Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

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.

Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

“…Compared with traditional neural networks, the topology of the SVM is determined by support vectors, which can avoid the problem of conventional neural networks that need to be repeatedly adapted to determine the network structure. In contrast, the SVM uses nonlinear kernel functions to map the original vectors to a high-dimensional feature space, ensuring the good generalization of the model and overcoming the issue of dimensionality [21]. The SVM can be used for pattern classification and regression, and in this paper, it is applied to regression.…”