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Enabling 3D stochastic geological modelling

Australia

The Loop platform is an open source 3D probabilistic geological and geophysical modelling platform, initiated by Geoscience Australia and the OneGeology consortium. The project is funded by Australian territory, State and Federal Geological Surveys, the Australian Research Council and the MinEx Collaborative Research Centre.

Loop is led by Laurent Ailleres (Monash University) with a team of Work Package leaders from:

  • Monash University: Roy Thomson, Lachlan Grose and Robin Armit
  • University of Western Australia: Mark Jessell, Jeremie Giraud, Mark Lindsay and Guillaume Pirot
  • Geological Survey of Canada: Boyan Brodaric and Eric de Kemp

  • Data Processing
    Package Lead: Mark Jessell
    Work Package 2 will provide Loop with the ability to directly access online and offline geoscientific datasets, which it will then process to extract the data required for 3D Geological Modelling. This includes re-projection, upscaling, information extraction and exporting in native formats aimed at a variety of implicit 3D Geological modelling engines.

    Geophysical Modelling
    Package Lead: Jeremie Giraud
    Work Package 4 will provide Loop with the possibility to connect geological modelling with geophysical measurements and uncertainty through a multi-purpose geophysical inverse modelling engine. This engine will be capable of running as a standalone or using uncertain geological model, be it from a deterministic aspect or in a probabilistic-oriented fashion. The geophysical engine will be able to use the output of, and feed into, WP3 and WP5.

    Model Analysis and Uncertainty Reduction
    Package Lead: Mark Lindsay
    Work Package 5 will offer loop several tools to improve the characterization and the reduction of prediction uncertainties. They will account for both epistemic and aleatory aspects of uncertainty , related to the lack of knowledge and to errors respectively. In particular, it will provide new methods to explore conceptual uncertainty for a better characterization and to quantify the value of (additional) information to reduce efficiently uncertainty.