MA Xiumei; ZHOU Kefa; WANG Jinlin; CUI Shichao; ZHOU Shuguang; WANG Shanshan; ZHANG Guanbin
Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands, high resolution, and abundant information. Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks, the influence of bandwidth on the inversion accuracy are ignored. In this study, we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City, Xinjiang Uygur Autonomous Region, China and measured the ground spectra of these samples. The original spectra were resampled with different bandwidths. A Partial Least Squares Regression (PLSR) model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored. According to the results, the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm, with the model determination coefficient (R2) of 0.5907. The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5–80 nm, but the accuracy decreases significantly at 85 nm bandwidth (R2=0.5473), and the accuracy gradually decreased at bandwidths beyond 85 nm. Hence, bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model. This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.
CUI Shichao; ZHOU Kefa; ZHANG Guanbin; DING Rufu; WANG Jinlin; CHENG Yinyi; JIANG Guo
|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 6.56 μg/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 7.07 μg/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.|