75
5 CONCLUSIONS
The model of the Grasberg porphyry deposit estimated by LVA kriging closely mimics the
spatial distribution of Cu, Au and Ag grades that characterize the deposit. High grade mineralization hosted in a central quartz-magnetite vein stockwork has a strong trend with CuAu-Ag grade contours decreasing outward in a concentric pattern. This pattern reflects the
lateral temperature gradient at the time of mineralization which controlled the precipitation
of Au and Ag bearing Cu-sulfides. The LVA estimation method dampens the impact of the
outwardly decreasing grade trends by directing the search path along the circular continuity. The goal of the estimation was not only to calculate the correct grade-tonnage curve for
long term planning, it was also critical to have the resource model accurately and realistically
represent the local spatial distribution of grades to support the block cave design. Other
tested methods that use linear anisotropy (directional search ellipsoid, pie slice selection,
local directional anisotropy, spherical isotopic, octant search) cannot adequately follow the
circular grade continuity of the primary mineralized domain. In this context, LVA kriging
was chosen as the solution for estimation in the GIC, and has proven effective.
REFERENCES
Boisvert, J.B., 2010. Geostatistics with Locally Varying Anisotropy. Ph.D. Dissertation, University of
Alberta, Edmonton, Alberta.
Boisvert, J.B., and Deutsch. 2011. Programs for kriging and sequential Gaussian simulation with locally
varying anisotropy using non-Euclidean distances. Computers & Geosciences 37: 495–510.
Deutsch, C.V. and Journel, A.G. 1992. GSLIB: Geostatistical Software Library and User’s Guide (2nd
Ed.), Oxford University Press, New York.
Leys, C.A., Cloos, M., New, B.T.E., and MacDonald, G.D. 2012. Copper-gold ± molybdenum deposits
of the Ertsberg-Grasberg District, Papua, Indonesia. In Hedenquist, J.W., Harris, M., and Camus,
F., (eds.) Society of Economic Geologists Special Publication 16: 215–235.
Lillah, M., and Boisvert, J.B. 2015. Inference of locally varying anisotropy fields from diverse data
sources. Computers & Geosciences 82: 170–182.
MacDonald, G.D., and Arnold, L.C. 1994. Geological and geochemical zoning of the Grasberg Igneous
Complex, Irian Jaya, Indonesia. Journal of Geochemical Exploration 50: 143–178.
Sapiie, B., and Cloos, M. 2004. Strike-slip faulting in the core of the Central Range of West New
Guinea, Ertsberg mining district, Indonesia. Geological Society of America Bulletin 116: 277–293.
5 CONCLUSIONS
The model of the Grasberg porphyry deposit estimated by LVA kriging closely mimics the
spatial distribution of Cu, Au and Ag grades that characterize the deposit. High grade mineralization hosted in a central quartz-magnetite vein stockwork has a strong trend with CuAu-Ag grade contours decreasing outward in a concentric pattern. This pattern reflects the
lateral temperature gradient at the time of mineralization which controlled the precipitation
of Au and Ag bearing Cu-sulfides. The LVA estimation method dampens the impact of the
outwardly decreasing grade trends by directing the search path along the circular continuity. The goal of the estimation was not only to calculate the correct grade-tonnage curve for
long term planning, it was also critical to have the resource model accurately and realistically
represent the local spatial distribution of grades to support the block cave design. Other
tested methods that use linear anisotropy (directional search ellipsoid, pie slice selection,
local directional anisotropy, spherical isotopic, octant search) cannot adequately follow the
circular grade continuity of the primary mineralized domain. In this context, LVA kriging
was chosen as the solution for estimation in the GIC, and has proven effective.
REFERENCES
Boisvert, J.B., 2010. Geostatistics with Locally Varying Anisotropy. Ph.D. Dissertation, University of
Alberta, Edmonton, Alberta.
Boisvert, J.B., and Deutsch. 2011. Programs for kriging and sequential Gaussian simulation with locally
varying anisotropy using non-Euclidean distances. Computers & Geosciences 37: 495–510.
Deutsch, C.V. and Journel, A.G. 1992. GSLIB: Geostatistical Software Library and User’s Guide (2nd
Ed.), Oxford University Press, New York.
Leys, C.A., Cloos, M., New, B.T.E., and MacDonald, G.D. 2012. Copper-gold ± molybdenum deposits
of the Ertsberg-Grasberg District, Papua, Indonesia. In Hedenquist, J.W., Harris, M., and Camus,
F., (eds.) Society of Economic Geologists Special Publication 16: 215–235.
Lillah, M., and Boisvert, J.B. 2015. Inference of locally varying anisotropy fields from diverse data
sources. Computers & Geosciences 82: 170–182.
MacDonald, G.D., and Arnold, L.C. 1994. Geological and geochemical zoning of the Grasberg Igneous
Complex, Irian Jaya, Indonesia. Journal of Geochemical Exploration 50: 143–178.
Sapiie, B., and Cloos, M. 2004. Strike-slip faulting in the core of the Central Range of West New
Guinea, Ertsberg mining district, Indonesia. Geological Society of America Bulletin 116: 277–293.