The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of the influence of ship traffic and marine weather information on underwater ambient noise using public data

공공데이터를 활용한 선박 통행량 및 해양기상정보의 수중 주변소음에 대한 영향성 분석

  • 김용국 (LIG넥스원 해양연구소) ;
  • 국영민 (LIG넥스원 해양연구소) ;
  • 김동관 (LIG넥스원 해양연구소) ;
  • 김규철 (LIG넥스원 해양연구소) ;
  • 윤상기 (LIG넥스원 해양연구소) ;
  • 최창호 (LIG넥스원 해양연구소) ;
  • 김홍국 (광주과학기술원 전기전자컴퓨터공학부)
  • Received : 2020.09.15
  • Accepted : 2020.11.09
  • Published : 2020.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we analyze the influences of ship traffic and marine weather information on underwater ambient noise. Ambient noise is an important environmental factor that greatly affects the detection performance of underwater sonar systems. In order to implement an automated system such as prediction of detection performance using artificial intelligence technology, which has been recently studied, it is necessary to obtain and analyze major data related to these. The main sources of ambient noise have various causes. In the case of sonar systems operating in offshore seas, the detection performance is greatly affected by the noise caused by ship traffic and marine weather. Therefore, in this paper, the impact of each data was analyzed using the measurement results of ambient noise obtained in coastal area of the East Sea of Korea, and public data of nearby ship traffic and ocean weather information. As a result, it was observed that the underwater ambient noise was highly correlated with the change of the ship's traffic volume, and that marine environment factors such as wind speed, wave height, and rainfall had an effect on a specific frequency band.

본 논문에서는 수중 주변소음 생성에 주요한 영향을 끼치는 선박 통행량 및 해양기상정보와 수중 주변소음간 영향성을 분석한다. 주변소음은 수중 소나 시스템의 탐지 성능에 큰 영향을 끼치는 중요한 환경 요소이다. 최근에 많은 연구가 진행 중인 인공지능 기술을 이용한 탐지성능 예측 등 자동화 시스템 구현을 위하여 이와 관련된 주요 데이터 확보 및 분석이 필요하다. 주변소음의 주요 발생원은 다양한 원인이 있는데, 연근해에서 운용되는 소나 시스템의 경우 탐지 성능에 있어서 선박 통행에 의한 소음 및 해양 기상에 의해 발생하는 소음의 영향을 크게 받는다. 따라서 본 논문에서는 대한민국 동해 연안에서 획득한 주변소음 측정 결과와 인근 선박 통행량 및 해양기상정보 공공데이터를 이용하여 각 데이터의 영향성을 분석하였다. 분석 결과 수중 주변 소음은 선박의 통행량의 변화에 따라 높은 연관성을 보였으며, 풍속과 파고, 강우 등 해양환경 요소에 있어서도 특정 주파수 대역에 영향성이 있음을 관찰하였다.

Keywords

References

  1. J. S. Kim, S. B. Hwang, and C. M. Lee, "A DEMON processing robust to interference of tonals" (in Korean), J. Acoust. Soc. Kr. 31, 384-390 (2012). https://doi.org/10.7776/ASK.2012.31.6.384
  2. A. D. Waite, Sonar for Practising Engineers, 3rd Ed. (Wiley, UK, 2002), pp. xvii.
  3. R. J. Urick, Principles of Underwater Sound, 3rd Ed. (Peninsula Publishing, CT, 2010), Chap. 7.
  4. R. P. Hodges, Underwater Acoustics: Analysis, Design and Performance of Sonar (Wiley, UK, 2010), Chap. 7.
  5. B. K. Choi, B.-C. Kim, C. Kim, and B.-N. Kim, "Analysis of dependence on wind speed and ship traffic of underwater ambient noise at shallow sea surrounding the korean peninsula," (in Korean), J. Acoust. Soc. Kr. 22, 233-241 (2003).
  6. B.-C. Kim, B.-N. Kim, J. Hahn, and B. K. Choi, "Measurements of underwater ambient noise generated by breaking waves in surf zone," Proc. Symposium on Ultrasonic Electronics, 391-392 (2009).
  7. Y. Guo, R. Ai, Y, Chen, and Y. Qi, "Prediction of passive sonar detection range in different detection probability," Proc. ICSAI. 1289-1293 (2018).
  8. G. Hu, K. Wang, Y. Peng, M. Qiu, J. Shi, and L. Liu, "Deep learning methods for underwater target feature extraction and recognition," Computational Intelligence and Neuroscience 2018, 1-10 (2018).
  9. J. Choi, Y. Choo, and K. Lee, "Acoustic classification of surface and underwater vessels in the ocean using supervised machine learning," Sensors 2019, 1-15 (2019).
  10. Port Management Information System (PORT- MIS), https://new.portmis.go.kr/, (Last viewed November 11, 2020).
  11. Weather data opening portal, https://data.kma.go.kr/, (Last viewed November 11, 2020).
  12. H. U. Sverdrup and W. H. Munk, Wind, Sea And Swell: Theory of Relations for Forecasting (U.S. Navy Hydrographic Office, Washington, 1947), pp. 26.
  13. National Weather Service, https://www.weather.gov/key/marine_sigwave/, (Last viewed November 11, 2020).