| 基于多波束背向散射强度信号的海底表层沉积物粒度分类研究——以澳洲Joseph Bonaparte湾为例 |
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| 引用本文： | 徐韦,程和琴,黄知,郑树伟,陈钢.基于多波束背向散射强度信号的海底表层沉积物粒度分类研究——以澳洲Joseph Bonaparte湾为例[J].海洋学报,2019,41(1):172-182. |
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| 作者姓名： | 徐韦 程和琴 黄知 郑树伟 陈钢 |
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| 作者单位： | 华东师范大学 河口海岸学国家重点实验室,上海,200062;Geoscience Australia,GPO Box 378,Canberra,ACT 2601 |
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| 基金项目： | 国家自然科学基金项目（51761135023）；华东师范大学河口海岸学国家重点实验室开放研究课题（sklec-kf201504）。 |
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| 摘 要： | 近海海底地形探测与沉积物精确分类对涉海工程建设、生物栖息地反演以及海底资源勘查与开发具有重要的现实意义。以澳洲Joseph Bonaparte湾为例，利用多波束测深技术获取了该海湾约880 km2水域的水深数据与背向散射强度信号，结合同步采集的54个海底表层沉积物样品，通过随机决策树模型对该海域海底表层沉积物进行了分类研究。结果表明:（1）利用随机决策树模型分析该海域沉积物类型与背向散射强度的关系时，当模型内部参数设置:树的总数为200，最小分裂节点为2，每棵树的最大分裂级数为5时，可提高预测准确率；（2）该参数设置下，利用13°和37°入射角的背向散射强度预测该海域沉积物类型时，准确率最高，其值为83.3%，且在研究海域，砂质砾和砾质砂分布在背向散射强度较强的深槽或海沟等地区，而砾质泥质砂和含砾泥质砂主要分布在背向散射强度较弱的浅水海域。分析还发现，当水深数据作为预测海底表层沉积物类型的特征变量时，有可能降低最终预测结果的准确率。
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| 关 键 词： | 底质分类 随机决策树模型 背向散射强度 Joseph Bonaparte湾 |
| 收稿时间： | 2017/11/12 0:00:00 |
| 修稿时间： | 2018/3/11 0:00:00 |
Classifying the grain size of seabed sediments based on multibeam backscatter data-A case study in Joseph Bonaparte Gulf, Australia |
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| Xu Wei,Cheng Heqin,Huang Zhi,Zheng Shuwei and Chen Gang.Classifying the grain size of seabed sediments based on multibeam backscatter data-A case study in Joseph Bonaparte Gulf, Australia[J].Acta Oceanologica Sinica (in Chinese),2019,41(1):172-182. |
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| Authors: | Xu Wei Cheng Heqin Huang Zhi Zheng Shuwei and Chen Gang |
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| Institution: | 1.State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China2.Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia |
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| Abstract: | The accurate information of subaqueous topography and seabed substrata are of great significant for marine engineering construction, benthic habitat mapping, and management of marine protected areas (MPAs). The bathymetric and backscatter data of 880 km2 in the Joseph Bonaparte Gulf, Northern Australia were collected by using a multi-beam echo-sounder system (Kongsberg''s 300 kHz EM3002), and 54 samples of seabed sediments were collected simultaneously. The Random Forest Decision Tree (RFDT) was chosen as the modelling method for prediction. The results show that:(1) Improvement of the predicted accuracy for bed sediment classification is made when the parameters of RDFT are set as "number of trees" 200, "minimum size node to split" 2 and the "maximum splitting levels" 5 in this paper. (2) The highest accuracy of 83.3% is predicted from the incidence angle (backscatter) of 13° and 37°, and the coarse sediment, such as sandy gravel and gravelly sand are mainly distributed in the area with stronger backscatter intensity, but the fine sediment, such as gravelly muddy sand and (gravelly) muddy sand are distributed in the shallow area. However, it is noteworthy that the predicted accuracy of sediment classification may decrease when bathymetry data is chosen as the characteristic variable with the backscatter. |
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| Keywords: | classification of seabed sediment Random Forest Decision Tree multibeam backscatter intensity Joseph Bonaparte Gulf |
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