文章摘要
丁志华,盛艳蕊,张子广.华北地区高光谱气体影响因素分析[J].地震工程学报,2021,43(2):279-286. DING Zhihua,SHENG Yanrui,ZHANG Ziguang.Influencing Factors for Hyperspectral Gas in North China[J].China Earthquake Engineering Journal,2021,43(2):279-286.
华北地区高光谱气体影响因素分析
Influencing Factors for Hyperspectral Gas in North China
投稿时间:2018-09-18  
DOI:10.3969/j.issn.1000-0844.2021.02.279
中文关键词: 高光谱;RST算法;相关系数;回归方程;不同地形
英文关键词: hyperspectral;RST algorithm;correlation coefficient;regression equation;different terrain
基金项目:河北省地震局科技星火计划项目(DZ20190415003,DZ20180322026,DZ20160406028)
作者单位
丁志华 河北省地震局, 河北 石家庄 050021 
盛艳蕊 河北省地震局, 河北 石家庄 050021 
张子广 河北省地震局, 河北 石家庄 050021 
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中文摘要:
      基于高光谱遥感数据分析CH4与CO的影响因素,应用于京津冀地区异常性质判定。通过不同地形高光谱气体与水汽、温度、长波辐射、气压等参数进行相关系数计算。发现陆地与海洋具有不同的特性,CH4与CO呈较强的负相关,且相关系数盆地大于山区大于平原;CH4与水汽、温度、长波辐射呈负相关,与气压呈正相关,且相关系数平原大于山区大于盆地。分析认为在陆地环境CH4浓度受水汽、温度、气压等气象因素影响较大,CO因为海洋源的影响,与不同参数相关系数较低。通过RST算法提取近期京津冀地区高光谱气体异常,发现北京西南与河北交界地区在6月有明显的高值异常,通过回归方程检验,认为该异常是气象因素引起的可能性较大,为震情跟踪判定提供了依据。
英文摘要:
      Based on the hyperspectral remote sensing data, the influencing factors of CH4 and CO were analyzed and applied to the abnormal determination in Beijing-Tianjin-Hebei region. The correlation coefficient between hyperspectral gas in different terrain and water vapor, temperature, long wave radiation, and air pressure were calculated. It is found that the correlation coefficients on land and ocean have different characteristics. There is a strong negative correlation between CH4 and Co, and the correlation coefficient in basin is greater than that in mountain area and plain area. The results showed that CH4 concentration in terrestrial environment is greatly affected by meteorological factors such as water vapor, temperature, and air pressure, while CO has low correlation coefficient with the parameters due to the influence of marine sources. The recent hyperspectral gas anomaly in Beijing-Tianjin-Hebei region is extracted by RST algorithm. It is found that there is an obvious high-value anomaly in June in the border area of southwest Beijing and Hebei. Through the test of regression equation, it is more likely that the anomaly is caused by meteorological factors, which provides a basis for the earthquake tracking and judgment.
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