CoFI - Linking geoscience inference problems with tools for their solution

Inference problems within the geosciences vary significantly in size and scope, ranging from the detection of data trends through simple linear regressions, to the construction of complex 3D models representing the Earth’s interior structure. Successfully solving an inverse problem typically requires combining various types of data sets, each associated with its own forward solver. In the absence of established software, many researchers and practitioners resort to developing bespoke inversion and parameter estimation algorithms tailored to their specific needs. However, this practice does not promote reproducibility and necessitates a substantial amount of work that is frequently beyond the primary objectives of the research.

Our aim with CoFI (pronounced: coffee), the Common Framework for Inference, is to capture inherent commonalities present in all types of inverse problems, independent of the specific methods employed to solve them. CoFI is an open-source Python package that provides a link to reliable and sophisticated third-party packages, such as SciPy and PyTorch, to tackle inverse problems of a broad range. The modular and object-oriented design of CoFI, supplemented by our comprehensive suite of tutorials and practical examples, ensures its accessibility to users of all skill levels, from experts to novices. This not only has the potential to streamline research but also to support education and STEM training.

This poster presentation aims to give an overview of CoFI’s main features and usage through practical examples. Moreover, we hope to foster collaboration and invite contributions on inference algorithms and domain-relevant examples.