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.
Acoustic scattering as the perturbation of an incident acoustic field from an arbitrary object is a critical part of the target-recognition process in synthetic aperture sonar (SAS) systems. The complexity of scattering models strongly depends on the size and structure of the scattered surface. In accurate scattering models including numerical models, the computational cost significantly increases with the object complexity. In this paper, an efficient model is proposed to calculate the acoustic scattering from underwater objects with less computational cost and time compared with numerical models, especially in 3D space. The proposed model, called texture element method (TEM), uses statistical and structural information of the target surface texture by employing non-uniform elements described with local binary pattern (LBP) descriptors by solving the Helmholtz integral equation. The proposed model is compared with two other well-known models, one numerical and other analytical, and the results show excellent agreement between them while the proposed model requires fewer elements. This demonstrates the ability of the proposed model to work with arbitrary targets in different SAS systems with better computational time and cost, enabling the proposed model to be applied in real environment.
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