Quantification Model of Qualitative Geological Data Variables for Exploration Risk Assessment in Prospect Cu-Au Porphyry Deposit Randu Kuning, Wonogiri, Central Java - Nurkhamim Nurkhamim; Arifudin Idrus; Agung Harijoko; Irwan Endrayanto; Sapto Putranto (UNEJ e-Proceeding, 2016)
Almost geological data variable contain some degree of uncertainty. Most decisions in mineral exploration was based on geological reports, measurements, calculations as well as ignorance of the geological uncertainty underlies all natural risks of the exploration effort. Risks affecting mineral exploration activities, among others caused by several things. Inherent natural variability in the process of geology and geological objects. Uncertainty on the conceptual and models, associated with incomplete knowledge and subjective interpretations of processes and geological objects. Errors can also occur when observing, measuring or evaluating samples or mathematical analysis of geological data. Data from exploration activities, can be grouped into two types of data, namely quantitative data (e g; grade) and qualitative data (geological data). Geological data variables still largely a qualitative data, resulting between some geologists are not infrequent errors of judgment (assessment of subjective data). This leads to misinterpretation of results of exploration that will ultimately impact on the exploration risk assessment. Currently, the quantification of qualitative data variable is one parameter which is becoming a necessity, because it will be easier in terms of interpretation, communication and measurable. Porphyry Cu - Au deposit in the Randu Kuning Prospect, Wonogiri has the characteristic geometry and grade distribution are quite complex. It is characterized by the appearance of some kind of vein and stockwork with different characteristics. Quantification of geological variables will result in a value that allows the quantification in quantifying exploration risk. For quantitative variables data (grade) using geostatistical methods, while for qualitative variables geological data using canonical correlation and multivariable regression.