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Exploratory Analysis of Geochemical Anomalies                         75























           Fig. 3-17. Anomalies in the log e -transformed As data subsets according to rock type at sample
           points and  exclusive of As censored values (see Table 3-IV), Aroroy district (Philippines).
           Anomalies are  based on threshold defined as  (A) mean+2SDEV, (B) median+2MAD and (C)
           boxplot UW. Triangles represent locations of epithermal Au deposit occurrences. Light-grey lines
           represent lithologic contacts (see Fig. 3-9).

           defined by each of the three methods are apparently non-problematic (Table 3-IV), so
           they are  mapped to study the anomalies and compare the performance of the three
           methods (Fig. 3-17). It is obvious that the As anomalies based on threshold defined as
           median+2MAD show the best spatial associations with the known epithermal Au deposit
           occurrences in the case study area (Fig. 3-17B). The northwestern most cluster of As
           anomalies are associated  with hydrothermally altered volcano-sedimentary rocks. The
           As anomaly map in Fig. 3-17B is even better than the As anomaly map in Fig. 3-16B. In
           the former, there are 18 anomalous samples (or at least 13%) of the 135 samples, which
           is probably why the median+2MAD threshold outperforms the boxplot UW threshold as
           well as the mean+2SDEV threshold.

           Analysis of inter-element relationships
              In the preceding analysis of the uni-element data distributions, it can be perceived
           that probably the most dominant inter-element relationships in the study area is due to
           lithology (see Table 3-II and Fig. 3-13). That is, parts of the study area underlain by
           diorite have relatively lower concentrations  of the elements under study compared to
           parts  of the study area  underlain by  dacitic/andesitic volcano-sedimentary rocks. It is
           important to further unravel other inter-element relationships in the data, which may be
           useful in the interpretation of significant geochemical anomalies. For example, in this
           case study it is instructive to determine further (a) whether or not anomalies of As are
           plausibly due  to scavenging by Mn-oxides and  (b) whether there are inter-element
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