Abstract:
Identification of geochemical anomalies is of particular importance for tracing the footprints
of anomalies. This can be implemented by advanced techniques of exploratory data analysis,
such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical
elements. The latter workflow involves analysis of 2D/3D produced maps, which
can be mostly obtained by geostatistical algorithms. There are two challenging issues for
such an analysis. The first one corresponds to handling the cross-correlation structures
among the data, and the second one relates to the compositional nature of data. To tackle
these problems, this paper investigates the application of Gaussian co-simulation for modeling
the cross-correlated compositional data in order to recognize the multivariate geochemical
anomalies in integration with fractal analysis. In this context, an innovative
algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The
compositional nature of data is addressed by additive log-ratio transformation of original
data while the Gaussian co-simulation handles the reproduction of cross-correlation among
the components. The co-simulated outputs are then taken into account for capturing different
geochemical populations, showing different levels of backgrounds and anomalies. The
algorithm is illustrated via a real case study located in Philippine wherever seven geochemical
components are required to be considered. The accuracy of results is examined by
statistical validation techniques, indicating the capability of the CoSS-N algorithm for
multivariate identification of geochemical anomalies.