Curlew 1.0: Spatio-temporal implicit geological modelling with neural fields in python

Akshay Vijay Kamath , Samuel T. Thiele, Marie Moulard, Lachlan Grose, Raimon Tolosana-Delgado, Michael Hillier, Florian Wellmann , Richard Gloaguen

We present curlew, an open-source python package for structural geological modelling using neural fields. This modelling framework incorporates various local constraints (value, gradient, orientation and (in)equalities) and tailored global loss functions to ensure data-consistent and geologically realistic predictions. Random Fourier Feature (RFF) encodings are used to improve model convergence and facilitate stochastic uncertainty quantification, while simultaneously improving the model’s ability to learn naturally periodic features such as folds. These advances are integrated into a software framework that allows incremental construction of complex geological models through temporally-linked neural fields, each representing a specific deposition, intrusion or faulting event. Significantly, this framework allows semi-supervised learning to integrate diverse unlabelled datasets (e.g., geochemistry, petrophysics), reducing interpretation bias and potentially improving model robustness. We describe and demonstrate these various capabilities using synthetic examples and real data from a faulted stratigraphic digital outcrop model from Newcastle, Australia.