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基于太阳辐射效应校正的高原铁路廊道地热异常区识别研究
引用本文:边民,董庆,王栋,孟德利,赵文博,周厚瑀. 基于太阳辐射效应校正的高原铁路廊道地热异常区识别研究[J]. 隧道建设, 2022, 42(8): 1461-1472. DOI: 10.3973/j.issn.2096-4498.2022.08.016
作者姓名:边民  董庆  王栋  孟德利  赵文博  周厚瑀
作者单位:(1. 中国科学院空天信息创新研究院 数字地球重点实验室, 北京〓100094; 2. 中国科学院大学, 北京〓100049; 3. 中科卫星应用德清研究院 浙江省微波目标特性测量与遥感重点实验室,浙江湖州〓313200; 4. 中铁二院工程集团有限责任公司,四川成都〓610031)
摘    要:为提高热红外遥感识别地热异常区的准确度,选取高原铁路廊道作为研究区,基于Landsat8数据采用单窗算法反演得到白天地表温度,并基于ECO2LSTE数据得到夜间地表温度;然后,利用随机森林方法选取海拔、坡度、坡向、累积太阳辐射、NDVI、NDSI、NDWI以及反照率作为输入因子,得到去除太阳辐射效应的地表温度;最后,选取去除太阳辐射效应的地表温度、断裂密度、到水系距离和地磁异常作为指标因子,采用确定性系数模型定量识别地热异常区,并利用已知温泉点对结果进行评价。结果表明: 1)随机森林方法可有效去除非地热引起的温度变化,减弱太阳辐射效应,提高地热异常区识别的准确度; 2)地热异常区评价结果与已知温泉点分布较为一致,高异常区和中异常区的识别结果可靠; 3)高原铁路廊道主要途经7个地热异常区,经过高异常区和中异常区的廊道长度分别为72 km和125 km,泸定—康定、理塘盆地及周边、巴塘、贡觉、昌都—察雅、八宿、波密—鲁朗是需要重点防范高温热害的区段。研究结果初步实现了高原铁路廊道的地热异常区定量化和精细化识别。


Identification of Geothermal Anomalies in a Plateau RailwayCorridor Based on Solar Radiation Effect Correction
BIAN Min,DONG Qing,WANG Dong,MENG Deli,ZHAO Wenbo,ZHOU Houyu. Identification of Geothermal Anomalies in a Plateau RailwayCorridor Based on Solar Radiation Effect Correction[J]. Tunnel Construction, 2022, 42(8): 1461-1472. DOI: 10.3973/j.issn.2096-4498.2022.08.016
Authors:BIAN Min  DONG Qing  WANG Dong  MENG Deli  ZHAO Wenbo  ZHOU Houyu
Affiliation:(1. Key Laboratory of Digital Earth Science, AerospaceInformation Research Institute, Chinese Academy ofSciences, Beijing 100094, China; 2. University of Chinese Academy of Sciences,Beijing 100049, China; 3. Key Laboratory of TargetMicrowave Properties of Zhejiang, Deqing Academy of Satellite Applications, Huzhou 313200, Zhejiang, China; 4. China Railway Eryuan EngineeringGroup Co., Ltd., Chengdu 610031, Sichuan, China)
Abstract:To improve the accuracy of identifying geothermalanomaly areas by thermal infrared remote sensing, a plateau railway corridor isselected as the research area. The daytime surface temperature is retrievedusing the single window algorithm based on Landsat8 data, and the nighttime surfacetemperature is obtained by ECO2LSTE.Next, the altitude, slope, aspect, cumulative solar radiation, NDVI,NDSI, NDWI, and albedo are selected as input factors using the random forestmethod, and the surface temperature without the solar radiation effect isobtained. Finally, the surface temperature after removing the solar radiationeffect, fault density, buffer distance to river, and magnetic anomaly areselected as index factors. The geothermal anomaly area is quantitativelyidentified using the certainty factor model, and the results are evaluatedusing the known hot spring points. The results reveal the following: (1) Therandom forest method can effectively remove the temperature changes caused bynon geothermal factors, reduce the effect of solarradiation, and improve the accuracy of identifying geothermal anomalies. (2)The evaluation results of geothermal anomaly areas are consistent with thedistribution of known hot spring points, and the identification results of high and medium anomaly areas are reliable. (3)The plateau railway corridor mainly passes through seven geothermal anomalyareas, and the lengths of the line passing through the high and medium anomaly areas are 72 km and 125km, respectively. Luding Kangding, Litang basin and itssurrounding areas, Batang, Gongjue, Changdu Chaya,Basu, and Bomi Lulang are the sections that need tofocus on preventing high temperature and heat damage. The research results havepreliminarily realized the quantitative and refined identification ofgeothermal anomalies in the plateau railway corridor.
Keywords:   plateaurailway   geothermal anomaly   quantitative identification   surface temperature  solar radiation effect   random forest  
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