基于高光谱的砂岩露头孔隙度估算方法研究
投稿时间:2018-04-25  修订日期:2018-09-21  点此下载全文
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作者单位E-mail
盛 洁 中国石油大学(华东)地球科学与技术学院 helloshengjie@163.com 
刘 展 中国石油大学(华东)地球科学与技术学院 liuzhan5791@sina.com 
曾齐红 中国石油勘探开发研究院测井遥感所  
张友焱 中国石油勘探开发研究院测井遥感所  
白永良 中国石油大学(华东)地球科学与技术学院  
刘兰法 Institute for Cartography,TU Dresden  
基金项目:国家重大专项(2017ZX05001001);中国石油股份重大专项(2016B-03)
中文摘要:为快速获得宏观、定量的砂岩露头孔隙度,本文提出了基于高光谱的孔隙度估算新方法。采集野外露头砂岩样品并测得其孔隙度,利用岩石薄片鉴定资料分析砂岩孔隙度的影响因素;对岩样实测光谱预处理,探索砂岩孔隙度的光谱响应机理;考虑到光谱波段高维性和波段间多重相关性,采用偏最小二乘方法构建孔隙度估算模型;通过变量投影重要性分析模型中重要波段。研究结果表明:基于砂岩填隙物与孔隙度的相关性以及填隙物的光谱特征,可间接反演孔隙度;砂岩孔隙度具有良好的光谱响应;反射率能够定量估算砂岩孔隙度(全波段模型R2=0.72,RMSE=2.28,RPD=1.94);重要波段帮助降低自变量维度,发现孔隙度敏感波谱响应。本研究为基于高光谱图像的野外露头孔隙度表征奠定了基础。
中文关键词:砂岩露头  孔隙度估算  高光谱  偏最小二乘
 
Porosity Estimation Method in Sandstone Outcrop Based on Hyperspectrum
Abstract:In order to obtain macroscopic and quantitative porosity data in outcrop rapidly, a new Hyperspectrum based porosity estimation method was proposed in this paper. Sandstone samples were collected from field outcrop and measured for porosity data, whose influence factors were analyzed with rock thin section. After the preprocessing on the spectral data of rock samples, the spectral response mechanism of porosity was preliminarily explored. Considering the high dimensionality of spectral bands and the multiple correlation between bands, the porosity estimation models were constructed using the partial least squares method. The important bands in the model were indicated by the variable importance in the projection. The results show that: Sandstone porosity can be indirectly retrieved based on the correlation between interstitial fillings and porosity and the spectral characteristics of interstitial fillings; Sandstone porosity shows good spectral response; Reflectance has the ability to estimate porosity quantitatively (porosity estimation model based on full wavelengths: R2=0.72,RMSE=2.28,RPD=1.94); The important bands help to reduce the independent variable dimension and find the porosity-sensitive spectral response. This study lays the foundation for porosity characterization in the outcrop based on hyperspectral images.
keywords:Sandstone outcrop  Porosity estimation  Hyperspectrum  PLSR
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