用声学监测珊瑚礁健康,降噪后数据与实际状况高度相关
Passive Acoustic-based Composite Indices for Reef Health Monitoring in Noisy Tropical waters
- 用卷积神经网络去除热带海域噪声,还原真实声景
- 降噪后声强和复杂度指数与活珊瑚覆盖率显著相关
- 小虾弹指频率可稳定反映礁区健康状况,适合长期监测
被动声学监测为长期、大范围珊瑚礁评估提供了可能。本研究在新加坡周边十处珊瑚礁站点部署水下录音设备,持续两年采集数据。为应对人为噪音和水流噪声对低频声景的干扰,训练了一个卷积神经网络去噪器。分析显示,声景存在明显的晨昏合唱现象。尽管原始噪声数据中声学指标与环境变量相关性被掩盖,但去噪后的数据表明声压级、声学复杂度指数等与潜水员评估的活珊瑚丰富度、覆盖度及藻类覆盖度存在显著相关性。此外,高频段计算的小虾弹指率在时空上均与礁区参数强相关。研究表明,有效去噪后,被动声学可提供有价值的礁区健康信息,该方法可推广至其他受持续噪声干扰的海洋环境。
原文摘要 · Abstract (English)
Passive acoustic monitoring offers the potential to enable long-term, spatially extensive assessments of coral reefs. To explore this approach, we deployed underwater acoustic recorders at ten coral reef sites around Singapore waters over two years. To mitigate the persistent anthropogenic and current-induced noise masking the low-frequency reef soundscape, we trained a convolutional neural network denoiser. Analysis of the acoustic data reveals distinct morning and evening choruses. Though the correlation with environmental variates was obscured in the low-frequency part of the noisy recordings, the denoised data showed correlations of acoustic activity indices such as sound pressure level and acoustic complexity index with diver-based assessments of reef health such as live coral richness and cover, and algal cover. Furthermore, the shrimp snap rate, computed from the high-frequency acoustic band, is robustly correlated with the reef parameters, both temporally and spatially. This study demonstrates that passive acoustics holds valuable information that can help with reef monitoring, provided the data is effectively denoised and interpreted. This methodology can be extended to other marine environments where acoustic monitoring is hindered by persistent noise.
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