EGU21-12978, updated on 05 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Graph Neural Networks for 3-D Structural Geological Modelling

Michael Hillier1,2, Florian Wellmann1, Boyan Brodaric2, Eric de Kemp2, and Ernst Schetselaar2
Michael Hillier et al.
  • 1RWTH Aachen University, Advanced Study in Computational Engineering Science (AICES), Aachen, Germany (
  • 2Geological Survey of Canada, Ottawa, Canada

A new approach for constrained 3-D structural geological modelling using Graph Neural Networks (GNN) has been developed that is driven by a learning through training paradigm. Graph neural networks are an emerging deep learning model for graph structured data which can produce vector embeddings of graph elements including nodes, edges, and graphs themselves, useful for various learning objectives. In this work our graphs represent unstructured volumetric meshes. Our developed GNN architecture can generate spatially interpolated implicit scalar fields and discrete geological unit predictions on graph nodes (e.g. mesh vertices) to construct 3-D structural models. Interpolations are constrained by scattered point data sampling geological units, interfaces, as well as linear and planar orientation measurements. Interpolation constraints are incorporated into the neural architecture using loss functions associated with each constraint type that measure the error between the network’s predictions and data observations. This presentation will describe key concepts involved within this approach including vector embeddings, spatial-based convolutions on graphs, and loss functions for structural geological features. In addition, several modelling results will be given that demonstrate the capabilities and potential of GNNs for representing geological structures.

How to cite: Hillier, M., Wellmann, F., Brodaric, B., de Kemp, E., and Schetselaar, E.: Using Graph Neural Networks for 3-D Structural Geological Modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12978,, 2021.

Display materials

Display file

Comments on the display material

to access the discussion