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隧道建设（中英文） ›› 2022, Vol. 42 ›› Issue (8): 1461-1472.DOI: 10.3973/j.issn.2096-4498.2022.08.016

• 研究与探索 • 上一篇    下一篇

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

边民1,2， 董庆1,3,*， 王栋4， 孟德利1， 2， 赵文博1， 2， 周厚瑀1， 2   

  1. （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）
  • 出版日期:2022-08-20 发布日期:2022-09-09
  • 作者简介:边民（1997—），男，山东济南人，中国科学院大学地图学与地理信息系统专业在读硕士，研究方向为热红外遥感地学应用。Email： bianmin19@mails.ucas.ac.cn。 *通信作者： 董庆， Email： dongqing@aircas.ac.cn。

Identification of Geothermal Anomalies in a Plateau Railway Corridor Based on Solar Radiation Effect Correction

BIAN Min1, 2, DONG Qing1, 3, *, WANG Dong4, MENG Deli1, 2, ZHAO Wenbo1, 2, ZHOU Houyu1, 2   

  1. (1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. Key Laboratory of Target Microwave Properties of Zhejiang, Deqing Academy of Satellite Applications, Huzhou 313200, Zhejiang, China; 4. China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, Sichuan, China)
  • Online:2022-08-20 Published:2022-09-09

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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。

关键词: 高原铁路, 地热异常, 定量识别, 地表温度, 太阳辐射效应, 随机森林

Abstract: To improve the accuracy of identifying geothermal anomaly areas by thermal infrared remote sensing, a plateau railway corridor is selected as the research area. The daytime surface temperature is retrieved using the singlewindow algorithm based on Landsat8 data, and the nighttime surface temperature 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 forest method, and the surface temperature without the solar radiation effect is obtained. Finally, the surface temperature after removing the solar radiation effect, fault density, buffer distance to river, and magnetic anomaly are selected as index factors. The geothermal anomaly area is quantitatively identified using the certainty factor model, and the results are evaluated using the known hot spring points. The results reveal the following: (1) The random forest method can effectively remove the temperature changes caused by nongeothermal factors, reduce the effect of solar radiation, and improve the accuracy of identifying geothermal anomalies. (2) The evaluation results of geothermal anomaly areas are consistent with the distribution of known hot spring points, and the identification results of high and mediumanomaly areas are reliable. (3) The plateau railway corridor mainly passes through seven geothermal anomaly areas, and the lengths of the line passing through the high and mediumanomaly areas are 72 km and 125 km, respectively. LudingKangding, Litang basin and its surrounding areas, Batang, Gongjue, ChangduChaya, Basu, and BomiLulang are the sections that need to focus on preventing high temperature and heat damage. The research results have preliminarily realized the quantitative and refined identification of geothermal anomalies in the plateau railway corridor.

Key words:  , plateau railway, geothermal anomaly, quantitative identification, surface temperature, solar radiation effect, random forest

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