摘要
在研究富钴结壳高产区地形特征基础上,以富钴结壳站点地理坐标为中心,获得了一平方公里的海拔高度数值矩阵作为地形特征.使用卷积神经网络的分析方法对数值矩阵进行训练,学习坡度和平整度等区域特征,将富钴结壳站点地形和其他海底地形进行区分.依据训练后获得的模型,对富钴结壳高产区进行预测,取得了较好的预测效果,结合其他因素的影响,可以提高结壳靶区选取的精准度.
Cobalt-rich crusted deposits are found all over the world’s oceans,and their distribution is closely related to the submarine topography.The determination of crusting area is the basic work for the exploration and mining of these deposits.Many factors affect the accumulation of crusts,and topography is a crucial factor.Mineralization forecast requires comprehensive consideration of geological background and experts’views and opinions,the prior knowledge of prospectors is the biggest factor affecting the results.In the course of ocean research,especially with the rapid development of space information technology,a huge amount of ocean data that cover about 70% of the total surface area have been accumulated rapidly;how to extract valuable information from large,fast,complex,and multisource data has become a hot topic in current ocean research.Machine learning-and deep learningrelated research methods can read feature signs from mineral data to obtain existing mineral knowledge to further serve mine prediction work.Based on the study of terrain features of cobalt-rich crust in high-producing areas,the numerical matrix of altitude of 1 km^2 ocean surface was obtained,with the geographical coordinates of cobalt-rich crust sites as the center.Using the analysis method of convolutional neural network,the numerical matrix is trained to learn regional features such as slope and flatness and to distinguish the cobalt-rich crust-crust site topography from other submarine topography.According to the training model,the high-producing cobaltrich crusting area was predicted and better forecasting value is obtained.Meanwhile,the accuracy of the selection of crusting target area was improved by combining the influence of other factors.
作者
袁传新
贾东宁
周生辉
YUAN Chuan-xin;JIA Dong-ning;ZHOU Sheng-hui(College of Information Science and Engineering,Ocean University of China,Qingdao 266000,China;High-performance Scientific Computing and System Simulation Platform,Pilot National Laboratory for Marine Science and Technology(QingDao),Qingdao 266000,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2020年第12期1597-1604,共8页
Chinese Journal of Engineering
基金
海洋大数据中心资助项目(2018SDPT01)。
关键词
富钴结壳
海底地形
数值矩阵
卷积神经网络
矿区预测
cobalt-rich crust
seafloor terrain
numerical matrix
convolutional neural network
mining area forecast
