A machine learning approach for detecting biofouling in oceanographic data

Autonomous ocean observing platforms collect long-term biogeochemical time series, but sensor degradation from biofouling introduces progressive biases that contaminate the climate record. This work focuses on the BGC-Argo fleet of profiling floats, where optical sensors measuring chlorophyll-a and backscatter are particularly susceptible to biofouling. Current detection relies on per-float empirical exponential fits and threshold-based quality controls. This work presents a variational autoencoder (VAE) trained on depth-resolved profiles from 86 Mediterranean BGC-Argo floats to detect biofouling drift as an unsupervised anomaly. The VAE is trained exclusively on early-deployment (clean) profiles from all floats, then evaluated on the full temporal trajectory of each float. Reconstruction error increases over deployment time for 34 of 86 floats (40\%), with a mean Pearson correlation $\rho = 0.20$ and a mean late-to-early error ratio of 1.70. The detection signal is strongest in floats with multi-year deployments and surface-intensified CHLA, consistent with the known biofouling mechanism. To the authors' knowledge, this is the first large-scale ML benchmark for biofouling detection in autonomous ocean sensors, demonstrating that an unsupervised shape-based VAE can detect drift across a heterogeneous fleet. \end{abstract}