Jessell, Mark1, Ogarko, Vitaliy2, Lindsay, Mark1, Joshi, Ranee1, Piechocka, Agnieszka1,5, Grose, Lachlan3, de la Varga, Miguel4, Fitzgerald, Des6, Aillères, Laurent3, Pirot, Guillaume1
1Mineral Exploration Cooperative Research Centre, Centre for Exploration Targeting, School of Earth Sciences, UWA, Perth, Australia, 2International Centre for Radio Astronomy Research, UWA, Perth, Australia, 3School of Earth, Atmosphere and Environment, Monash University, 4Computational Geoscience and Reservoir Engineering, RWTH Aachen, Germany, 5CSIRO, Mineral Resources – Discovery, ARRC, Kensington WA, Australia
The advent of digital geological maps has not been matched by an uptake of analysing the structural data contained within. At the regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. This remains true even after recognising that a map is also a model, with all the attendant hidden biases ‘model’ status implies. The difficulty in reproducibly preparing input data for 3D geological models has created a demand for increased automation in the model building process. The information stored in a map falls into three categories of geometric data: positional data such as the position of faults, intrusive and stratigraphic contacts; gradient data, such as the dips of contacts or faults and topological data, such as the age relationships of faults and stratigraphic units.
We present two Python libraries (map2loop and map2model) which combine all these observations with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a reasonable 3D geological model. These algorithms allow automatic deconstruction of a geological map to recover the necessary positional, topological and gradient data as inputs to different 3D geological modelling codes. This automation provides significant advantages: it significantly reduces the time to first prototype models; it produces reproducible models for the same source data, it clearly separates the primary data from subsets produced from filtering via data reduction and conceptual constraints; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification and Value of Information studies. We use examples of the folded and faulted terrains across Australia to demonstrate a complete workflow from data extraction to 3D modelling using three different 3D modelling engines: GemPy, LoopStructural and 3D GeoModeller.
We acknowledge the support of the MinEx CRC and the Loop: Enabling Stochastic 3D Geological Modelling (LP170100985) consortia. The work has been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Programme. This is MinEx CRC Document 2020/48.
Mark Jessell is a Professor at the Centre for Exploration Targeting at The University of Western Australia. His current scientific interests revolve around integration of geology and geophysics in 3D (the Loop project), and the tectonics and metallogenesis of the West African and Guyanese Cratons (WAXI & SAXI).