Statistical analysis and modeling of underwater wind noise at the northeast pacific continental margin

Schwock, Felix; Abadi, Shima

doi:10.1121/10.0007463

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…Long-term statistics of ocean ambient noise have been investigated at plenty of locations in the global ocean, ranging from tropical/subtropical Pacific region (Farrokhrooz et al, 2017;Niu et al, 2021;Yang et al, 2023) and South China Sea (Da et al, 2014;Jiang et al, 2017;Shi et al, 2019), and temperate North Pacific region (McDonald et al, 2006;Seger et al, 2015;Schwock and Abadi, 2021), to polar region (Chen and Schmidt, 2017;Bonnel et al, 2021;Mo et al, 2023). The motivation of these previous works is to acquire the diel, monthly or seasonal variations of ambient noise in different areas, and describe the relationship between noise and meteorological data at the recording locations.…”

Section: Introductionmentioning

confidence: 99%

Long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea

Guo,

Liu,

et al. 2024

Front. Mar. Sci.

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Research on ocean ambient noise is highly important for environment monitoring, marine mammal protection, underwater communication and navigation. In this paper, we present the long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea, at a depth of 1240 m. The data were collected from 11th July 2022 to 31st December 2022 together with local wind speeds ranging from 1 to 58 knots (two typhoons involved), and the processing frequency band is between 20 and 2000 Hz. The long-term mean noise level is calculated along with its skewness, kurtosis and percentile distributions. Diurnal and monthly average of noise levels are analyzed, and the large fluctuations in lower (≤100 Hz) and higher (≥400 Hz) frequencies are respectively caused by the variation of the number of nearby and distant ships and the diverse distributions of the windspeeds in individual months. We find that the noise level in winter (Dec.) is 10~11 dB higher than that in summer (Jul.) at higher frequencies. The probability densities of noise levels in the situation of a fixed wind speed are likely to obey the Burr distributions in low frequencies (50 and 100 Hz) and the Weibull distributions in high frequencies (400 and 1000 Hz). In addition, the mean noise levels for different Beaufort scales match well with the 5-dB-addtion Wenz curves, and a mathematic relationship is acquired between the noise level and wind speed in the experimental site. The results are of great representativeness, and are significant to data-driven noise modelling, evaluation and improvement of sonar performance in the region of South China Sea with an incomplete deep-water sound channel.

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Section: Introductionmentioning

confidence: 99%

Long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea

Guo,

Liu,

et al. 2024

Front. Mar. Sci.

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“…Its sources are diverse, including the movement of seawater, the impact of wind and atmosphere on sea surface noise, changes in water layers due to movement or melting, alterations in submarine geological structures, sounds produced by marine organisms, and noise generated by man-made sources [1][2][3][4][5]. There are differences in marine environmental noise under different time and space conditions [6][7][8][9][10], which makes it extremely difficult to predict marine environmental noise [11,12]. Based on a large amount of data from environmental noise surveys, previous studies have derived empirical prediction formulas and conclusions that are applicable within a specific range [13,14].…”

Section: Introductionmentioning

confidence: 99%

Depth Spatial Characterization of Marine Environmental Noise in the Zengmu Basin

Cui,

Cang,

et al. 2023

JMSE

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Based on the measured data obtained from an array of optical fiber hydrophones, this paper analyzes and interprets the depth spatial spectrum characteristics, correlation characteristics, and vertical directionality of marine environmental noise in the Zengmu Basin. The analysis is conducted within the frequency range of 20 Hz to 2500 Hz. Additionally, the statistical characteristics of the probability density distribution of environmental noise in the Zengmu Basin were studied and analyzed. The findings indicate that the predominant ambient noise in the low-frequency range (less than 400 Hz) in the Zengmu Basin is primarily attributed to distant sources, commonly identified as ship radiation noise. In the high-frequency band (greater than 400 Hz), the marine ambient noise is primarily derived from the sea surface, predominantly in the form of wind-generated noise. In the frequency range of 25–1600 Hz, examined in this study, the power spectral density exhibits an average decrease of over 3 dB and a maximum decrease of over 5 dB with each doubling of frequency. When the frequency is below 400 Hz, there is a higher vertical spatial correlation to ambient noise. The vertical directivity of the noise energy is horizontal, meaning that it is perpendicular to the vertical array direction. Additionally, the probability distribution of the noise level approximately follows the Burr distribution. When the frequency exceeds 400 Hz, there is a low vertical spatial correlation to noise. The vertical directivity of environmental noise exhibits distinct grooves in the horizontal direction, and the probability distribution of the noise spectrum level generally follows a normal distribution.

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An overview of ambient sound using Ocean Observatories Initiative hydrophones

Ragland

Schwock

Munson

et al. 2022

The Journal of the Acoustical Society of America

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The Ocean Observatories Initiative (OOI) sensor network provides a unique opportunity to study ambient sound in the north-east Pacific Ocean. The OOI sensor network has five low frequency (Fs = 200 Hz) and six broadband (Fs = 64 kHz) hydrophones that have been recording ambient sound since 2015. In this paper, we analyze acoustic data from 2015 to 2020 to identify prominent features that are present in the OOI acoustic dataset. Notable features in the acoustic dataset that are highlighted in this paper include volcanic and seismic activity, rain and wind noise, marine mammal vocalizations, and anthropogenic sound, such as shipping noise. For all low frequency hydrophones and four of the six broadband hydrophones, we will present long-term spectrograms, median time-series trends for different spectral bands, and different statistical metrics about the acoustic environment. We find that 6-yr acoustic trends vary, depending on the location of the hydrophone and the spectral band that is observed. Over the course of six years, increases in spectral levels are seen in some locations and spectral bands, while decreases are seen in other locations and spectral bands. Last, we discuss future areas of research to which the OOI dataset lends itself.

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Statistical analysis and modeling of underwater wind noise at the northeast pacific continental margin

Cited by 4 publications

References 44 publications

Long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea

Long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea

Depth Spatial Characterization of Marine Environmental Noise in the Zengmu Basin

An overview of ambient sound using Ocean Observatories Initiative hydrophones

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