Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of Yarlung Zangbo ophiolite belt, particularly harzburgite unit mantle section. Conducting field surveys complex poorly accessible terrain challenging, expensive, time-consuming. Remote sensing an advanced method achieving modern geological work powerful technical means research exploration. In order to delineate outcrops chromite-bearing peridotite, present study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, ratio, independent component analysis, principal minimum noise fraction, false color composite, interpretation Landsat8 OLI WorldView-2 satellite data. This integrated approach allows effective discrimination chromite-containing peridotite Luobusa area, Tibet. The results derived from these image-processing techniques were systematically verified formed basis feature selection process different lithologies supported by support vector machine algorithm. Furthermore, distribution range ferric contamination anomaly detected through de-interference abnormal thresholding technique, which shows high spatial matching relationship with peridotite. first utilize remote data explore largest China, enriches methods deposits area. Accordingly, this investigation indicate that integration information extracted algorithms using could be broadly applicable tool prospecting ore associated ophiolitic complexes mountainous inaccessible regions such as Tibet’s zones.