The assessment of submarine slope instability in Baiyun Sag using gray clustering method

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• 作者：Cuilin Li (1)
  Shiguo Wu (1)
  Zhenyu Zhu (2)
  Xingxian Bao (3)
  
• 关键词：Seafloor stability ; Baiyun submarine slope ; Risk assessment ; Gray evaluation method
• 刊名：Natural Hazards
• 出版年：2014
• 出版时间：November 2014
• 年：2014
• 卷：74
• 期：2
• 页码：1179-1190
• 全文大小：4,492 KB
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• 作者单位：Cuilin Li (1)
  Shiguo Wu (1)
  Zhenyu Zhu (2)
  Xingxian Bao (3)
  
  1. Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, Shandong, China
  2. Beijing Research Center, CNOOC Research Institute, Beijing, 100027, China
  3. Department of Naval Architecture and Ocean Engineering, School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
  
• ISSN：1573-0840

文摘

It is a significant task to evaluate the seafloor stability for the exploitation of marine resources and the construction of marine structures. This paper analyzes the impact factors of submarine slope instability using geological and geophysical data of the Baiyun Sag in the Pearl River Mouth Basin and builds the evaluation index system of submarine slope instability. The weight of each index is calculated by analytic hierarchy process method to weaken subjective effect. Gray clustering method is applied to estimate the risk of submarine slope instability in Baiyun Sag qualitatively and quantitatively. The assessment standard is divided into five grades, and the whitening function of each gray cluster is determined by the assessment scheme. The gray evaluation weight vector of each indicator is calculated by the table-dispatching method. The gray assessment value is estimated by making the gray assessment weight vector single value, and the gray grade is determined according to the maximum principle. The evaluation results show quantitatively that the risk grade of submarine slope instability in the Baiyun Sag is medium, and the gray clustering method is reasonable and feasible.