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Study on a Detection Technique for Scholte Waves at the Seafloor.
Liang, Minshuai; Wang, Liang; Yu, Gaokun; Ren, Yun; Peng, Linhui.
Affiliation
  • Liang M; Department of Marine Technology, Ocean University of China, Qingdao 266100, China.
  • Wang L; Department of Marine Technology, Ocean University of China, Qingdao 266100, China.
  • Yu G; Department of Marine Technology, Ocean University of China, Qingdao 266100, China.
  • Ren Y; State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
  • Peng L; Department of Marine Technology, Ocean University of China, Qingdao 266100, China.
Sensors (Basel) ; 22(14)2022 Jul 18.
Article in En | MEDLINE | ID: mdl-35891023
Scholte waves at the seafloor have significant potential for underwater detection and communication, so a study about detecting Scholte waves is very meaningful in practice. In this paper, the detection of Scholte waves at the seafloor is researched theoretically and experimentally. Acoustic models with the multilayer elastic bottom are established according to the ocean environment, and a tank experiment is designed and carried out to detect Scholte waves. Different from detecting Scholte waves in the seismic wavefield, a technique for detecting Scholte waves in the sound pressure field is proposed in this paper. The experimental results show that the proposed technique can detect Scholte waves effectively, and there are no problems such as seabed coupling and the effect of wave speeds. Furthermore, the results also show that this detection technique is still effective in conditions with a sediment layer. The existence of sediment layers changes the acoustic field conditions and affects the excitation of Scholte waves.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sound / Acoustics Type of study: Diagnostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: China Country of publication: Suiza

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sound / Acoustics Type of study: Diagnostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: China Country of publication: Suiza