Efficient Detection of Underwater Natural Gas Pipeline Leak Based on Synthetic Aperture Sonar (SAS) Systems

Natural gas is an important source of energy. Underwater gas pipeline leaks, on the other hand, have a serious impact on the marine environment; hence, the need for a reliable and preferably automated inspection method is essential. Due to the high impedance difference and strong scattering properties of gas bubbles in the marine environment, sonar systems are recognized as excellent tools for leak detection. In this paper, a new method for gas leak detection is proposed based on gas bubble acoustic scattering modeling using Synthetic Aperture Sonar (SAS) technology, in which a coherent combination of gas bubble and pipeline scattering fields at different angles along synthetic apertures is used for leak detection. The proposed method can distinguish leak signals from the background noise using coherent processing in SAS range migration. SAS as an active sonar can collect accurate information at wide area coverage rate, independent of operating range and frequency, which can potentially reduce the time and cost of pipeline inspection. The simulation and comparison results of the proposed method based on coherent processing of synthetic aperture technology and the real aperture system show that the proposed method can effectively distinguish gas bubble signals at different ranges even in a single pass and improves pipeline leak detection operations.

Compressive Underwater Sonar Imaging with Synthetic Aperture Processing

Synthetic aperture sonar (SAS) is a technique that acquires an underwater image by synthesizing the signal received by the sonar as it moves. By forming a synthetic aperture, the sonar overcomes physical limitations and shows superior resolution when compared with use of a side-scan sonar, which is another technique for obtaining underwater images. Conventional SAS algorithms require a high concentration of sampling in the time and space domains according to Nyquist theory. Because conventional SAS algorithms go through matched filtering, side lobes are generated, resulting in deterioration of imaging performance. To overcome the shortcomings of conventional SAS algorithms, such as the low imaging performance and the requirement for high-level sampling, this paper proposes SAS algorithms applying compressive sensing (CS). SAS imaging algorithms applying CS were formulated for a single sensor and uniform line array and were verified through simulation and experimental data. The simulation showed better resolution than the ω-k algorithms, one of the representative conventional SAS algorithms, with minimal performance degradation by side lobes. The experimental data confirmed that the proposed method is superior and robust with respect to sensor loss.

The Binaural Illusion of Wallach (1940) Apparent in Synthetic Aperture Images of the Field of Audition Generated as the Head Turns

Wallach (J. Exp. Psychol. 1940, 27, 339–368) predicted that a human subject rotating about a vertical axis through the auditory centre, having an acoustic source rotating around the same axis at twice the rotation rate of the human subject, would perceive the acoustic source to be stationary. His prediction, which he confirmed by experiment, was made to test the hypothesis that humans integrate head movement information derived from the vestibular system and visual cues, with measurements of arrival time differences between the acoustic signals received at the ears, to determine directions to acoustic sources. The simulation experiments described here demonstrate that a synthetic aperture calculation performed as the head turns, to determine the direction to an acoustic source (Tamsett, Robotics 2017, 6, 10), is also subject to the Wallach illusion. This constitutes evidence that human audition deploys a synthetic aperture process in which a virtual image of the field of audition is populated as the head turns, and from which directions to acoustic sources are inferred. The process is akin to those in synthetic aperture sonar/radar technologies and to migration in seismic profiler image processing. It could be implemented in a binaural robot localizing acoustic sources from arrival time differences in emulation of an aspect of human audition.