同自然特征的地表温度在白天和夜间表现出相对冷/热的异常，影响了异常区域的识别。本研究的目的是通过整合多视图白天和夜间地表温度数据来突出地热异常区域。具体来说，2013-2023年钻探的冬季LST时间序列是基于Landsat-8 TIRS在昼夜情景下进行处理的。针对ASTER LST产品的夜间目标识别问题，提出了一种基于Dempster-Shafer证据理论的信息聚合分类方法。这种昼夜信息融合分析有效地突出了潜在的地热异常区域。结果表明，地热异常主要沿瑞丽—龙陵断裂带和畹町断裂带呈东北（NE）—西南（SW）方向分布，且NE侧较SW侧更为显着。这些发现表明，东侧距离岩浆热源较近，推测的岩浆热源位置与检测到的地热异常高度一致。利用地质数据对LST异常区域进行地质解释，消除非地热影响。结合钻探资料验证，该方法成功应用于云南省瑞丽市地热异常区识别。总体而言，这项研究为通过 TIRS 检测地热异常提供了宝贵的见解，有助于地热资源的成功开发。