PySubdiv 1.0: open-source geological modeling and reconstruction bynon-manifold subdivision surfaces

Sealed geological models are commonly used as an input to processsimulations, for example, in hydrogeological or geomechanical studies.Creating these meshes often requires tedious manual work, and it istherefore difficult to adjust a once-created model. In this work, wepropose a flexible framework to create and interact with geological modelsusing explicit surface representations. The essence of the work lies in thedetermination of the control mesh and the definition of semi-sharp-creasevalues, which, in combination, enable the representation of complexstructural settings with a low number of control points. We achieve thisflexibility through the adaptation of recent algorithms from the field ofcomputer graphics to the specific requirements of geological modeling,specifically the representation of non-manifold topologies and sharpfeatures. We combine the method with a particle swarm optimization (PSO)approach to enable the automatic optimization of vertex position and creasesharpness values. The result of this work is implemented in an open-sourcesoftware (PySubdiv) for reconstructing geological structures while resultingin a model which is (1) sealed/watertight, (2) controllable with a controlmesh and (3) topologically similar to the input geological structure. Also,the reconstructed model may include a lower number of vertices compared tothe input geological structure, which results in reducing the cost ofmodeling and simulation. In addition to enabling a manual adjustment ofsealed geological models, the algorithm also provides a method for theintegration of explicit surface representations in inverse frameworks andthe consideration of uncertainties.

Automated summary

This work proposes a flexible framework for creating and interacting with geological models using explicit surface representations. The control mesh and semi-sharp-crease values are determined to enable the representation of complex structural settings with a low number of control points. The method is combined with a particle swarm optimization approach for automatic optimization of vertex position and crease sharpness values.