期刊
OCEAN ENGINEERING
卷 171, 期 -, 页码 131-138出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2018.10.041
关键词
Akaike information criterion; Clarion-Clipperton fracture zone; Copula; Deep-sea manganese nodule miner; Joint probability density function; Seafloor manganese nodules
资金
- Endowment Project, Development of CAE based Core Technology for Component Design of Offshore Plant Equipment - Korea Research Institute of Ships and Ocean Engineering [PES9310]
- National R&D Project, Technology Development of Deep-Seabed Mining System for Manganese Nodules - Ministry of Oceans and Fisheries, Korea [PMS3090]
- Korea Institute of Marine Science & Technology Promotion (KIMST) [PES9310] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, probability distribution for the size and mass of seafloor manganese nodules is investigated to improve the reliability of a pick-up device in a deep-sea manganese nodule miner. Because the size and mass of the manganese nodules are strongly correlated, statistical models for the size and mass cannot be estimated independently. In order to consider the correlation between the size and mass in a statistical model, the joint probability density function (PDF) is estimated by using copula. This method requires the estimation of the marginal distributions and copula for the two correlated environmental variables. However, this is significantly difficult when there is no prior knowledge of the two physical properties. The proposed method, which employs the Akaike information criterion to select the fittest marginal distributions and copula, provides a systematic procedure to determine a statistical model of correlated environmental variables without any prior knowledge of their distributions. To demonstrate the effectiveness of the proposed method, the joint PDF for the size and mass of manganese nodules is modeled by using the multivariate normal distribution and the proposed method. It was found that the proposed method provides more accurate and reliable estimation results for the two correlated environmental variables.
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