Albitized granite (ABG) is considered as one of the most significant hosts of rare metals
(RMs). Consequently, adequate recognition of ABG through proper lithological …

Machine learning is a subcategory of artificial intelligence, which aims to make computers
capable of solving complex problems without being explicitly programmed. Availability of …

Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …

Due to the combined influences such as ore-forming temperature, fluid and metal sources,
sphalerite tends to incorporate diverse contents of trace elements during the formation of …

Lithological mapping is a critical aspect of geological mapping that can be useful in studying
the mineralization potential of a region and has implications for mineral prospectivity …

There has been in recent years a trend towards adopting deep neural networks for
addressing earth science problems. Of the various deep neural networks applied to different …

Data-driven mineral prospectivity mapping (MPM) based on deep learning methods has
become a powerful tool for mineral exploration targeting in the past years. Convolutional …

The operation of large-scale ore-forming processes triggers the development of neighboring
mineral deposits of the same or related types in a metallogenic province. While these …

Prospecting and exploring minerals present major challenges in tectonically complex
regions for sustainable development as in Northeastern Algeria. This area is promising for …

Abstract The Nanling Range in South China is endowed with abundant W-Sn and other
important rare metal resources associated with granitic intrusions, but the rate of new major …