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Journal of Tropical Oceanography

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Research on Seismic P - wave Identification Method Based on Convolutional Neural Network Optimized by RLPSO Algorithm

WANG Luowen1, 2, 3, YANG Zerong4, WEN Yongpeng4, ZHU Xinke2, 3, YAN Bo1, 2, 3, QIN Huawei1   

  1. 1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;

    2. State Key Laboratory of Submarine Geoscience, Hangzhou 310012, China;

    3. Second Institute of Oceanography, Hangzhou 310012, China;

    4. Guodian Electric Power Zhejiang Zhoushan Offshore WindPower Development Co.,Ltd, Zhoushan 316000, China

  • Received:2025-03-11 Revised:2025-04-11 Accepted:2025-04-18
  • Supported by:

     National Key R&D Program of China(2022YFC3003802); the Research Fund of the Key Laboratory of Marine Observation Technology, Ministry of Natural Resources(2024klootA11); the Open Fund of the Key Laboratory of Submarine Geoscience, Ministry of Natural Resources(KLSG2409)

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Abstract: In response to the demand for real-time monitoring of seismic P-waves in marine environments, a deep learning-based algorithm for seismic P-wave identification has been designed. This algorithm constructs a hybrid model for seismic P-wave identification by integrating a CNN(convolutional neural network) with a RLPSO(reinforcement learning particle swarm optimization) algorithm. The model is trained using a prior seismic data as prior information, and the RLPSO algorithm is employed to iteratively optimize the hyperparameters of the CNN. The optimized hyperparameters are then incorporated into the CNN model to achieve predict the arrival time of seismic P-wave identifications. To validate the effectiveness of the algorithm, simulation tests were conducted on a test dataset using different algorithms. The experimental results demonstrate that the proposed algorithm exhibits lower training loss and higher identification accuracy during the training process. Furthermore, the algorithm maintains high identification precision under low signal-to-noise ratio conditions, demonstrating showcasing strong robustness. The algorithm meets the requirements for real-time seismic P-wave identification in marine seismic monitoring systems.

Key words: Seismic P-wave identification, Convolutional Neural Network, Reinforcement Learning, Particle Swarm Optimization Algorithm