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Journal of Arid Land  2021, Vol. 13 Issue (11): 1183-1198    DOI: 10.1007/s40333-021-0068-3
Geography, geology and natural resources in Central Asia (Guest Editorial Board Member:Prof. Dr. XIAO Wenjiao)     
A new method of searching for concealed Au deposits by using the spectrum of arid desert plant species
CUI Shichao1,2,3,4, ZHOU Kefa1,2,3,4,*(), ZHANG Guanbin5,*(), DING Rufu6, WANG Jinlin1,2,3,4, CHENG Yinyi1,2,3,4, JIANG Guo1,2,3,4
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
3Xinjiang Research Centre for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5Xinjiang Academy of Science and Technology for Development, Urumqi 830011, China
6China Non-Ferrous Metals Resources Geological Survey, Beijing 100012, China
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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.

Key wordsconcealed Au deposits      reflectance spectroscopy      soil Au content      characteristic band      soil geochemical prospecting      competitive adaptive reweighted sampling      Seriphidium terrae-albae     
Received: 28 September 2020      Published: 10 November 2021
Corresponding Authors: ZHOU Kefa, ZHANG Guanbin     E-mail:;
Cite this article:

CUI Shichao, ZHOU Kefa, ZHANG Guanbin, DING Rufu, WANG Jinlin, CHENG Yinyi, JIANG Guo. A new method of searching for concealed Au deposits by using the spectrum of arid desert plant species. Journal of Arid Land, 2021, 13(11): 1183-1198.

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Fig. 1 Natural landscape of the study area
Fig. 2 Demonstration of the locations of sampling points. Note that the numbers represent the sampling points.
Element type Minimum value
Maximum value
Average value
Standard deviation
Coefficient of variation
Au 0.3437 77.5711 6.0543 15.7080 2.5945
Table 1 Statistics of the Au content in the 48 collected soil samples
Fig. 3 Reflectance spectra of the collected 48 plant samples
Fig. 4 Distribution of the soil Au content at the 48 sampling points
Fig. 5 Number and average Au content of soil samples grown in mineralized and non-mineralized altered zones. Note that boxes represent interquartile ranges (25th to 75th percentiles); thick horizontal bars in each box denotes the median (50th percentile); whiskers (thin horizontal bars) represent the highest and the lowest values, respectively; red and green rectangles denote the average value; and red and green dots represent the Au content of each soil sample in the mineralized altered zone and non-mineralized altered zone, respectively.
Table 2 Comparisons of the R2, RMSE, and RPD values obtained using leave-one-out cross-validation method that constructed by 15 different transformed spectra for each of four band selection methods to estimate the soil Au content
Fig. 6 Scatter diagram of the predicted soil Au content obtained using the leave-one-out cross-validated method versus measured soil Au content. R2, coefficient of determination; RMSE, root-mean-squared error; RPD, ratio of performance to deviation; 1/lnR, reciprocal of logarithmic spectrum; ', first derivative.
Fig. 7 M1 and M3 values of the different characteristic band screening methods (a), and M2 value of the different characteristic band screening methods and the numbers of characteristic bands in the models (b). M1, the mean value of the coefficient of determination (R2) obtained by the leave-one-out cross-validation of 15 estimation models based on different transform spectra; M2, the mean value of the root-mean-squared error (RMSE) obtained by the leave-one-out cross-validation of 15 estimation models based on different transform spectra; M3, the mean value of the ratio of performance to deviation (RPD) obtained by the leave-one-out cross-validation of 15 estimation models based on different transform spectra; GA, genetic algorithm; STE, stepwise regression analysis; CARS, competitive adaptive reweighted sampling; CC, correlation coefficient method.
Number of characteristic bands Band distribution range (nm)
56 412-427, 517, 542, 577, 652, 692, 772, 842, 922, 947, 1032, 1047-1052, 1092, 1127, 1147, 1167-1172, 1197, 1212, 1402, 1427, 1457, 1487, 1622-1627, 1652, 1672, 1692-1697, 1702, 1712-1727, 1737, 1782, 1792, 2022, 2042, 2127, 2147-2152, 2167, 2197, 2242, 2257, 2292-2297, 2322-2327, 2342, 2352
Table 3 Distribution of characteristic bands in the spectral transformations (ST14 (1/lnR)') selected by the competitive adaptive reweighted sampling (CARS) method
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