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\begin{document}

\title{Mineral prospectivity mapping of tungsten polymetallic deposits using
machine learning algorithms and comparison of their performance in the
Gannan region of China}



\author[1]{Yonghang Lou}%
\author[1]{Yue Liu}%
\affil[1]{China University of Geosciences}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
The current study aimed at assessing the capabilities of five machine
learning models in term of mapping tungsten polymetallic prospectivity
in the Gannan region of China. The five models include logistic
regression (LR), support vector machine (SVM), random forest (RF),
convolutional neural network (CNN), and light gradient boosting machine
(LGBM) models. Geochemical, lithostratigraphic, and structural datasets
were used to generate 16 evidential maps, which were integrated into the
machine learning models. Tungsten polymetallic deposits were randomly
separated into two parts: 80\% for training and 20\% for validating.
Performances of the models were evaluated through receiver operating
characteristic (ROC) and K-fold cross validation, with an emphasis on
the variable influence within different machine learning methods. The
results show that the models are especially sensitive to the chemical
elements: Be, Bi, Pb and Cd, implying that these are closely related to
tungsten polymetallic mineralization. Compared to other models, the LGBM
and CNN models performed best, while the LR model was the most stable.
The results also indicated that the CNN model can predict maximum known
deposits within a minimum area, based on the prediction-area plot
analysis of the five models, while the RF model can capture the most
well-known deposits within the smallest study area. Finally, eighteen
prospective areas were delineated according to the predicting results of
the machine learning models, which will provide important guidance for
further tungsten polymetallic exploration and associated studies.%
\end{abstract}%



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