GEOCHEMICAL DATA ANALYSIS TECHNIQUES FOR GOLD EXPLORATION IN SUMBAWA, INDONESIA
Comparisons of the multivariate data analysis methods were based upon three factors. The first factor was reliability: a method was deemed reliable if it correctly identified nearly all of the catchments with known mineral occurrences. The second was ease-of-use: this criterion relates to the time and experience required to prepare and run each technique. The third factor was cost-effectiveness: this criterion incorporates reliability and consideration of operational costs associated with each technique in terms of both time and money.
Factor and cluster analyses do not require training data to adequately interpret the results. R-mode factor analysis, providing information on element associations per sample site, was the most reliable and cost-effective method, taking the least amount of time to interpret. Cluster analysis, providing information on the sample site associations, was the second most reliable and was estimated to be the third most cost-effective, based upon the description of the steps taken to complete the analysis by Sjoekri (1997). Discriminant analysis and most neural network techniques require training data sets to adequately interpret unknown data. Neither the orientation nor reconnaissance survey contained enough data to develop adequate training sets. Thus, discriminant analysis was the third most reliable, despite its ease of use, as it did not correctly identify most catchments with known mineral occurrences. The neural network technique was the least reliable of the four methods due to the lack of training patterns available resulting in an unstable network.