多源数据融合的溢油扩散预测与响应技术研究  

Research on Multi-source Data Fusion for Oil Spill Diffusion Prediction and Response Technology

作　　者：张鹏[1] 王长虹 冯业刚[1] 栾海滨[1] 刘园超 Zhang Peng;Wang Changhong;Feng Yegang;Luan Haibin;Liu Yuanchao(Shengli Oil Production Plant,Shengli Oilfield Branch,China Petroleum&Chemical Corporation,Dongying 257051)

机构地区：[1]中国石油化工股份有限公司胜利油田分公司胜利采油厂,山东东营257051

出　　处：《石化技术》2025年第3期30-32,共3页Petrochemical Industry Technology

摘　　要：溢油事故导致水域生态系统破坏程度持续加剧,高精度溢油扩散范围预测对应急响应具有重要意义。基于多源数据融合框架,整合卫星遥感监测信息与水文气象数据,构建溢油动态扩散预测模型。通过深度学习算法提取多维特征,结合动力学模型,建立溢油扩散态势智能预测系统。在溢油应急实验中,预测精度达89.5%,相较传统单源数据模型提升15.2%。智能响应决策系统依托预测结果制定最优处置方案,实现溢油防控智能化管理,为水域生态环境保护提供科学依据。The escalating severity of ecological damage to aquatic ecosystems caused by oil spill incidents underscores the critical importance of high-precision predictions of spill diffusion for effective emergency response.Leveraging a multi-source data fusion framework,this study integrates satellite remote sensing monitoring information with hydro-meteorological data to construct a dynamic oil spill diffusion prediction model.By employing deep learning algorithms to extract multi-dimensional features and incorporating kinetic models,an intelligent oil spill diffusion prediction system is established.In oil spill emergency experiments,the model achieved a prediction accuracy of 89.5%,representing a 15.2%improvement over traditional single-source data models.The intelligent response decision-making system utilizes these predictions to formulate optimal mitigation strategies,enabling intelligent management of oil spill prevention and control.This approach provides a scientific foundation for the protection of aquatic ecological environments.

关 键 词：数据融合 溢油扩散 预测建模 应急响应 深度学习 

分 类 号：X55[环境科学与工程—环境工程]