Submitted as: development and technical paper 22 Jan 2021

Submitted as: development and technical paper | 22 Jan 2021

Review status: this preprint is currently under review for the journal GMD.

Automated geological map deconstruction for 3D model construction

Mark Jessell1, Vitaliy Ogarko2,6, Mark Lindsay1, Ranee Joshi1, Agnieszka Piechocka1,5, Lachlan Grose3, Miguel de la Varga4, Laurent Ailleres3, and Guillaume Pirot1 Mark Jessell et al.
  • 1Mineral Exploration Cooperative Research Centre, Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
  • 2International Centre for Radio Astronomy Research, The University of Western Australia, 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
  • 6ARC Centre of Excellence for all Sky Astrophysics in 3 Dimensions (ASTRO 3D)

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 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 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 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.

Mark Jessell et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on gmd-2020-400', Mark Jessell, 04 Feb 2021
    • EC1: 'Reply on AC1', Thomas Poulet, 05 Feb 2021
  • CEC1: 'Comment on gmd-2020-400', Astrid Kerkweg, 26 Feb 2021
    • AC2: 'Reply on CEC1', Mark Jessell, 03 Mar 2021
  • RC1: 'Comment on gmd-2020-400', Rob Harrap, 08 Mar 2021
  • RC2: 'Reviwer comment on gmd-2020-400', Stuart Clark, 30 Mar 2021

Mark Jessell et al.

Model code and software

map2loop code Mark Jessell and Yohan de Rose

Mark Jessell et al.


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Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to automatically build 3D geological models. By automating the precess we are able to remove human bias from the procedure, which makes the workflow reproducible.