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A critical decision process in geophysical data collection is how to efficiently combine a variety of sensor types and where to collect new observations, in particular if measurements are very costly or resources are limited. This may include drill-core placements or the layout of a geophysical survey. Bayesian Optimisation (BO) solves this decision making problem by finding global optimal solutions given multiple, often distinct measurements and model uncertainties. One of the major challenges for applying BO in geoscience still lies in constructing a 3D model from sparse, typically 2D sensor data that can be computationally efficiently evaluated and that transforms the combined data and uncertainties from multiple sensor measurements coherently into a posterior function approximating the true model; this is also known as an inversion problem.

GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences