Reviwer comment on gmd-2020-400

Abstract

We present two Python libraries (map2loop and map2model) which combine the observations 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 reasonable 3D geological model. At a regional scale, the best predictor for the 3D 15 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 potential for hidden biases that this 'model' status implies. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. 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 20 their adjacency relationships. This work is being conducted within the Loop Consortium, in which algorithms are being developed that allow automatic deconstruction of a geological map to recover the necessary positional, gradient and topological data as inputs to different 3D geological modelling codes. This automation provides significant advantages: it reduces the time to first prototype models; 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 25 Information studies. We use the example of the re-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.