[Hyperspectral Inversion Analysis of Heavy Metal Content in Soil of Lead-Zinc-Copper Mining Area Based on Machine...

In view of the low content of heavy metals in soil， it is difficult to estimate the heavy metal content by directly analyzing the characteristic spectrum of heavy metals in weak soil spectral information. A combination scheme of the hyperspectral inversion model for soil heavy metal content was proposed， which combined multiple spectral transformation methods， such as first-order differential （FD）， second-order differential （SD）， and reciprocal logarithm （AT）， and four machine learning algorithms including random forest （RF）， support vector machine （SVM）， multiple stepwise linear regression （SMLR）， and artificial neural network （ANN）. Firstly， the XRF detection data and reflectance spectra of the soil samples were collected and transformed by FD， SD， AT， and other mathematical methods. Then， the correlation between the soil heavy metal content and the transformed spectral characteristics was systematically analyzed， and the representative characteristic bands were screened out. Finally， the inversion effects of the four modeling methods， RF， SVM， SMLR， and ANN， were compared based on the feature bands， and the optimal model selection of each element was determined by accuracy evaluation. The results show that： The five spectral changes in preprocessing effectively improved the signal-to-noise ratio of the image and reduced the intervals and differences between spectra， and the effects of the spectral changes， excluding the transformation method with a characteristic band of 0 （AT and ASD）， were in the order of SD > FD > AFD. All four machine learning models had certain predictive capabilities， and the determination coefficients R² of the model effects were almost greater than 0.6. The best prediction models for this mining area in order were SMLR > SVM > ANN > RF. The best inversion models for both the Cu and Mn elements were SD-SMLR models， the best inversion model for the Fe element was the SD-SVM model， and the best inversion model for the Zn element was the AFD-SMLR model. The research results provide technical support for large-scale measurement of heavy metal element content in lead-zinc-copper mining areas and offer a new development path for environmental monitoring.