CoFI - The Common Framework for Inference: A software platform for experimentation, education and application of geophysical inversion tools

Inference problems within the geosciences vary considerably in terms of size and scope, ranging from the detection of changepoints in 1D time/depth models, to the construction of complex 3D or 4D models of the Earth. Solving an inverse problem typically requires fusing various classes of data, each associated with its own forward model. The choice of an appropriate inference method is itself not obvious. An investment of much time effort and is required in software development and education. Many researchers have developed bespoke inversion and parameter estimation algorithms tailored to their specific needs. Associated software is then typically bespoke to the particular application, often requiring significant investment by new researchers to master with minimal documentation. This is entirely understandable as generalisation and ongoing support of inference codes requires significant time and effort that is frequently beyond the primary objectives of the research. As a result the all important experimentation often required to choose an appropriate inversion method for a new data set or domain, is often not practical. Furthermore design choices made in existing software implementations often dictate those by subsequent researchers and influence the scientific direction taken.

 

The Common Framework for Inference, CoFI, is an open source project which aims to capture inherent commonalities present in all types of inverse problems, independent of the specific methods employed to solve them. CoFl is codifies the definition of an inference problem and then provides an interface to reliable and sophisticated third-party packages, such as SciPy and PyTorch, to tackle inverse problems across a broad range. The modular and object-oriented design of CoFI, supplemented by a 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 and promote best practice but also to support education and STEM training. This poster gives an overview of CoFl through domain relevant examples, from optimisation to probabilistic sampling.  With a focus on CoFI’s modular approach we hope to foster collaboration centred around interaction by expanding the set of inference algorithms and domain-relevant examples.