Geostatistical Modelling of a High-grade Iron Ore Deposit View Full Text


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Article Info

DATE

2021-09-07

AUTHORS

Rahul K. Singh, Bhabesh C. Sarkar, Dipankar Ray

ABSTRACT

Precambrian banded iron formations (BIFs) of Singhbhum-Keonjhar-Bonai iron ore belt in eastern India host several high grade iron ore deposits. The deposits have been formed due to supergene enrichment of parent BIFs by gradual removal of SiO2 and Al2O3 under continuous process of leaching. With intensity of leaching differing spatially, the deposits exhibit variation in Fegrade both laterally and vertically. The extent of such variation in space is a function of the scale at which observations are made. Modelling of spatial variabilities at observation scale provides an appropriate means to estimate spatially distributed block values. The present study focusses on spatial variability analysis, ordinary kriging (OK) and sequential Gaussian simulation (SGS) for Fegrade modelling using drill-hole exploration data of a high grade iron ore deposit located in the Singhbhum-Keonjhar-Bonai iron ore belt. Classical statistical modelling revealed a three-parameter lognormal fit to the negatively skewed Fe distribution, and a two-parameter lognormal fit to the positively skewed SiO2 and Al2O3 distributions. Spatial variability modelling revealed a spherical model fit in respect of Fe, SiO2 and Al2O3. A moderately high ratio of nugget-to-sill variance reflect intrinsic characteristic of banded nature of Fe mineralization as evident in BIFs with varying proportions of iron, alumina and silica. Blocks of 15m × 15m × 10m size equalling the dimension of a mining unit configured within a 3D volume bounded vertically between 875 mRL and 705 mRL and laterally between 310W to 540E and 260S to 1600N have been evaluated employing OK. Stacking of altogether seventeen horizontal slices vertically down from 870 mRL to 710 mRL led to an estimate of iron ore inventory as 124.48 mt with overall mean kriged estimate as 63.93% and mean kriging variance as 2.83 (%)2. Statistical regression analysis of OK estimates and original sample values provided a regression slope of 1.03, thereby qualifying the conditional unbiasedness of the OK estimates. SGS study provided multiple equi-probable realisations with histogram and spatial variability closely approximating to that of the original sample values. More... »

PAGES

1005-1012

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http://scigraph.springernature.com/pub.10.1007/s12594-021-1815-y

DOI

http://dx.doi.org/10.1007/s12594-021-1815-y

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https://app.dimensions.ai/details/publication/pub.1140944156


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