Seasonal comparison of underwater ambient noise observed in the deep area of the South China Sea

Yang, Qi; Yang, Kunde

doi:10.1016/j.apacoust.2020.107672

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…The distribution of low-Figure 12. Oceanic environmental noise spectrum distribution chart (modified from reference [2], the green curve represents the results of this study).…”

Section: Discussionmentioning

confidence: 99%

“…He proposed the Wenz spectrum to describe the spectrum of marine environmental noise [1]. According to the Wenz spectrum, noise sources can be roughly divided into three overlapping categories: ocean turbulence, distant ships, and wind-generated noise [2][3][4]. Marine environmental noise is considered a form of chronic and continuous pollution, especially in shallow-water environments, which are the focal point of most human activities.…”

Section: Introductionmentioning

confidence: 99%

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Underwater Noise Characteristics of the Tidal Inlet of Zhanjiang Bay

Huo,

Zhang,

Yuan

et al. 2023

Water

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The coupling mechanism between natural and anthropogenic noise in shallow marine areas is of great significance for maintaining the ecological safety of these regions. In this study, a section of Zhanjiang Bay’s entrance was selected as a typical research area, and environmental noise data at different depths were collected during the spring and autumn seasons. The spectral characteristics, sound pressure levels, and underwater noise frequency correlation matrices of environmental noise were analyzed to reveal the underwater noise characteristics of tidal channels in Zhanjiang Bay and their main influencing factors. The results show that underwater noise in this study area had a stable frequency band distribution. In the low-frequency range of 20–50 Hz, the main source of noise was the flow noise influenced by tides and topography, with a peak sound pressure level of approximately 97 dB. In the frequency range of 50 Hz to 500 Hz, the main noise sources were ships at sea, followed by wind-generated noise. At frequencies above 500 Hz, the noise intensity decreased. In addition, it was found that the sound pressure level in the low-frequency range had a significant correlation with the tidal level, increasing with the rise of the tide and decreasing during low tides. This study provides a research case on the impact that human noise activity has on environmental noise in shallow marine bays. These research findings can support the selection of sites and reduce construction noise from offshore wind farms, as well as ensure the acoustic ecological environment in the vicinity of marine ranches.

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“…The distribution of low-Figure 12. Oceanic environmental noise spectrum distribution chart (modified from reference [2], the green curve represents the results of this study).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater Noise Characteristics of the Tidal Inlet of Zhanjiang Bay

Huo,

Zhang,

Yuan

et al. 2023

Water

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“…The underwater channel's behavior is best described as a low-pass filter. It can be modeled as a white noise sequence filtered using a Butterworth IIR low-pass filter with 30dB attenuation in stopband and normalized stopband frequency of 0.05 hal f cyle/sample per sample ad 0.9 hal f cyle/sample Respectively [27]. Figure 2 presents the signal and noise in the time domain with SNR=45dB.…”

Section: Underwater Ambient Noisementioning

confidence: 99%

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Talebpour¹,

Mozaffari²,

Saif³

et al. 2023

Adv. sci. technol. eng. syst. j.

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Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a localization method for a noisy, shallow water environment. Our proposed algorithm employs modal selection to process the most noiseresistive dispersion curves, improving the accuracy and noise-resistivity of the existing methods. Moreover, we proposed a 2D localization method with multiple unsynchronized hydrophones and minimal hardware requirements and limitations. Furthermore, we analyzed the effects of underwater ambient noise on the accuracy of the proposed method, using simulated and real recorded explosion and whale sounds, and compared our algorithm's localization performance with others. Simulation results show increased localization accuracy of 30m for the recorded explosion sound and 360m for the Whale sound.

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