New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

Authors:
raw_author_name: Xiumei Ma
raw_affiliation_string: Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, 830011 China; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Jinlin Wang
raw_affiliation_string: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 10094; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Kefa Zhou
raw_affiliation_string: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 10094; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Wenqiang Zhang
raw_affiliation_string: Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, 830011 China; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Philippe De Maeyer
raw_affiliation_string: Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Tim Van de Voorde
raw_affiliation_string: Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium

Abstract: The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.