TY - JOUR
T1 - Mineral Resource Classification Using Geostatistical and Fractal Simulation in the Masjed Daghi Cu–Mo Porphyry Deposit, NW Iran
AU - Afzal, Peyman
AU - Gholami, Hamid
AU - Madani, Nasser
AU - Yasrebi, Amir Bijan
AU - Sadeghi, Behnam
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - Mineral resource classification is an important step in mineral exploration and mining engineering. In this study, copper and molybdenum resources were classified using a combination of the Turning Bands Simulation (TBSIM) and the Concentration–Volume (C–V) fractal model based on the Conditional Coefficient of Variation (CCV) for Cu realizations in the Masjed Daghi porphyry deposit, NW Iran. In this research, 100 scenarios for the local variability of copper were correspondingly simulated using the TBSIM and the CCVs were calculated for each realization. Furthermore, various populations for these CCVs were distinguished using C–V fractal modeling. The C–V log–log plots indicate a multifractal nature that shows a ring structure for the “Measured”, “Indicated”, and “Inferred” classes in this deposit. Then, the results obtained using this hybrid method were compared with the CCV–Tonnage graphs. Finally, the results obtained using the geostatistical and fractal simulation showed that the marginal parts of this deposit constitute inferred resources and need more information from exploration boreholes.
AB - Mineral resource classification is an important step in mineral exploration and mining engineering. In this study, copper and molybdenum resources were classified using a combination of the Turning Bands Simulation (TBSIM) and the Concentration–Volume (C–V) fractal model based on the Conditional Coefficient of Variation (CCV) for Cu realizations in the Masjed Daghi porphyry deposit, NW Iran. In this research, 100 scenarios for the local variability of copper were correspondingly simulated using the TBSIM and the CCVs were calculated for each realization. Furthermore, various populations for these CCVs were distinguished using C–V fractal modeling. The C–V log–log plots indicate a multifractal nature that shows a ring structure for the “Measured”, “Indicated”, and “Inferred” classes in this deposit. Then, the results obtained using this hybrid method were compared with the CCV–Tonnage graphs. Finally, the results obtained using the geostatistical and fractal simulation showed that the marginal parts of this deposit constitute inferred resources and need more information from exploration boreholes.
KW - concentration–volume (C–V) fractal model
KW - conditional coefficient of variation (CCV)
KW - mineral resource classification
KW - turning bands simulation (TBSIM)
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U2 - 10.3390/min13030370
DO - 10.3390/min13030370
M3 - Article
AN - SCOPUS:85151624094
SN - 2075-163X
VL - 13
JO - Minerals
JF - Minerals
IS - 3
M1 - 370
ER -