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基于噪声级指标的东阿拉伯海浅海水声声景变异性的统计研究。

Statistical study on shallow water soundscape variability of Eastern Arabian Sea using noise level metrics.

机构信息

Department of Physical Oceanography, School of Marine Science, Cochin University of Science and Technology, Kochi, Kerala, 682016, India.

Naval Physical and Oceanographic Laboratory, Ministry of Defense, Thrikkakara, Kochi, Kerala, 682021, India.

出版信息

Environ Monit Assess. 2023 Oct 13;195(11):1314. doi: 10.1007/s10661-023-11912-4.

Abstract

Underwater soundscape that spans a broad frequency band shows variability consistent with contributing noise sources and ocean environment. However, increased anthropogenic activities result in noise proliferation which can harm natural marine habitat. Continuous monitoring of background sound is useful to assess such spatio-temporal variability of soundscape. Standard noise level metrics, for instance, mean (μ), 90th percentiles (90P), standard deviation (σ), and kurtosis (β), are constructed from noise field measured from three coastal stations in Eastern Arabian Sea. These metrics are found to be suitable to describe the soundscape variability with respect to season, frequency, and depth. Mean and 90P are used to compare the seasonal variations while kurtosis metrics are exercised to check the impulsive nature of composite signal. Histogram representation and probability density function (PDF) were utilized to analyze the spectral variation in soundscape with respect to season. Analysis was carried out at 500-ms temporal window in two spectral bands corresponding to traffic and wind noise fields. Seasonal analysis shows that in summer, mean noise level decreases as hydrophone depth increases, while in winter, deeper depths have higher mean value with the presence of seasonal surface duct. This implication of sound speed profile on noise field has also been confirmed using appropriate noise model.

摘要

涵盖较宽频带的水下声景表现出与噪声源和海洋环境一致的可变性。然而,人为活动的增加导致噪声扩散,从而危害自然海洋生境。对背景声音的连续监测有助于评估声景的这种时空可变性。例如,从阿拉伯海东部的三个沿海站测量的噪声场构建了标准噪声级指标,例如平均值 (μ)、90 百分位数 (90P)、标准差 (σ) 和峰度 (β)。这些指标被发现适合描述与季节、频率和深度有关的声景变化。均值和 90P 用于比较季节性变化,而峰度指标用于检查复合信号的脉冲性质。直方图表示法和概率密度函数 (PDF) 用于分析声景随季节的频谱变化。在两个对应于交通和风声场的频谱带中,以 500-ms 的时间窗口进行分析。季节性分析表明,在夏季,随着水听器深度的增加,平均噪声水平降低,而在冬季,由于季节性表面波导的存在,较深的深度具有更高的平均值。还使用适当的噪声模型证实了声速剖面对噪声场的这种影响。

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