Ice Core Dating using Probabilistic Programming
This work addresses the time-consuming and uncertain manual dating process for ice core data, which is crucial for climate science, but it is incremental as it applies existing probabilistic methods to a specific domain.
The paper tackled the problem of dating ice cores by automating the inference of chronology from noisy seasonal patterns, using probabilistic programming to prototype models and demonstrate common failure modes.
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in the ice display quasi-periodic cycles that delineate annual layers. Manually counting these noisy seasonal patterns to infer the chronology can be an imperfect and time-consuming process, and does not capture uncertainty in a principled fashion. In addition, several ice cores may be collected from a region, introducing an aspect of spatial correlation between them. We present an exploration of the use of probabilistic models for automatic dating of ice cores, using probabilistic programming to showcase its use for prototyping, automatic inference and maintainability, and demonstrate common failure modes of these tools.