利用光学卫星数据和深度学习,绘制中国海油污的地图
Lifeng Wang1, Yingcheng Lu1, Mingxiu Wang1
1International Institute for Earth System Science, Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China.
Journal of hazardous materials
|September 15, 2024
概括
一个新的深度学习模型使用卫星数据准确地识别和量化海洋石油泄漏. 船舶排放是主要来源,但平台泄漏会在中国海中产生更持久的石油乳液.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 海洋石油泄漏,包括非乳化油渣 (NEOS) 和油乳液 (OE),对海洋生态系统构成重大威胁.
- 光学遥感提供了一个独特的能力,以区分油类型和估计基于光谱签名的体积,与海水相比.
研究的目的:
- 开发和应用深度学习模型,用于在海洋环境中识别,分类和量化各种气候变化油类型.
- 利用卫星数据绘制中国海油污的来源,数量和空间分布图.
主要方法:
- 开发了一个深度学习模型来分析漏油事件.
- 在2019年4月至2023年8月期间,从7个卫星传感器收集了数据.
- 该模型用于分析含有石油的数据集,以识别和量化石油泄漏.
主要成果:
- 在中国海发生的石油泄漏事件主要源于船舶排放 (85.8%).
- 与船舶排放 (13.1%) 相比,平台泄漏导致石油乳液的比例更高 (58.6%).
- 漏油被绘制出地图,并与主要港口位置相关联,包括近海青岛,荣,博海湾,北海和越南沿海地区.
结论:
- 从平台上钻探石油是中国海的海洋石油污染的重要来源.
- 该研究提供了关于海洋石油泄漏的空间分布和来源的详细见解.
- 调查结果提高了对海上和海洋石油泄漏的评估和管理策略.
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