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Research on Strategy Marine Noise Map Based on i4Ocean Platform: Constructing Flow and Key Approach
  • ISSN号:1672-5182
  • 期刊名称:《中国海洋大学学报:英文版》
  • 时间:0
  • 分类:TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] P733.22[天文地球—物理海洋学;天文地球—海洋科学]
  • 作者机构:[1]College of Information Engineering, System Science Post-Doctoral Mobile Stations, Qingdao University, Qingdao 266071, P. R. China, [2]College of Information Science and Engineering, Ocean University of China, Qingdao 266100, P. R. China, [3]State Environmental Protection Key Laboratory of Estuary and Coastal Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China
  • 相关基金:This work is supported by the Global Change and Air-Sea Interaction Project (GASI-03-01-01-09), the National Natural Science Foundation of China under Grant No. 61170106, the Project funded by China Postdoctoral Science Foundation under Grant No. 2015M571993 and Qingdao Postdoctoral Application Research Funded Project.
中文摘要:

Noise level in a marine environment has raised extensive concern in the scientific community.The research is carried out on i4 Ocean platform following the process of ocean noise model integrating,noise data extracting,processing,visualizing,and interpreting,ocean noise map constructing and publishing.For the convenience of numerical computation,based on the characteristics of ocean noise field,a hybrid model related to spatial locations is suggested in the propagation model.The normal mode method K/I model is used for far field and ray method CANARY model is used for near field.Visualizing marine ambient noise data is critical to understanding and predicting marine noise for relevant decision making.Marine noise map can be constructed on virtual ocean scene.The systematic marine noise visualization framework includes preprocessing,coordinate transformation interpolation,and rendering.The simulation of ocean noise depends on realistic surface.Then the dynamic water simulation gird was improved with GPU fusion to achieve seamless combination with the visualization result of ocean noise.At the same time,the profile and spherical visualization include space,and time dimensionality were also provided for the vertical field characteristics of ocean ambient noise.Finally,marine noise map can be published with grid pre-processing and multistage cache technology to better serve the public.

英文摘要:

Noise level in a marine environment has raised extensive concern in the scientific community. The research is carried out on i4Ocean platform following the process of ocean noise model integrating, noise data extracting, processing, visualizing, and inter- preting, ocean noise map constructing and publishing. For the convenience of numerical computation, based on the characteristics of ocean noise field, a hybrid model related to spatial locations is suggested in the propagation model. The normal mode method K/I model is used for far field and ray method CANARY model is used for near field. Visualizing marine ambient noise data is critical to understanding and predicting marine noise for relevant decision making. Marine noise map can be constructed on virtual ocean scene The systematic marine noise visualization framework includes preprocessing, coordinate transformation interpolation, and rendering. The simulation of ocean noise depends on realistic surface. Then the dynamic water simulation gird was improved with GPU fusion to achieve seamless combination with the visualization result of ocean noise. At the same time, the profile and spherical visualization include space, and time dimensionality were also provided for the vertical field characteristics of ocean ambient noise. Finally, ma- rine noise map can be published with grid pre-processing and multistage cache technology to better serve the public.

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期刊信息
  • 《中国海洋大学学报:英文版》
  • 中国科技核心期刊
  • 主管单位:教育部
  • 主办单位:中国海洋大学
  • 主编:文圣常
  • 地址:青岛市松岭路238号
  • 邮编:266100
  • 邮箱:xbywb@ouc.edu.cn
  • 电话:0532-66782408
  • 国际标准刊号:ISSN:1672-5182
  • 国内统一刊号:ISSN:37-1415/P
  • 邮发代号:24-89
  • 获奖情况:
  • 被美国化学文摘(CA)和美国剑桥科学文摘(CSA)收录
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国化学文摘(网络版),英国农业与生物科学研究中心文摘,荷兰文摘与引文数据库,美国剑桥科学文摘,美国科学引文索引(扩展库),美国生物科学数据库,英国动物学记录,中国中国科技核心期刊
  • 被引量:123