State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China.
Sensors (Basel). 2020 Nov 6;20(21):6325. doi: 10.3390/s20216325.
Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination() = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future.
碎屑物地球化学信息已被证明是一种在矿产勘查中有效的找矿方法。然而,传统的基于野外采样和实验室化学分析的碎屑金属含量监测方法耗时耗力,可能无法满足大规模金属含量监测的要求。在本研究中,我们从中国新疆卡拉塔格戈壁地区获得了 95 个碎屑样本和 7 个 HySpex 高光谱图像场景,空间分辨率为 1m,使用偏最小二乘法和波段选择方法来探索低空 HySpex 高光谱图像在估计碎屑物中 Cu 元素含量的可行性和有效性。结果表明:(1)在变换光谱的所有反演模型中,幂对数变换光谱是最优预测模型(决定系数() = 0.586,平均绝对误差(MAE) = 21.405);(2)与遗传算法(GA)和连续投影算法(SPA)相比,竞争加权重采样算法(CARS)是最优的特征波段筛选方法。基于 CARS 选择的特征波段构建的反演模型的决定系数为 0.734,高于 GA(0.519)和 SPA(0.691),平均绝对误差(MAE)为 19.926,是最低的。该模型仅使用 20 个波段,低于 GA(105)和 SPA(42);(3)得到了 HySpex 高光谱图像模型与研究区铜含量分布的幂对数变换、CARS 以及碎屑物铜含量反演结果,与实地调查结果一致。研究结果表明,利用 HySpex 高光谱数据反演碎屑物中铜含量的方法是可行且有效的,为大面积获取地球化学元素分布和未来减少地质勘查关键区域提供了数据和参考方法。