Loop 3D implicit modelling project

Loop is a OneGeology initiative, initiated by Geoscience Australia and funded by Australian Territory, State and Federal Geological Surveys, the Australian Research Council and the MinEx CRC. The project is led by Monash University and involves research groups from the University of Western Australia, the RING consortium at the Universite de Lorraine, Nancy, France and RWTH Aachen in Germany. In-kind research is also provided by Natural Resources Canada (Geological Survey of Canada), Geoscience Australia and the British Geological Survey. Other partners include AuScope and the USGS.

3D Bayesian modelling of geological and geophysical data


In order to support a socially-licensed greener future, one of the biggest challenges of the next decade is to improve our ability to predict subsurface geology. As a society, we need to better understand and predict the location and quality of resources to mitigate the risk related to their exploitation. This includes discovery and management of (new) natural resources such as copper, critical metals and water, providing better information to decision and policy makers for urban and waste storage planning.

What we are doing

We are building a machine-supported system for rapid and adaptable resource management decision-making that incorporates risk-estimation and risk-reduction. This new type of geological model will support rapid and testable decision-making and must be:

In the last four years, we have empowered geologists to automatically build 3D models and tackle difficult yet interesting geological questions rather than the technical decisions as to which button to press next. The map2loop library provides an automated analysis of geological maps and extracts geometrical modelling parameters such as formations thicknesses, structural information (strike and dip of strata), topological information related to faults, faults and formations and the stratigraphy. This information is fed into the LoopStructural library to generate implicit formulation of a geological model.

The next iteration of the project will hopefully focus on framing model building as a Bayesian inference throughout the entire workflow including for input data and knowledge estimation and structural modelling. This novel approach allows us to investigate the parameter space and estimates of conceptual uncertainties. For example, one of the main limitations of the map2loop process is that structural information related to dip, shape, orientation and amplitude of offset of faults are usually not provided. Similarly, structural information is usually only provided as strike/dip of bedding while multiple deformation events are poorly documented and supporting data are lacking. We propose to estimate these parameters using a Bayesian inference and feed the posteriori distribution to the modelling engine.

The latter is subject to an application for funding to the Australian Research Council with an announcement expected at the end of August 2022.

Further information about the Loop project (including recent publications and links to the open-source libraries).


Dr Laurent Ailleres (laurent.ailleres@monash.edu).