This paper focuses on researching the scientific problem of deep extraction and inference favorable geological geochemical information about mineralization at depth, based which a mineral resources prediction model is established machine learning approaches are used to carry out quantitative prediction. The main contents include: (i) discussing method 3D anomaly under multi-fractal content-volu...

This paper focuses on the scientific problem of quantitative mineralization prediction at large depth in Zaozigou gold deposit, west Qinling, China. Five geological and geochemical indicators are used to establish model. Machine learning Deep algorithms employed for 3D Mineral Prospectivity Mapping (MPM). Especially, Student Teacher Ore-induced Anomaly Detection (STOAD) model is proposed based ...

Knowledge-and data-driven approaches are two major methods used to integrate various evidential maps for mineral prospectivity mapping (MPM). Geological maps, geochemical samples and data from known gold deposits were collected in the western Junggar area, Xinjiang Province. The geological and a spatial database for geological and mineral occurrences were constructed for the studied region. A w...

Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust—quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in I...