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.

翻译：被动声学监测为长期、大范围的珊瑚礁评估提供了潜力。为探索此方法，我们历时两年在新加坡海域十个珊瑚礁位点部署水下声学记录仪。为减轻持续的人为活动及海流噪声对低频珊瑚礁声景的干扰，我们训练了卷积神经网络降噪器。声学数据分析揭示了明显的清晨与黄昏音律。尽管原始噪声记录中低频部分的声学活动指数与环境变量的相关性被掩盖，降噪后的数据显示：声压级和声学复杂度指数等声学活动指标与潜水员评估的礁体健康参数（如活珊瑚丰富度、覆盖度及藻类覆盖率）存在相关性。此外，从高频声学频段计算获得的虾鸣率，在时空尺度上与珊瑚礁参数均呈现稳健相关性。本研究证实，在有效降噪与解译条件下，被动声学数据中蕴含的宝贵信息可用于珊瑚礁监测，该方法可推广至其他受持续性噪声干扰的海洋环境监测场景。