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遥感技术与应用  2021, Vol. 36 Issue (6): 1368-1378    DOI: 10.11873/j.issn.1004-0323.2021.6.1368
遥感应用     
基于热红外遥感的川藏铁路昌都—林芝段地热异常区定量预测评价研究
陈喆1,2,3(),董庆2(),陈建平1,3,赵文博2,5,蒋良文4,张广泽4,冯涛4,王栋4,毕晓佳4,边民2,5,张权平1,3,孟德利2,5
1.中国地质大学(北京)地球科学与资源学院,北京 100083
2.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
3.北京市国土资源信息研究开发重点实验室,北京 100083
4.中铁二院工程集团有限责任公司,四川 成都 610031
5.中国科学院大学,北京 100049
Research on Quantitative Prediction and Evaluation of Geothermal Anomaly Area in Qamdo-Nyingchi Section of Sichuan-Tibet Railway
Zhe Chen1,2,3(),Qing Dong2(),Jianping Chen1,3,Wenbo Zhao2,5,Liangwen Jiang4,Guangze Zhang4,Tao Feng4,Dong Wang4,Xiaojia Bi4,Min Bian2,5,Quanping Zhang1,3,Deli Meng2,5
1.School of Earth Science and Resources,China University of Geosciences(Beijing),Beijing 100083,China
2.Laboratory of Digital Earth Science,Aerospace Information Research Institute,CAS,Beijing 100094,China
3.Key Laboratory of Land and Resources Information Research & Development in Beijing,Beijing 100083,China
4.China Railway Eryuan Engineering Group Co. Ltd,Chengdu 610031,China
5.University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

识别川藏铁路沿线的地热异常区有助于工程的建设和后期的管理维护。以川藏铁路昌都—林芝段为研究区,基于Landsat 8热红外影像数据,反演地表温度并进行星地同步实验,得到校正后的地温值。围绕地热异常的成因与分布规律,选取地层组合熵、断层缓冲距、断层线密度、地表温度、水系缓冲距、地震动峰值加速度6个影响因子作为地热异常区评价指标并检验因子独立性。构建信息量模型进行定量预测,最终将识别结果划分为5个子区域。研究表明:高异常区和中异常区分别占研究区总面积的9.14%和28.57%,地热高温点的空间分布与地热异常区评价结果基本一致。研究结果可为川藏铁路的设计与施工提供参考依据。

关键词: 川藏铁路热红外遥感地表温度信息量模型地热异常区    
Abstract:

The identification of geothermal anomaly areas along the Sichuan-Tibet Railway is helpful to the construction and later management and maintenance of the project. Taking The Qamdo-Nyingchi section of Sichuan-Tibet Railway as the research area, based on the landsat-8 thermal infrared image data, the surface temperature was inverted and the planetary geostationary experiment was carried out to obtain the corrected geotherm value. Focusing on the genesis and distribution of geothermal anomalies, six influencing factors, namely, formation assemblage entropy, fault buffer distance, fault line density, surface temperature, water buffer distance, and peak ground motion acceleration, were selected as the evaluation indexes of geothermal anomaly areas and the independence of factors was tested. An information quantity model was built for quantitative prediction, and the recognition results were finally divided into 5 sub-regions. The results show that the high anomaly area and the middle anomaly area account for 9.14% and 28.57% of the total area of the study area respectively, and the spatial distribution of geothermal high-temperature points is basically consistent with the evaluation results of geothermal anomaly area. The research results can provide reference for the design and construction of Sichuan-Tibet railway.

Key words: Sichuan-Tibet Railway    Thermal infrared remote sensing    Surface temperature    Information model    Geothermal anomaly region
收稿日期: 2020-10-06 出版日期: 2022-01-26
ZTFLH:  P642.2  
基金资助: 中铁二院科学技术项目“川藏铁路雅安至昌都段地温集中发育区地热分布高精度热红外遥感解译专题”,国家重点研发计划“深地资源勘查开采”重点专项课题“深部成矿地质异常定量预测方法与模型”(2017YFC0601502)
通讯作者: 董庆     E-mail: chenz115@foxmail.com;dongqing@aircas.ac.cn
作者简介: 陈喆(1991-),男,湖南石门人,硕士研究生,主要从事热红外遥感地表温度反演及地热异常识别研究。E?mail: chenz115@foxmail.com
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引用本文:

陈喆,董庆,陈建平,赵文博,蒋良文,张广泽,冯涛,王栋,毕晓佳,边民,张权平,孟德利. 基于热红外遥感的川藏铁路昌都—林芝段地热异常区定量预测评价研究[J]. 遥感技术与应用, 2021, 36(6): 1368-1378.

Zhe Chen,Qing Dong,Jianping Chen,Wenbo Zhao,Liangwen Jiang,Guangze Zhang,Tao Feng,Dong Wang,Xiaojia Bi,Min Bian,Quanping Zhang,Deli Meng. Research on Quantitative Prediction and Evaluation of Geothermal Anomaly Area in Qamdo-Nyingchi Section of Sichuan-Tibet Railway. Remote Sensing Technology and Application, 2021, 36(6): 1368-1378.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1368        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1368

图1  研究区与同步实验测区概况
图2  实测地温值与反演地温值的关系
图3  校正前后地表温度
图4  影响因子图
指标因子地层组合熵断层缓冲距断层线密度地表温度水系缓冲距地震烈度
地层组合熵1.000
断层缓冲距-0.0811.000
断层线密度0.143-0.2701.000
地表温度-0.004-0.0620.1431.000
水系缓冲距-0.0240.091-0.068-0.1531.000
地震烈度-0.132-0.0180.1290.072-0.0091.000
表1  各指标因子间的相关关系
影响因子分级地热点 个数/个面积 /km2信息量
地层组合熵0~1049124 419-0.361
10~20913 1230.188
20~401925 9820.252
40~501013 4250.270
50~601313 6920.513
60~802222 3170.551
80~10048 694-0.211

断层缓冲距

/km

0~25328 1451.198
2~42824 1670.712
4~61020 690-0.162
6~81017 3160.016
8~10514 973-0.532
10~12412 839-0.601
>1216103 522-1.302
断层线密度 /(km/km2)0.000~0.0121599 114-1.323
0.012~0.033723 838-0.661
0.033~0.0533545 1910.309
0.053~0.0741818 4000.543
0.074~0.0982521 1030.734
0.098~0.1281710 3911.057
0.128~0.18093 6151.477
地表温度 /℃-10~-6076 0090.000
-6~01151 784-0.984
0~61838 579-0.197
6~113226 3440.789
11~163517 7021.247
16~22248 7681.572
22~4362 4661.454
水系缓冲距 /km0~25529 4341.190
2~41427 588-0.113
4~61226 126-0.213
6~81024 261-0.321
8~10721 523-0.558
10~12918 524-0.157
>121974 197-0.797
地震烈度 /级786147 7190.029
83869 796-0.043
924 137-0.162
单元栅格尺寸为30 m×30 m,计算时面积可用对应的栅格数目替代
表2  信息量值计算结果
图5  信息量模型分级图
地热异常分级地热点数地热点占比网格单元数网格单元占比成功指数
高异常区6350.00%22 396 6399.14%0.000 28%
中异常区3628.57%50 748 93120.70%0.000 07%
低异常区1411.11%51 387 18820.96%0.000 03%
异常缓冲区97.14%47 482 52919.37%0.000 02%
无异常区43.17%73 133 05929.83%0.000 01%
表3  预测结果分级图对应的成功指数
图6  预测率函数曲线
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