Gold Prospectivity Modeling by Combination of Laplacian Eigenmaps and Least Angle Regression

To integrate the evidential signatures into a mineral prospective map, it is necessary to model the observed data of metallogenic predictors with complex relationships. In this paper, a methodology was developed for gold prospectivity modeling by combining Laplacian eigenmaps (LEMS) and the least angle regression (LARS). LEMS were used to transform metallogenic predictors into Laplacian spectra to enhance the data classification ability, and the Laplacian spectra were used in LARS for gold prospectivity modeling. The Jinchanggouliang area, an important gold metallogenic district in China, was taken as the study area. Based on the geological and geochemical survey data of the study area, the LARS model, the combination of LEMS and LARS (LEMS–LARS) model, the logistic regression (LGR) model and the one-class support vector machine (OCSVM) model were each established for gold prospectivity modeling in the study area. The performances of these four models were measured using different performance evaluation methods. The receiver operating characteristic (ROC) analysis reveals that the LEMS–LARS model, the LGR model and the OCSVM model yield comparable performances and were superior to the LARS model, for gold prospectivity modeling in the study area. The prediction–area (P–A) plots of the four models show that the LEMS–LARS model, the LGR model and the OCSVM model each predicted about 20% of the study area as gold prospective areas, which contain about 80% of the known gold deposit locations. However, the performance of the LARS model was difficult to evaluate by the P-A plot because its prediction curve overlaps with its area curve, forming a straight-line segment, in the P-A plot. The gold prospective areas delineated optimally separately by the LEMS–LARS model, the LGR model and the OCSVM model are associated spatially with areas having favorable metallogenic conditions. The case study illustrates that the LEMS–LARS model can be used as a data-driven mineral prospectivity method, and its performance, in gold prospectivity modeling in the study area, is comparable to that of the LGR method and OCSVM method.