光谱学与光谱分析, 2019, 39 (7): 2077, 网络出版: 2019-07-23  

火成岩中长石含量与其特征光谱间响应关系研究

Response Relationship between Feldspar Content and Characteristic Spectra in Igneous Rocks
作者单位
1 吉林大学地球探测科学与技术学院, 吉林 长春 130026
2 中国人民解放军32023部队, 辽宁 大连 116000
摘要
长石是地表岩石最重要的造岩矿物, 在地壳中的比例高达60%, 几乎是所有火成岩的主要矿物成分。 随着高光谱技术的发展, 国内外众多学者研究主要造岩矿物含量与其特征光谱的响应关系, 对遥感岩矿识别以及矿化蚀变信息提取提供了多种可能性。 该研究以USGS光谱库里18个火成岩样本为基础数据, 研究长石的特征光谱及其与含量之间的定量关系。 通过原始光谱反射率及其变换(包括小波三层分解高频分量、 小波二层分解、 去包络线后光谱、 去包络线后小波三层分解高频分量及去包络线后小波二层分解), 研究其与长石的含量之间的相关关系, 结果表明: (1)分析六种光谱反射率的变换, 去包络线后小波三层分解高频分量的光谱反射率与长石含量的相关关系最好, 且相关系数正负不断变化, 根据相关系数极值获得长石的特征谱带为431, 570, 972, 1 456, 1 856, 2 292.9和2 481 nm; (2)原始光谱反射率与长石含量的相关性曲线趋势较为平缓, 而经小波分解得到的高频分量后, 趋势明显, 经去包络线及小波分解得到高频分量后, 相关性曲线的变化趋势愈加明显, 由此可见, 自变量的微小变化就会引起因变量变化, 当岩石中长石的含量极小时, 小波分解处理能够提高模型的精度。 将长石含量与特征光谱的关系量化, 运用多元逐步线性回归分析以及最小二乘法建模, 建立6个线性回归模型和6个最小二乘法回归模型, 结果表明: (1)去包络线后的光谱比原始光谱建立的回归模型精度更高, 经过小波二层分解后的低频分量建模的回归模型精度优于未进行小波分解的光谱, 其中去包络线后小波二层分解低频分量建立的回归模型效果最佳。 (2)多元线性回归建立的模型精度优于最小二乘法, 同时筛选对因变量影响较大的自变量, 972, 1 456, 1 856, 2 292.9以及2 481 nm。 因此选择去包络线后的光谱进行多元线性回归法进行分析长石含量与光谱反射率之间关系, 考虑到不同的特征吸收波段对长石含量的影响因子不同, 可以利用长石的特征光谱定量反演某一区域内的长石的含量, 对识别矿物具有重要意义。
Abstract
Feldspar is the most important rock-forming mineral in surface rocks, accounting for up to 60% in the earth's crust. With the development of high-spectral technology, many scholars at home and abroad have been studying the response of the main building of rock and mineral content and the spectrum of the features, and it offers a variety of possibilities for remote sensing, mineralization, and mineralization. It is based on data of 18 igneous samples in the USGS spectral library to study the quantitative relation between the characteristic spectra and the content of the feldspar. Through the original spectral reflectivity and the transformation (including the three layers of the small wave to break down the high frequencies, the little wave layers, the spectrum of the wave to the back of the line, and then the little wave layer after the cable, and then the little wave layer after the cable, and then the small wave layer of the wave, and then the second layer of the cable) study the correlation between the high and the long rocks, and it turns out: (1) to analyze the transformation of six spectral reflectivity, the relation between the spectral reflectivity of the high frequency and the long rock content of the small wave three layers of the envelope, and the correlation coefficients are the best, and the correlation coefficients are constantly changing, and based on the relative coefficients of the relative coefficients, the high value of the long rock is 4, 31, 570, 972, 1 456, 1 856, 2 292.9, 2 481 nm; (2) the correlation between the original spectral reflectance and feldspar content curve trend is relatively flat, and after the wavelet decomposed high frequency component, through to the envelope and wavelet decomposition for the high frequency component, the change trend of correlation curve is becoming ever more obvious, therefore, the independent variable of the small changes will cause changes in the dependent variable, very hour when the content of the rock feldspar, wavelet decomposition processing can improve the accuracy of the model. The relation between the content of feldspar and the characteristic spectrum is quantified, using a multi-element stepwise linear regression analysis and a least-square method of modeling, establishing six linear regression models and six least-squares regression models, and the results show that: (1) the spectrum of the spectrum behind the envelope is more accurate than the original spectrum, and the low-frequency portion of the lower wave part of the wave is better than the one that's not done with the small wave decomposition, which is the best way to get to the back model of the low-frequency portion of the small wave after the envelope. (2) the multilinear regression model is better than the least-squares, and the variables that affect the variables that affect the larger variables, 972, 1 456, 1 856, 2 292.9 and 2 481 nm. In view of that relation between the content of feldspar and the spectral reflectance of the long stone and the influence factors on the content of feldspar in different absorption band, it is important to use the characteristic spectrum of feldspar to quantitatively invert the content of feldspar in a region.

杨长保, 高文博, 侯光宇, 李星喆, 高曼婷. 火成岩中长石含量与其特征光谱间响应关系研究[J]. 光谱学与光谱分析, 2019, 39(7): 2077. YANG Chang-bao, GAO Wen-bo, HOU Guang-yu, LI Xing-zhe, GAO Man-ting. Response Relationship between Feldspar Content and Characteristic Spectra in Igneous Rocks[J]. Spectroscopy and Spectral Analysis, 2019, 39(7): 2077.

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