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Knowledge discovery on drilling data to predict potential gold deposit
Saptawati G.A.P.a, Nata G.N.M.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 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]© 2015 IEEE.Drilling is one of activities in mineral exploration Industry which is very important, risky and the most expensive. However, geologist are still using qualitative judgments in determining drilling targets. As a result, the number of failures in drilling become very high. Prediction of drilling target is important to minimize the risk of failure and minimize the loss of opportunities to find a new drilling area. Drilling data consists of geochemical data, geophysical data and geological data. Based on the theory of magmatic-hydrothermal, geophysical data and geochemical data, it is possible to build predictive models to predict potential subsurface Au. Meanwhile, geological data can be used to determine the tendency of gold presence along with lithology and alteration. The problem arises when determining data mining techniques to be used to support the prediction of mineral potential, how to represent drilling data for the mining process. This research focused on the analysis of data mining techniques to mine the knowledge from drilling data, and also the drilling data representation for the mining process. From the analysis result, the classification of data and frequent itemsets mining is capable to support the prediction of drilling targets. The test results using two areas of exploration drilling show that classification on drilling data can be used to predict the potential for new drilling target. Moreover, frequent pattern mining can be used to mine the occurrence pattern of Au together with lithology and alteration. Both the results of data mining can help mineral exploration industries in predicting the Au subsurface potential. The goal is to minimize the risk of failure of drilling and support the industry’s decision to be quantitatively decide a new drilling target.[/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]Classification of data,Drilling targets,Exploration drilling,Frequent itemsets minings,Frequent pattern mining,Geochemical data,Occurrence pattern,Predictive models[/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]classification,data mining,drilling,frequent pattern mining,mineral exploration[/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]https://doi.org/10.1109/ICODSE.2015.7436987[/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]