Scientific drilling has produced large archives of physical, chemical and magnetic measurements linked to descriptions of the rock recovered. These archives are mostly incomplete: instruments sample at different intervals, recovery is partial, and destructive analyses preclude later measurement of the same material. We train an self-supervised embedding model on the LILY ocean drilling database using six routinely logged properties (bulk density, magnetic susceptibility, natural gamma radiation, and red, green and blue reflectance) across 236,963 observations from 296 boreholes, with a masked variational autoencoder that has a heteroscedastic Gaussian decoder and learnable mask tokens. The embeddings separate geologist-assigned lithologies as well as a variant trained with those labels, and increasing the weight on the labels degrades reconstruction while leaving classification unchanged. The labels are recoverable from the measurements geologists use to assign them, so training on them adds nothing. Lithological categories compress continuous measurements into classes, and compressing them discards what the measurements record about the processes that produced the rock. The embeddings also predict physical properties that were absent from training, on boreholes the model has never seen. While depth should account for this since sediments compact with burial, it does not. On held-out boreholes depth alone reaches a median $R^2$ of -0.02, against 0.20 for the embedding.
Joseph O'Leary, Tapio Simula, Tom Kimpson, Corey Davey, Wenchao Li, Andrew D. Greentree, Allison Kealy
Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric magnetic anomaly map. Existing map-matching algorithms either assume a Gaussian posterior (extended Kalman filter), require Monte Carlo sampling (particle filter), or discard posterior uncertainty in favour of a single optimal path (Viterbi algorithm). Here we formulate map matching as inference on a chain-structured factor graph and solve it segment by segment using the forward-backward algorithm, which yields a discrete posterior over candidate positions and its Shannon entropy $H_t$ at time $t$. Over the \texttt{Renfrew\_395} map, $10^3$ synthetic trials yield a median position error of $32.7$ m, $91\%$ below the median inertial drift of $362$ m. On these trials and $10^3$ flight paths, the median error is $43\%$ below the extended Kalman filter and particle filter. Across these paths, a threshold on segment-level entropy detects $47\%$ of segments with errors above two map cells and $94\%$ of those above three, at a $10\%$ false-alarm rate. The foregoing results indicate that $H_t$ provides a truth-free diagnostic of map-conditioned navigation ambiguity.
Floods are the most common form of natural disaster and accurate flood forecasting is essential for early warning systems. Previous work has shown that machine learning (ML) models are a promising way to improve flood predictions when trained on large, geographically-diverse datasets. This requirement of global training can result in a loss of ownership for national forecasters who cannot easily adapt the models to improve performance in their region, preventing ML models from being operationally deployed. Furthermore, traditional hydrology research with physics-based models suggests that local data -- which in many cases is only accessible to local agencies -- is valuable for improving model performance. To address these concerns, we demonstrate a methodology of pre-training a model on a large, global dataset and then fine-tuning that model on data from individual basins. This results in performance increases, validating our hypothesis that there is extra information to be captured in local data. In particular, we show that performance increases are most significant in watersheds that underperform during global training. We provide a roadmap for national forecasters who wish to take ownership of global models using their own data, aiming to lower the barrier to operational deployment of ML-based hydrological forecast systems.