Fault representation in structural modelling with implicit neural representations

Implicit neural representations have been demonstrated to provide a flexible and scalable framework for computer graphics and three-dimensional modelling and, consequently, have found their way also into geological modelling. These networks are feature-based and resolution-independent, making them effective for modelling geological structures from scattered interface points, units, and structural orientations. Despite the promising characteristics of existing implicit neural representation approaches, modelling faults within implicit neural representations remains a significant challenge. In this work, we present a fault feature encoding approach to represent faults in implicit neural representations, where the discontinuous information is concatenated as additional features of observation points and query points for network input. We apply this methodology first to a synthetic model to evaluate its efficacy, and subsequently to a real-world dataset from a part of the Gullfaks field in the northern North Sea. The modelling results demonstrate the method’s capacity to generate a well-defined implicit scalar field while preserving sharp transitions at fault locations. Moreover, this work mentions the advantages of the presented approach over using Boolean operations and discontinuous activation functions. Furthermore, we discuss the potential opportunity to integrate prior domain knowledge and geophysics datasets into structural modelling by embedding them as model input features or incorporating them as constraints by loss functions. • Implement fault modelling in a neural network-based structural modelling framework. • Conduct a comparative study with conventional fault modelling approaches. • Propose a framework for integrating domain knowledge into structural modelling. • Discuss methods to incorporate geophysical data into structural modelling.