基于Hapke模型多角度孔雀石光谱特性分析

岩矿的光谱特征是利用遥感手段识别岩矿的理论基础,随着遥感技术的不断发展,多角度观测为岩矿的遥感精确识别提供了新的思路。选取孔雀石粉末的多角度光谱特征作为研究对象,利用Hapke模型对孔雀石的多角度光谱特征进行精确描述,以确定孔雀石的单次散射反照率的多角度光谱特征。研究表明,光源天顶角和传感器观测天顶角之间的关系对于孔雀石的单次散射反照率有着重要影响,样本的单次散射反照率标准差与其光谱之间整体体现负相关关系。根据这一结论,比较样品单位波长内各角度二向反射率和单次散射反照率标准差,二向反射率受到影响作用较小,故可以利用多角度条件下单次散射反照率波谱特征提高对孔雀石的遥感识别精度。

A new method of searching for concealed Au deposits by using the spectrum of arid desert plant species

With the increase of exploration depth,it is more and more difficult to find Au deposits.Due to the limitation of time and cost,traditional geological exploration methods are becoming increasingly difficult to be effectively applied.Thus,new methods and ideas are urgently needed.This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits.For this purpose,48 plant(Seriphidium terrae-albae)and soil(aeolian gravel desert soil)samples were first collected along a sampling line that traverses an Au mineralization alteration zone(Aketasi mining region in an arid region of China)and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device(ASD)FieldSpec3 spectrometer.Then,the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated.Additionally,the characteristic bands were selected from plant spectra using four different methods,namely,genetic algorithm(GA),stepwise regression analysis(STE),competitive adaptive reweighted sampling(CARS),and correlation coefficient method(CC),and were then input into the partial least squares(PLS)method to construct a model for estimating the soil Au content.Finally,the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method.The results were compared with those of a model based on the full spectrum.The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information,and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil.The cross-validated coefficient of determination(R2)and the ratio of the performance to deviation(RPD)between the predicted value and the measured value reached the maximum of 0.8218 and 2.37,respectively,with a minimum value of 6.56μg/kg for the root-mean-squared error(RMSE)in the full spectrum model.However,in the process of modeling,it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method.Compared with the GA,STE,and CC methods,CARS was the superior characteristic band screening method based on the accuracy and complexity of the model.When modeling with characteristic bands,the highest accuracy,R2 of 0.8016,RMSE of 7.07μg/kg,and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra(1/lnR)'(where it represents the first derivative of the reciprocal of the logarithmic spectrum)of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil.Thus,characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content.Finally,this study proposes a method of using plant spectra to find concealed Au deposits,which may have promising application prospects because of its simplicity and rapidity.

Estimation of rock Fe content based on hyperspectral indices

Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines.However,the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies.The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area.Specifically,we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements(Dataset I)and field measurements(Dataset II).In situ field measurements were acquired to verify the laboratory measurements.Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples.In addition,we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples.The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces.It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content.Based on sample data analysis,the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content,with R2 values of 0.45 and 0.40 corresponding to datasets I and II,respectively.Hence,the modified normalized difference index(the wavelengths of 2220,2290,and 2370 nm)identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas.The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.