Sound Velocity Estimation of Seabed Sediment Based on Parametric Array Sonar

Ma, Jingxin; Li, Haisen; Jian-jun, Zhu; Chen, Baowei

doi:10.1155/2020/9810215

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…At this stage, the seabed detection is mainly based on underwater acoustic technology [8][9][10]. However, no sonar equipment can simultaneously obtain the above three types of feature information, and traditionally, the measurement of the different seabed feature information requires different acoustic detection equipment [11][12][13]. For example, multibeam sounding sonar (MBSS) and side-scan sonar (SSS) can realize the detection of seabed topography and sediment geomorphology, respectively, while sedimentary profile information is generally achieved by sub-bottom profiler (SBP) [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Design and Experiments of a Portable Seabed Integrated Detection Sonar

Jian-jun

et al. 2021

Sensors

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The integrated observation of seabed topography, sediment geomorphology and sub-bottom profile information is very important for seabed remote sensing and mapping. To improve the efficiency of seabed detection and meet the needs of portable development of detection equipment, we developed a portable seabed feature integrated detection sonar (PSIDS) with whcih a single sonar device can simultaneously detect the above three types of seabed information. The underwater transducer is mainly composed of the following three components: a parametric emission array as the sound source, a high frequency receiving linear array for multibeam echo signal collection, and a two-dimensional vector hydrophone for receiving the low-frequency sediment echo signal. Field experiments were conducted to validate the performance of the PSIDS on 11–17 January 2018 in Jiaozhou Bay, China. (1) PSIDS could perform the functions of both multibeam sonar and sub-bottom profiler; (2) The synchronously and integrated measurement of various seabed information was achieved by alternately emitting multibeam echo-sounding and sub-bottom profiling signal using parametric source. The detection results proved the feasibility and practicability of PSIDS to achieve multiple seafloor characteristics. PSIDS provides a new idea for developing integrated seabed detection sonar. In terms of convenience and data fusion, it is a good option to use this equipment for integrated seabed detection.

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Section: Introductionmentioning

confidence: 99%

Design and Experiments of a Portable Seabed Integrated Detection Sonar

Jian-jun

et al. 2021

Sensors

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Signal Processing Algorithm and System Realization of Broadband Parametric Array Sub-Bottom Profiler

Zhou

Huang

2022

IEEE Sensors J.

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First Demonstration of Recognition of Manganese Crust by Deep-Learning Networks with a Parametric Acoustic Probe

et al. 2022

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The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strategic submarine mineral resources, lack effective and low-cost detection devices for surveying since the challenging distribution requires a high vertical and horizontal resolution. To solve this problem, we have built an engineering prototype parametric acoustic probe named PPPAAP19. With the echo data acquired by the probe, the interpretation of the accurate thickness information and the seabed classification using the deep learning network-based method are realized. We introduce the acoustic dataset of the minerals collected from two sea trials. Firstly, the preprocessing method and data augment strategy used to form the dataset are described. Afterward, the performances of several baseline approaches are assessed on the dataset, and the experimental results show that they all achieve high accuracy for binary classification. We find that the end-to-end approach for binary classification based on a 1D Convolution Neural Network has a comprehensive advantage. Such a demonstration validates the possibility of binary classification for recognizing the ferromanganese crust only in an acoustic manner, which may significantly contribute to the efficiency of the survey.

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Sound Velocity Estimation of Seabed Sediment Based on Parametric Array Sonar

Cited by 3 publications

References 19 publications

Design and Experiments of a Portable Seabed Integrated Detection Sonar

Design and Experiments of a Portable Seabed Integrated Detection Sonar

Signal Processing Algorithm and System Realization of Broadband Parametric Array Sub-Bottom Profiler

First Demonstration of Recognition of Manganese Crust by Deep-Learning Networks with a Parametric Acoustic Probe

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