Mapping Himalayan leucogranites using a hybrid method of metric learning and support vector machine

Rare metals play a considerable role in the development of new materials and energy, making them key mineral resources for global competition. Widely distributed along the Himalayan orogen, the Himalayan leucogranite belt is expected to be an important rare metal metallogenic belt in China. Thus, mapping the spatial distribution of Himalayan leucogranites is critical for prospecting rare metal deposits. The distribution characteristics of geochemical elements are important indicators for lithological identification. The differences in mineral composition and major oxide content between leucogranites and the surrounding rocks facilitate lithological mapping. However, significant uncertainty could arise owing to limited geochemical data due to particularly adverse working conditions and to difficulty in handling similar geochemical data. In this study, a metric learning-based approach is used for mapping leucogranites based on regional geochemical exploration datasets. Defined as a measure of similarity between two samples, metric learning reveals a "better distance" by converting original data into a more suitable Mahalanobis metric space with maximum separation of the target and the background. In this approach, a local weighted metric learning method is first used to assign weights to the training samples in the neighborhood, with respect to their reconstruction contributions in learning the local metric. Then, a discriminative local ensemble learning method is employed to integrate all learned metrics and to convert the original geochemical data into a metric space. This enables more effective separation of highly similar target leucogranites from the surrounding rocks with the help of a support vector machine. The distribution of leucogranites mapped by such a hybrid method showed high consistency with the geological map, indicating that this approach is reasonable for providing the indicated signature of leucogranites mapping in the study area. These results further provide an alternative way for identifying favorable intrusions based on geochemical exploration data.