Bayesian Optimisation for Antarctic Survey Planning

Remoteness, harsh environmental conditions, short field seasons, and high operational costs severely constrain the ability to collect observations of the polar cryosphere at scale. These limitations make efficient survey planning an important methodological need: data acquisition strategies must prioritise measurement locations that simultaneously (i) reduce model uncertainty and (ii) maximise scientific utility, for example by tightening constraints on projected ice sheet contributions to sea level rise. We address this need with Bayesian optimisation (BO), a probabilistic machine learning framework for black-box optimisation that uses a Gaussian process surrogate to model the target geospatial field and an acquisition function to formalise the trade-off between uncertainty reduction and scientific utility when proposing subsequent measurement locations. To showcase the approach, we consider a case study on planning airborne geophysical surveys of Antarctic ice thickness and bed topography, for which we introduce a set of novel acquisition functions tailored to Antarctic ice dynamics that translate cryospheric objectives into the BO framework:

• The FluxUCB (Flux Upper Confidence Bound) acquisition function incorporates satellite-derived ice velocity observations to prioritise sampling uncertain, potentially high-flux regions under the current posterior, since such regions can exert a disproportionate influence on ice discharge.
• Alternatively, PBBS (Probability of Bed Below Sea level) prioritises locations with a high posterior probability of marine-based grounding, thereby focusing effort on areas most relevant to assessing marine ice sheet instability (MISI).

In simulation, these objectives reduce posterior uncertainty per flight hour more efficiently than baseline strategies and more consistently target scientifically consequential regions. Together, these acquisition functions illustrate how BO can translate scientific priorities into an uncertainty-aware decision framework for data-efficient polar observation campaigns. More broadly, the framework has strong potential to extract greater value from limited polar field resources beyond airborne surveys, from optimising seismic survey design to informing ice core drilling site selection.