从单个地震传感器对鱼范围估计的分类准确性进行分类
Andreia Pereira1, Carolina Marques2, Rose Hilmo3
1Instituto Dom Luiz, University of Lisbon, Lisbon 1749-016, Portugal.
The Journal of the Acoustical Society of America
|February 13, 2026
概括
一个新的决策树模型准确地分类了海底地震计 (OBS) 数据,用于跟踪鱼的声音. 这提高了使用地震信号的海洋哺乳动物监测.
科学领域:
- 海洋生物声学 海洋生物声学
- 地震学 地震学
- 机器学习 机器学习
背景情况:
- 海底地震计 (OBS) 越来越多地用于鱼的声学监测.
- 单站测距技术,就像三组件 (3C) 方法一样,使用地面运动方向来估计鱼信号范围.
- 需要进行分类过程,以确保基于仪器深度的3C范围估计的有效性.
研究的目的:
- 开发和评估分类模型,以确定从OBS数据3C范围估计的有效性.
- 提高地震数据对海洋哺乳动物监测和保护的有用性.
主要方法:
- 训练和评估了三个分类模型:决策树 (DTs),通用添加模型和神经网络.
- 用鱼从六个地点发出20Hz的呼叫来进行模型开发.
- 包含的变量包括通道振幅,信号质量,偏振和估计的信号角度.
主要成果:
- 决策树 (DT) 模型在测试数据上以0.94准确度实现了最高的性能.
- 范围估计有效性的关键预测因素包括水平与垂直幅度比率差异,极化指标和Y频道幅度.
- 开发的框架提高了基于地震的鱼跟踪的可靠性.
结论:
- 决策树提供了一种可靠的方法来分类地震衍生的鱼范围的有效性.
- 该框架加强了OBS数据在海洋哺乳动物研究和保护工作中的应用.
- 对地震信号进行准确的分类对于有效的生物声学监测至关重要.
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