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

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          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.

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          Author and article information

          Journal of Ocean University of China
          Science Press and Springer (China )
          15 November 2020
          01 December 2020
          : 19
          : 6
          : 1283-1291
          1Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education and College of Marine Geosciences, Ocean University of China, Qingdao 266100, China
          2Evaluation and Detection Technology Laboratory of Marine Mineral Resources, Pilot Qingdao National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China
          Author notes
          *Corresponding author: ZHANG Jin, E-mail: zhjmeteor@
          Copyright © Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020.

          The copyright to this article, including any graphic elements therein (e.g. illustrations, charts, moving images), is hereby assigned for good and valuable consideration to the editorial office of Journal of Ocean University of China, Science Press and Springer effective if and when the article is accepted for publication and to the extent assignable if assignability is restricted for by applicable law or regulations (e.g. for U.S. government or crown employees).

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