集成的气象海洋和地震数据集用于基于人工智能的海水CO2估计在南极洲的欺骗岛
S Flecha1, S Amaya-Vías2, M Medina3
1Instituto de Ciencias Marinas de Andalucía (ICMAN), Consejo Superior de Investigaciones Científicas (CSIC), 11519, Puerto Real, Spain. susana.flecha@csic.es.
Scientific data
|November 29, 2025
概括
这项研究为南极海岸系统提供了关键的碳循环数据,使用深度学习来估计欺骗岛上的二氧化碳水平.
科学领域:
- 环境科学 环境科学
- 海洋学 海洋学 海洋学
- 气候科学 气候科学
背景情况:
- 南极海岸系统对于全球碳循环调节至关重要,但样本不足.
- 对这些地区的碳流量的准确数据对于气候建模至关重要.
- 南极洲的欺骗岛为研究极地海洋碳动力学提供了一个独特的环境.
研究的目的:
- 展示来自欺骗岛的环境变量的高分辨率数据集.
- 通过数据驱动的建模,加强对海水pCO2的直接观测.
- 支持对空气-海气交换和南极碳系统动态的研究.
主要方法:
- 收集了高分辨率的环境数据,包括海水pCO2,温度,盐度和地震信号.
- 应用了深度学习方法,特别是双向长期短期记忆 (Bi-LSTM) 神经网络.
- 在多变量序列上训练Bi-LSTM模型以估计海水pCO2,通过交叉验证进行验证.
主要成果:
- 创建了一个全面的数据集,包括测量和估计的海水pCO2值.
- 该Bi-LSTM模型证明了海水pCO2的有效估计.
- 所有数据和处理步骤都符合FAIR数据可访问性原则.
结论:
- 这项研究为了解南极海岸碳循环提供了有价值的增强数据.
- 深度学习模型可以有效地增强碳流研究的直接测量.
- 该数据集支持空海气交换研究和遥感校准方面的进展.
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