Abstract. At a regional scale, the best predictor for the 3D geology of the
near-subsurface is often the information contained in a geological map. One
challenge we face is the difficulty in reproducibly preparing input data for
3D geological models. We present two libraries (map2loop and map2model) that automatically
combine the information available in digital geological maps with conceptual
information, including assumptions regarding the subsurface extent of faults
and plutons to provide sufficient constraints to build a prototype 3D
geological model. 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 or their
spatial adjacency relationships. This automation provides significant
advantages: it reduces the time to first prototype models; it clearly
separates the data, concepts, and interpretations; and provides a homogenous
pathway to sensitivity analysis, uncertainty quantification, and value of
information studies that require stochastic simulations, and thus the
automation of the 3D modelling workflow from data extraction through to
model construction. We use the example of the folded and faulted Hamersley
Basin in Western Australia to demonstrate a complete workflow from data
extraction to 3D modelling using two different open-source 3D modelling
engines: GemPy and LoopStructural.
Inferring symmetric and asymmetric interactions between animals and groups from positional data
