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Inversion of Oceanic Parameters Represented by CTD Utilizing Seismic Multi-Attributes Based on Convolutional Neural Network

查看全文 作　　者：AN [1,2]Zhenfang;ZHANG [1,2]Jin;XING [1,2]Lei 高影响力作者 机构地区：[1]Key Laboratory of Submarine Geosciences and Prospecting Techniques,Ministry of Education and College of Marine Geosciences,Ocean University of China,Qingdao 266100,China;[2]Evaluation and Detection Technology Laboratory of Marine Mineral Resources,Pilot Qingdao National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266071,China高影响力机构 出　　处：《Journal of Ocean University of China》索引2020年第19卷第6期,共9页高影响力期刊 基　　金：This research is jointly funded by the National Key Research and Development Program of China(No.2017 YFC0307401);the National Natural Science Foundation of China(No.41230318);the Fundamental Research Funds for the Central Universities(No.201964017);and the National Science and Technology Major Project of China(No.2016ZX05024-001-002). 摘　　要：In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters. 关 键 词：oceanic parameter inversion seismic multi-attributes convolutional neural network
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