Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods

Author:

Tuşa L.,Kern M.,Khodadadzadeh M.,Blannin R.,Gloaguen R.,Gutzmer J.

Publisher

Elsevier BV

Subject

Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Reference36 articles.

1. A machine learning framework for drill-core mineral mapping using hyperspectral and high-resolution mineralogical data fusion;Acosta;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2019

2. Geological and structural constraints on the localization of neogene porphyry– epithermal related Cu-Au (Mo), and epigenetic hydrothermal deposits/prospects from South Apuseni Mts., Romania;Berbeleac;Rom. J. Miner. Depos.,2014

3. Blannin, R., Tusa, L., Birtel, S., Gutzmer, J., Gilbricht, S., Ivascanu, P., 2019. Metal deportment and ore variability of the Bolcana porphyry Au-Cu system (Apuseni Mts, Romania) - implications for ore processing. 15th SGA Biennial Meeting 2019 Proceedings.

4. Random forests;Breiman;Mach. Learn.,2001

5. Evaluation of magnetic separation efficiency on a cassiterite-bearing skarn ore by means of integrative SEM-based image and XRF–XRD data analysis;Buchmann;Minerals,2018

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