Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image

• Published in: Open Geosciences 2022-12, Vol.14 (1), p.1444-1465
• Main Authors: Lin, Nan, Liu, Hanlin, Li, Genjun, Wu, Menghong, Li, Delin, Jiang, Ranzhe, Yang, Xuesong
• Format: Article
• Language: English
• Subjects: Algorithms; ensemble learning; Geology; hyperspectral image; Indicators; Mineral exploration; Mineral resources; Mineralization; mineralized indicative minerals; Minerals; Optimization; Performance evaluation; Remote sensing; Resource exploration; Youden index
• ISSN: 2391-5447; 2391-5447
• DOI: 10.1515/geo-2022-0436

Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.