Advanced methodologies for the analysis of databases of mineral deposits and major faults

Abstract

The effectiveness of some novel software tools used for clustering and classifying multivariate data is tested and used to evaluate mineral exploration criteria by examining a mineral deposit and major fault database. The database containing 364 diverse mineral deposits is divided into natural groups utilising a vector quantisation data-mining approach based on a self-organising map (SOM), and phenetic and cladistic analysis packages. The last two approaches are loosely based on biological principles of numerical taxonomy and evolutionary relationships, respectively. Based on the assumption of a common process of formation, the analyses are used to define the group (or class) of Archean orogenic-gold deposits, as distinct from gold deposit types such as turbidite-hosted orogenic gold, Carlin-type, and porphyry Cu -Au that should be excluded from this group. The main findings from this study are: (i) large, global-scale databases, representing the full range of commodity types, geographic locations, ages and variation in deposit characteristics, are required in order to classify new deposit examples using these techniques; (ii) traditional classifications are broadly correct but inadequately define deposit types; and (iii) SOM, and phenetic and cladistic analysis packages can aid in the identification of the characteristics (i.e. commodity, rock type, alteration, vein morphology, fluid composition) of the deposits that are mainly responsible for defining individual deposit groups. Applied to metallogenic terranes that host many different styles of mineralisation and deposit groups (such as Archean cratons), this approach can aid in identifying which deposits belong to a single coherent group. Analysis of the major fault database in this pilot study emphasises the need to obtain a significantly larger number of entries (total of 138 entries used, whereas 200 entries are required). It also highlights the impact of incomplete attribute data and the categorical nature of many of the datafields that describe faults. Nevertheless, preliminary results of several statistical analyses (Boolean, SOM, phenetic, cladistic) of the major fault database confirm the importance of empirically derived criteria for mineralisation, such as proximity to crustal-scale faults and anticlinal hinge zones, dilational jogs and fault roughness, strong rheological contrasts at lithological boundaries and metamorphic grade. Presence and concurrence of these parameters determine the extent of metallogenic endowment of a given fault system and segments within it.

Iron occurrence in soils and sediments of a coastal catchment

“…U-matrix. The U-matrix representation of the map indicates the closeness between adjacent nodes on the map in terms of Euclidean distance (Bierlein et al, 2008). A colour scale is used so that cool colours (blues) separate adjacent nodes that are closer or similar, while warm colours (reds) indicate larger distances and greater differences between the nodes.…”

Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

“…SOM aids visualization and interpretation by reducing n-dimensional (nD) multivariate data to a two-dimensional 'map' where the spatial arrangement of neighbouring groups is representative of their similarities in nD space (Penn 2005;Bierlein et al 2008). SOM uses vector quantization and measures of vector similarity, typically Euclidean distances, as a means of grouping input samples.…”

“…The resultant groups or nodes are represented by a vector (code-vector) that summarises the properties of the associated input samples. Visualization of SOM component planes assists the interpretation of patterns and structures within the input data (Penn 2005;Bierlein et al 2008;Löhr et al 2010). For more detailed descriptions of SOM implementation and theory see Sun et al (2009) and Cracknell et al (2015).…”

Supervised and unsupervised classification of near-mine soil Geochemistry and Geophysics data

“…This generates a mapping of inputs to trained seed nodes (node-vectors) onto a 2D space. The topology between node-vectors (clusters) is preserved such that those close in nD space maintain their relative proximities on the 2D map (Bierlein et al 2008).…”

“…SOM is an unsupervised clustering algorithm proven to be useful and efficient for exploring high dimensional geoscience data (e.g., Bierlein et al 2008, de Carvalho Carneiro et al 2012. SOM treats each sample as an n-dimensional vector in variable space.…”