Speaker
Description
[Shortened! Full abstract as pdf]
Shipboard ADCPs provide continuous upper-ocean velocity profiles and are indispensable for oceanographic field studies. In shelf regions and coastal waters, a particularly time-consuming part of post-processing is editing the velocity time series to remove contamination near the seafloor caused by sidelobe interference from the bottom echo. Existing automated approaches are often insufficiently robust under real-world conditions — particularly given acoustic interference from other simultaneously operated instruments — so reliable quality control still largely depends on manual inspection, limiting timely data provision as raw data volumes grow.
Within the DAM (German Marine Research Alliance) project "Marine Data – Research Vessels", we aim to develop a two-stage machine learning solution that automates bottom-signal editing using the same signals an experienced analyst relies on: beam-averaged echo intensity and along-track velocity. A convolutional neural network first locates the seafloor by jointly evaluating both signals across consecutive pings; a gradient-boosted classifier then flags velocity cells contaminated by bottom interference.
A key contribution of this project is the compilation of a large, high-quality reference dataset: more than 50 manually processed shipboard ADCP cruises, comprising around 4,500 raw data files with expert-derived bottom and contamination masks. Models are trained and evaluated at cruise level to avoid data leakage, and each prediction is accompanied by a confidence score so that ambiguous cases can be flagged for expert review rather than processed silently.
The resulting module will be integrated into the OSADCP toolbox — the standardized workflow developed within this DAM project for FAIR provision of ocean current data from the German research fleet — with the longer-term goal of enabling autonomous, near-real-time processing on board, as a service to the wider oceanographic community.