基于连续小波变换的地下天然气微泄漏点识别模型  

Model of Micro-Leakage Point Recognition of Underground Gas Based on Continuous Wavelet Transform

作　　者：李辉[1] 蒋金豹[1] 陈绪慧 彭金英 乔小军[1] 王思佳 LI Hui;JIANG Jin-bao;CHEN Xu-hui;PENG Jin-ying;QIAO Xiao-jun;WANG Si-jia(College of Geosciences and Surveying Engineering,China University of Mining and Technology,Beijing 100083,China;Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)

机构地区：[1]中国矿业大学(北京)地球科学与测绘工程学院,北京100083 [2]北京师范大学地理科学学部,北京100875

出　　处：《光谱学与光谱分析》2019年第12期3743-3748,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41571412);宁夏农林科学院科技创新引导项目(NKYG-18-01);宁夏农牧厅东西部合作项目资助

摘　　要：天然气作为一种清洁、高效的低碳能源,消费占比日益增大。无论是地下输气管道还是储气库,由于管道腐蚀、老化、自然灾害,地下断层、注入井封存不好等因素,都会导致天然气泄漏。从安全、经济、环境等方面考虑,开展地下天然气管道和储气库微泄漏检测是十分必要的。利用高光谱遥感监测地表植被变化而间接探测天然气微泄漏点,通过野外可控系统模拟地下储存天然气微泄漏实验,以冬小麦为研究对象,采集了9期小麦冠层光谱数据,通过光谱分析探寻胁迫小麦光谱特征并构建指数识别模型。首先对小麦冠层光谱进行奇异值剔除和平滑处理,对连续统去除之后的冠层光谱进行连续小波变换,选用Mexihat母小波,在尺度参数为32时,小波系数有较少的峰值和谷值,能与原始光谱拟合较好,且小麦多期数据其峰值和谷值位置都比较稳定。受胁迫和健康小麦的原始光谱可分性较差,但小波系数在487,550和770 nm处受胁迫与健康小麦样本可分性较优,且具有明显的诊断性特征:(1)受胁迫和健康小麦的小波系数在487 nm处为“吸收谷”,其小波系数值为负值,健康小麦小波系数值大于受胁迫小麦的;(2)受胁迫和健康小麦的小波系数在550和770 nm处,有明显的“反射峰”,且受胁迫小麦的小波系数值较大。为更好突出差异性,增强受胁迫和健康小麦的小波系数差异特征,构建了CWT mexh(CWT mexh=CW^2770/(1-CW 487)·CW 550)指数用于胁迫与健康小麦的识别;然后分别与NDVI 705,mNDVI 705,ARI1,R 440/R 740,D 725/D 702指数进行对比分析,经J-M距离定量检验,结果显示CWT mexh指数对天然气微泄漏胁迫下的冬小麦具有较好的识别效果,该指数在天然气胁迫发生20 d后可以稳定区分胁迫和健康两类小麦,且在全生育期都保持相同的规律,而NDVI 705,mNDVI 705,ARI1等指数在整个生育期内无法准确识别健康与胁迫小麦。CWT mexh指�As a clean,efficient low-carbon energy source,natural gas accounts for an increasing proportion of consumption.For underground gas pipelines,gas storage,and the like,natural gas leakage will occur due to factors such as pipeline corrosion,aging,natural disasters,underground faults,and the bad sealing of injection-wells.In terms of security,economic,environmental and other considerations,micro-leak detection of underground natural gas pipeline and gas storage is essential.In this paper,we use hyperspectral remote sensing to monitor surface vegetation changes,thus indirectly detecting natural gas micro-leakage points.The field controllable system is used to simulate the underground micro-leakage experiment.Winter wheat is used in this study,and a time series of 9 experiments of canopy spectral collecting were conducted.Spectral analysis was used to identify and exploit the spectral characteristics of stress wheat and thereby constructing an index recognition model.Firstly,the wheat canopy spectrum is subjected to the processing of singular value culling and smoothing,and then the continuous spectrum wavelet transform is performed on the canopy spectrum after continuum removal.Specifically,mother wavelet of Mexihat is selected.When the scale parameter is 32,the wavelet coefficients have fewer peaks and valleys,which can fit well with the original spectrum,and the peak and valley positions of wheat multi-phase data are relatively stable.The original spectrum of stress and healthy wheat was poorly separable,but separability of proposed model using the wavelet coefficients at 487,550 and 770 nm was better among wheat samples,and had obvious diagnostic characteristics:(1)The wavelet coefficient of stress and healthy wheat having“absorption valley”at 487 nm,the wavelet coefficient value being negative,and the wavelet coefficient of healthy wheat being larger than that of stressed wheat;(2)the wavelet coefficient of stress and healthy wheat is 550 nm at 770 nm,where clear“reflection peak”can be observed,and the wa

关 键 词：天然气微泄漏 冬小麦 光谱特征 指数模型 

分 类 号：P237[天文地球—摄影测量与遥感]