Automatic detection of marine gas seeps using an interferometric sidescan sonar

Blomberg, Ann Elisabeth Albright
Sæbø, Torstein Olsmo
Hansen, Roy Edgar
Pedersen, Rolf B.
Austeng, Andreas
Date Issued
Blomberg, Ann Elisabeth Albright; Sæbø, Torstein Olsmo; Hansen, Roy Edgar; Pedersen, Rolf B.; Austeng, Andreas. Automatic detection of marine gas seeps using an interferometric sidescan sonar. IEEE Journal of Oceanic Engineering 2016 ;Volum PP.(99)
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There is a significant need for reliable, cost-effective, and preferably automatic methods for detecting and monitoring marine gas seeps. Seeps at the seafloor may originate from natural sources including sediments releasing biogenic methane and volcanoes releasing CO 2 , or from man-made constructions such as pipelines or well heads, and potentially also from subseafloor CO 2 storage sites. Improved seep detection makes it possible to estimate the amount of greenhouse gases entering the oceans, and to promptly detect and address potential leaks to reduce environmental and economical consequences. Sonar is an excellent tool for seep detection due to the strong acoustic backscatter properties of gas-filled bubbles in water. Existing methods for acoustic seep detection include multibeam and sidescan surveying, as well as active and passive sensors mounted on a stationary platform. In this work, we develop a new method for automatic seep detection using an interferometric sidescan sonar. We apply signal processing techniques combined with knowledge about acoustical and spatial properties of seeps for improved detectability. The proposed method fills an important gap in existing technology—the ability to automatically detect a seep during a single pass with an autonomous underwater vehicle (AUV) equipped with an interferometric sidescan sonar. Results from simulations as well as field data from two leaking abandoned wells in the North Sea indicate that small seeps are consistently detected on a sandy seafloor even when the observation time is limited (a single pass with the AUV). We explore the detection capability for different seafloor types ranging from silt to gravel.
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