济州岛南部海域海底声呐图像分析与声学底质分类

唐秋华; 李杰; 周兴华; 陆凯; 张志珣

doi:10.3969/j.issn.0253-4193.2014.04.015

济州岛南部海域海底声呐图像分析与声学底质分类

doi: 10.3969/j.issn.0253-4193.2014.04.015

1.
国土资源部海洋油气资源与环境地质重点实验室, 山东青岛 266071;国家海洋局第一海洋研究所, 山东青岛 266061
2.
国家海洋局第一海洋研究所, 山东青岛 266061
3.
青岛海洋地质研究所, 山东青岛 266071

基金项目: 国土资源部海洋油气资源与环境地质重点实验室资助项目（MRE201107）；山东省自然科学基金资助项目（ZR2009FM005）；中央级公益性科研院所基本科研业务费专项资金资助项目（GY02-2011T05）；国土资源大调查项目资助（GZH201100302）；国家自然科学基金资助项目（40706038）。

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出版历程
- 收稿日期: 2013-04-23
- 修回日期: 2013-12-20

Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island

1.
Key Laboratory of Marine Hydrocarbon Resources and Environmental Geology, Ministry of Land and Resources, Qingdao 266071, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
2.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
3.
Qingdao Institute of Marine Geology, Qingdao 266071, China

摘要

摘要: 东海北部外陆架靠近济州岛南部海域，是黄海槽向冲绳海槽延伸的部分，属于黑潮分支黄海暖流的通道入口，分布着脊槽相间的海底底形，对其海底声呐图像的处理分析及声学底质分类的分析研究，有助于了解该通道海底底形表层纹理特征及沉积物分布规律。基于在济州岛南部海域获取的多波束声呐数据，应用图像处理技术和方法，对数据进行了处理，获得了海底声呐影像图，并对其表层纹理特征进行了描述和分析；同时，基于多波束反向散射强度数据，结合19组海底地质取样数据，建立研究区海底反向散射强度与沉积物粒度特征之间的统计关系模型，并以改进的学习向量量化神经网络方法，实现对海底粉砂质砂、黏土质砂以及砂-粉砂-黏土3种底质类型的快速自动分类识别。
- 济州岛南部海域 /
- 多波束测深系统 /
- 海底声呐图像 /
- 声学底质分类
Abstract: The selected area in this paper is located in the south of the Cheju Island. The study area is part of the Yellow Sea Trough extends to the Okinawa Trough and it's in the pathway of the Yellow Warm Current which is one of the branches of the Kuroshio Warm Current. Its seabed sonar image processing analysis and acoustic seabed classification research,contribute to a comprehensive understanding of the characteristics of the channel seabed bedforms surface texture and sediment distribution pattern. With the high-precision multibeam echo sounder sonar data in the research area,we apply image processing techniques and methods and get the seabed sonar image,then we can do quantitative description and analysis to the seabed surface texture features. Based on multibeam echo sounder backscatter strength data and 19 geological seabed sediment sample data,a statistical model which presents the relationship between seabed backscatter signal and sediment type is set up. Using improved Learning Vector Quantization neural network methods,a fast and accurate automatic identification for three seabed sediment types (TS,YS,STY) implementation is feasible.
- the south of the Cheju Island /
- multibeam echo sounder /
- seabed sonar image /
- acoustic seabed classification

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参考文献(33)

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济州岛南部海域海底声呐图像分析与声学底质分类

doi: 10.3969/j.issn.0253-4193.2014.04.015

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Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island

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济州岛南部海域海底声呐图像分析与声学底质分类

doi: 10.3969/j.issn.0253-4193.2014.04.015

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Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island

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