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Optimizing the resources model of “horse shoe” cu-Au porphyritic deposit using unfolding geostatistical methods
Heriawan M.N.a, Indartoa, Abdillaha, Widayat A.H.a, Perdana A.b
a Research Group of Earth Resources Exploration, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung (ITB), Bandung, 40132, Indonesia
b Division of Geo Data and Modeling, PT. Freeport Indonesia, Tembagapura, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]One of the largest Cu-Au porphyritic deposits in Indonesia has a folded geometric like “horse shoe” due to the presence of last barren intrusion around the central part of orebody. The “horse shoe” folded geometric will generate not optimum resource model in term of producing high error and local variability in Cu-Au grades, because the spatial distribution of data points is not regular. In this research we try to transform the spatial distribution of all points data in 3D into an unfolded geometric by using two methods, rotation and projection matrices. The unfolded geometric will generate more regular data point. So the variography analysis as well as geostatistical estimation and conditional simulation on Cu-Au grades will be performed both in rotation and projection unfolded geometrics. These results were compared to the ones in original form as folded geometric in order to recognize the improvement of unfolded transformation results.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conditional simulations,Geostatistical estimation,Geostatistical method,Horse shoe,Ordinary kriging,Projection matrix,Sequential Gaubian Simulation,Unfolding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cu-Au grades,Horse shoe,Ordinary Kriging,Sequential Gaubian Simulation,Unfolding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]