地学前缘 ›› 2019, Vol. 26 ›› Issue (4): 109-116.DOI: 10.13745/j.esf.sf.2019.7.5

• 矿床大数据研究 • 上一篇    下一篇

基于样本数据的红土型铝土矿定量遥感建模与反演研究

成功，钟超岭，袁海明，任明，许文文，王冬军   

  1. 1. 中南大学 地球科学与信息物理学院, 湖南 长沙 410083
    2. 中南大学 有色金属成矿预测与地质环境监测教育部重点实验室, 湖南 长沙 410083
    3. 中南大学 有色资源与地质灾害探查湖南省重点实验室, 湖南 长沙 410083
    4. 河南省有色金属地质矿产局 第三地质大队, 河南 郑州 450016
  • 收稿日期:2019-04-12 修回日期:2019-07-02 出版日期:2019-07-25 发布日期:2019-07-25
  • 作者简介:成功(1972—)，男，博士，讲师，硕士生导师，主要从事国内外定量遥感找矿研究。
  • 基金资助:
    国家自然科学基金项目(41772348)；国家重点研发计划项目(2017YFC0601503)

Quantitative remote sensing modeling and inversion of laterite type bauxite based on sample data

CHENG Gong,ZHONG Chaoling,YUAN Haiming,REN Ming,XU Wenwen,WANG Dongjun   

  1. 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    2. Key Laboratory of Metallogenic Prediction of Non-ferrous Metals and Geological Environment Monitor(Ministry of Education), Central South University, Changsha 410083, China
    3. Key Laboratory of Non-ferrous Resources and Geological Detection of Hunan Province, Central South University, Changsha 410083, China
    4. No.3 Geological Team, Henan Provincial Non-ferrous Metals Geological and Mineral Resources Bureau, Zhengzhou 450016, China
  • Received:2019-04-12 Revised:2019-07-02 Online:2019-07-25 Published:2019-07-25
  • Supported by:
     

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摘要： 定量遥感找矿是遥感大数据在找矿应用中的一项前沿技术。本文采用多元回归分析方法，通过对多组不同数量地表铝土矿样本进行遥感建模与反演实验，探索样本量对定量遥感建模的影响。实验中，首先以地表采样获得的Al2O3、SiO2含量分析结果作为样本数据，再根据样本位置从Landsat 8遥感数据中分别读取1~7个波段反射率数据，然后利用SPSS软件，将Al2O3、SiO2含量与相应的1~7波段反射率进行多元回归分析，并分别建立Al2O3、SiO2含量遥感定量反演模型。为了得到最佳反演模型，随机选出6批不同数量的样本进行建模实验，每批样本大约2/3用于建模，其余1/3用于模型检验。实验结果表明：随着样本量增加，模型的判定系数(R2)均呈先快速升高后缓慢下降的趋势，反演结果的均方根误差则与之相反。当样本量为50左右时，判定系数取得极大值，均方根误差取得极小值，总体具有偏态分布特征。最后，利用遥感影像对样本量为50时建立的模型进行反演验证，研究区地表Al2O3、SiO2含量的反演结果与实际情况吻合得很好，从而证实了该建模方法具有可靠性，可以进一步推广使用。

 

关键词: 定量遥感, 马达加斯加, 红土型铝土矿, 多元回归分析, 建模, 反演

Abstract: Quantitative remote sensing technique is at the forefront of using remote sensing big data for ore prospecting. To explore the effects of number of samples on quantitative remote sensing modeling, we used multiple regression analysis method to perform remote sensing modeling and inversion experiments with varying number of surface bauxite samples. In this study, surface samples were first analyzed to obtain Al2O3 and SiO2 content information. Then, from the Landsat 8 remote sensing data, spectral reflectance of 1 to 7 spectral bands were read according to sample position. Next, multiple regression analysis on Al2O3 and SiO2 contents and corresponding reflectance of the 17 spectral bands were carried out using the SPSS software to establish a remote sensing quantitative inversion model according to the Al2O3 and SiO2 contents. In order to obtain the best inversion model, we randomly selected 6 batches of different number of samples for the modeling experiments, using about 2/3 of samples for modeling and the rest for model testing. The experimental results showed that as number of samples increased, the coefficient of determination (R2) first rose rapidly and then slowly declined; whereas the root mean square error (RMSE) behaved oppositely. At number of samples 50, maximum R2 and minimum RMSE were reached, showing an overall skewed data distribution. Finally, the remote sensing image was used to invert the established model at number of samples 50. The inversion results were in good agreement with experimentally measured Al2O3 and SiO2 contents in the study area, confirming that the modeling method is reliable and its application can be further expanded.

Key words: quantitative remote sensing, Madagascar, laterite type bauxite, multiple regression analysis, modeling, inversion

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