Abstract
In recent years, most multibeam echo sounders (MBESs) have been able to collect water column image (WCI) data while performing seabed topography measurements, providing effective data sources for gas-leakage detection. However, there can be systematic (e.g., sidelobe interference) or natural disturbances in the images, which may introduce challenges for automatic detection of gas leaks. In this paper, we design two data-processing schemes to estimate motion velocities based on the Farneback optical flow principle according to types of WCIs, including time-angle and depth-across track images. Moreover, by combining the estimated motion velocities with the amplitudes of the image pixels, several decision thresholds are used to eliminate interferences, such as the seabed, non-gas backscatters in the water column, etc. To verify the effectiveness of the proposed method, we simulated the scenarios of pipeline leakage in a pool and the Songhua Lake, Jilin Province, China, and used a HT300 PA MBES (it was developed by Harbin Engineering University and its operating frequency is 300 kHz) to collect acoustic data in static and dynamic conditions. The results show that the proposed method can automatically detect underwater leaking gases, and both data-processing schemes have similar detection performance.
Highlights
Multibeam echo sounders (MBESs) are important remote-sensing acoustical systems whose primary goal is mapping the seabed
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A scene of leaking gases from a pipeline was simulated in a pool, and the acoustic data was collected by an HT300 PA MBES developed by Harbin Engineering University, China
Summary
Multibeam echo sounders (MBESs) are important remote-sensing acoustical systems whose primary goal is mapping the seabed. They are widely used to detect targets in water columns [1]. Many types of MBESs can collect water column image (WCI) data, which carry backscattering signals of scatters from the transducer to the seabed. The ascending gases and other scatterers may be deflected by water/sea currents [4] These may be the result of leaks from man-made structures, such as underwater gas pipelines, or gases that were released from the seabed. It is necessary to study methods automatically detecting gas emissions and improving data processing efficiency [19]
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