Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-6987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Nov 2023
Development and technical paper |
| 29 Nov 2023
| 29 Nov 2023
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Michael Hillier
CORRESPONDING AUTHOR
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Florian Wellmann
Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
Eric A. de Kemp
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Boyan Brodaric
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Ernst Schetselaar
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Karine Bédard
Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, Quebec City, QC G1K 9A9, Canada
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Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Neural networks can be used effectively to model three-dimensional geological structures from...
