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Mapping the ice draft of Dotson Ice Shelf with a long-range ADCP on an autonomous underwater vehicle

Stina Wahlgren, Anna Wåhlin, Karen J. Heywood
July 16, 2026
Published Date

Research Abstract & Technology Focus

Abstract Autonomous underwater vehicle missions have expanded our knowledge about ice shelf cavities. In order to understand ice shelf-ocean interactions and their consequences for basal melt rates, we need detailed information about the ice base. Knowledge of the ice base topography also aids planning and minimizes risk for missions going near the ice. Here we demonstrate how long-range (> 1000 m) Acoustic Current Doppler Profilers (ADCP) can be used to get information about the ice base while traveling near the seabed, at several hundred meters from the ice. Data from a seabed-following vehicle moving 300 – 700 m below the Dotson Ice Shelf, Antarctica, are used to derive maps of ice draft by analyzing the echo intensity from each beam separately. Vertical resolution is increased using a three-point range-interpolation scheme of the return echo. The ADCP-derived ice draft shows good agreement with higher resolution (10 m) sonar multibeam maps collected on different missions during the same campaign. As the presented method only requires standard ADCP data, it can be applied to derive higher resolution ice base maps from existing datasets and from future missions using AUVs without multibeam.
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What is the core focus of the research titled 'Mapping the ice draft of Dotson Ice Shelf with a long-range ADCP on an autonomous underwater vehicle'?

This literature focuses on: Abstract Autonomous underwater vehicle missions have expanded our knowledge about ice shelf cavities. In order to understand ice shelf-ocean interactions and their consequences for basal melt rates, we need detailed information about the ice base....

What other academic literature is closely related to 'Mapping the ice draft of Dotson Ice Shelf with a long-range ADCP on an autonomous underwater vehicle'?

Yes, highly correlated activity was mapped. An entry titled 'Multi-Source Sensor Fusion Localization Method for Autonomous Underwater Vehicles Based on Deep Learning' discusses this: Autonomous Underwater Vehicles (AUVs) are increasingly used in deep-sea exploration, environmental monitoring, and marine engineering. Their operat...

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