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Intrinsic neutralisation potential from automated drillcore logging for improved geoenvironmental domaining

Authors :
Bell, LC
Edraki, M
Gerbo, C
Jackson, LM
Parbhakar-Fox, A
Fox, N
Cooke, DR
Harris, AC
Savinova, E
Bell, LC
Edraki, M
Gerbo, C
Jackson, LM
Parbhakar-Fox, A
Fox, N
Cooke, DR
Harris, AC
Savinova, E

Abstract

Hyperspectral analysis integrating near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectrometry can accurately identify carbonate-, silicate and serpentine-group minerals. Significantly these mineral-groups offer short and long term neutralising capacity respectively in a range of mine waste environments. Routine hyperspectral mineral analysis is increasingly used for geometallurgical and geological domaining of ore deposits, however the mineralogical information obtained is rarely used for deposit-scale geoenvironmental characterisation. Evaluating the location, volume, and chemistry of neutralising gangue materials early in the mine planning process will permit prudent economic forecasting with regards to mine closure and beyond. By accurately identifying carbonate, silicate and serpentine minerals in low (or below) grade material, their use within rock and tailings storage facilities can be appropriately determined helps to alleviate the need to acquire costly neutralising materials particularly at the time of mine closure. This study focusses on using data generated by hyperspectral mineralogy platforms to develop geoenvironmental domaining algorithms. Seven drill holes from a porphyry Au-Cu deposit were analysed, with hyperspectral results validated against established geoenvironmental characterisation tests (including acid base accounting and X-ray diffractometry). These data show that neutralising characteristics of differing alteration types can be predicted accurately from hyperspectral data. Furthermore, when used in conjunction with NAG pH and these data can be used to precisely identify neutralising zones.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1046087066
Document Type :
Electronic Resource