Data Mining Geoscientific Data Sets Using Self Organizing Maps

Geoscientists are increasingly challenged by the joint interpretation of ever-expanding amounts of new and historic, spatially-located exploration data (e.g., geochemistry, geophysics, geology, mineralogy, elevation data, etc.). And because, we can gather data faster than it can be interpreted, the availability of geographic information systems (GIS) has, to some extent, compounded, rather than reduced this problem. Research into the analysis and interpretation methods for data held in a GIS is in its infancy. A limited number of “advanced” interpretation methods have been developed; however, these often rely on a priori knowledge, training, or assumptions about mineralisation models. Objective, unsupervised methods for the spatial analysis of disparate data sets are needed. We have investigated and developed a new computational “tool” to assist in the interpretation of spatially located mineral exploration data sets. Our procedures are based on the data-ordering and visualization capabilities of the Self Organizing Map (SOM), combined with interactive software to investigate and display the spatial context of the derived SOM “clusters”. These computational procedures have the capacity to improve the efficiency and effectiveness of geoscientists as they attempt to discover and understand the often subtle signals associated with specific geological processes (e.g., mineralization), and separate them from the effects of overprinting noise caused by other processes such as metamorphism or weathering. Based on the principles of “ordered vector quantization”, the SOM approach has the advantage that all input data samples are represented as vectors in a data-space defined by the number of observations (variables) for each sample. The SOM procedure is an exploratory data analysis technique whereby patterns and relationships within a database are internally derived (unsupervised) based on measures of vector similarity (e.g., Euclidean distance and the dot product). The outputs of a SOM analysis are highly visual, which assists the analyst in understanding the data’s internal relationships.