Abstract
Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.
Highlights
genetic algorithm (GA) is used to optimize the support vector machine (SVM) parameters σ and C based on the process of natural selection, in which Accuracy is adopted as the fitness function to evaluate the quality of the solutions
After setting the initial parameters for GA and SVM, the training dataset was used to train an SVM model, while the fitness was calculated by k-fold cross-validation classification accuracy
From the statistical comparison between the GA–SVM models with the four training datasets (Table 7), it is obvious that the GA–SVM model that used training dataset 2, which occupied the largest area of the study area, had a larger number of known Au deposits, accounting for the poor spatial efficiency of the prospectivity model, the F1 score was the highest
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. (2) Data-driven MPM methods analyze and quantify spatial associations between each evidence layer and the locations of known deposits that share a common genesis, and include weights of evidence [14,15], evidence belief functions [16,17], and logistic regression [18,19] These methods are commonly applied in well-explored areas with sufficient known mineral deposits of the desired type. SVM and GA were combined to optimize parameter design and develop a predictive model for mapping Au prospectivity zones in Karamay, NW China For this purpose, after constructing five evidence layers from geological and geochemical data using spatial data processing methods and a prediction-area (P-A) plot [34,35], point pattern analysis was employed to estimate and randomly select non-mineralized samples based on the selection criteria. The F1 score and spatial efficiency were compared between different prospectivity models to evaluate their performance
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