Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach
Highlights
► Palaeogeography was found to be a strong indicator of Australian precious opal. ► A data-mining methodology was used to quantitatively extract key time sequences. ► Only 11% of the entire Great Artesian Basin undergoes favourable palaeogeography. ► Important environmental transitions were extracted using data-mining approaches. ► The methodology was used to construct a map indicating favourable regions.
Introduction
Exploration practices and data analysis and modelling related to mineral and hydrocarbon systems are making increasing use of integrated geological datasets for understanding resource formation processes and thus improving exploration decision making. This is made possible by the rapid increase in the interconnected digital storage of data and the rapidly increasing power of computing systems. An important factor for many studies is the consideration of the palaeogeographic/depositional environment through time as inferred from the geological record, since the configuration of a particular region as observed today is nearly always substantially different from the geological past. This, together with an understanding of the physical processes involved, is particularly important for identifying niche geological conditions that result in a phenomena such as ore deposits, since only a consideration of the relevant geological history leads to an appropriate contextualisation of present-day observations. Thus investigating the nature and relationships involved in time-varying spatial data is a promising area for developing and applying machine learning to geological data. In this paper we develop a quantitative approach for utilising the palaeo-environmental history over a large portion of Australia to investigate some key factors associated with opal formation, as a step towards establishing more quantitative exploration criteria. A data-mining approach is taken here to cope with the large datasets involved, and to handle some degree of noise present in the datasets utilised, especially considering that the digital palaeogeographic maps this approach is based on involves interpretations and interpolations of sparse data points. The study is made possible through use of the GPlates plate tectonic geographic information system (Qin et al., 2012) and Gplates data-mining functionality (Landgrebe and Mueller, 2008), in which time-varying data are explicitly modelled, allowing for direct extraction of spatio-temporal associations.
Opal is a form of hydrated silica (SiO2·n H2O) found predominantly throughout the Great Artesian Basin in Australia. Although the Australian opal fields are responsible for over 90% of the world's opal supply, there has been a decline over the past 20 years in the number of high quality gemstones produced by the fields (Smallwood, 1997). This is accounted for, to some extent, by the absence of formal exploration models for Australian opal, which has led to an over-reliance on mining old opal fields discovered in the early 20th century (Barnes and Townsend, 1990). Making use of known locations of opal, we construct a method allowing us to search for other locations that possess similar palaeogeographic sequences throughout the entire Great Artesian Basin, and help establish important time-varying palaeogeographic/depositional/facies conditions favourable for opal formation.
Section snippets
Eastern Australian opals and palaeogeography
Opal in the Great Artesian Basin is found within fractures and primary and secondary pore spaces in the upper 30 m throughout the highly weathered Cretaceous sedimentary sequence of the Eromanga and Surat basins (Barnes and Townsend, 1990). The location of opal deposits is shown in Fig. 1, and includes primary opal mining regions from which a total of 1036 mining localities were identified for this study. The locations were taken from maps published by the state geological surveys (Carr, 1979,
Formalising the palaeogeographic time-series analysis
The quantitative methodology followed in this paper utilises the palaeogeographic dataset, referenced with known opal localities to derive a data-driven model of target palaeogeographic sequences. The digital representation consists of spatial zones defined by polygons, with associated geographical attributes. We define the unique set of 16 different palaeo-environment types , where the ith type is referred to as (the environment types are listed in Table 1). In this study it
Event sequence pattern-matching
Eq. (2) provides the means to represent the nature of the palaeogeographic evolution at a particular location without requiring absolute ages. This is important for opal exploration, since direct radiometric dating of Australian opal has not been possible due to its low uranium content (Gaillou et al., 2008). Consequently, it has not been possible to constrain the age of Australian opal further than the age of its host rock, which is predominantly Cretaceous, with a single location (Mintabie)
Computing methodology
The plate-tectonic Palaeogeographic Information System GPlates was used as the basis for this study (Boyden et al., 2011). GPlates provides the ability to assess multiple spatial datasets that include time-varying properties, allowing for visualisations and analyses coherent both in space and time. Thus factors such as a varying geographic environment, or spatial relationships between different datasets undergoing plate motions or crustal deformation, can be assessed. GPlates has been augmented
Results and validation
The result of the pattern matching methodology is shown in Fig. 5, with the Cretaceous sedimentary rocks highlighted, and filled black regions depicting areas matching the trained palaeogeographic patterns (targeted regions). Two general observations can be made. The first observation is that there is a strong correlation between the region encompassing Cretaceous sedimentary rock and the targeted regions. This is an expected result since this region has a unique palaeographic characteristic
Conclusions
This paper presents a quantitative methodology for utilising age-coded geological data to develop models/targeting criteria for opal exploration. Such data-driven approaches are becoming increasingly viable as the growth in the storage of digital geological data continues to accelerate, allowing for repeatable and less-subjective analyses. A palaeogeographic dataset consisting of age-coded geological environments in Australia was used to assess the palaeogeographic evolution at known opal
Acknowledgements
This research was funded by ARC Grants FL0992245 and DP0987604. We would also like to thank the reviewers for their constructive feedback.
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