Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2310-2318.DOI: 10.11772/j.issn.1001-9081.2023070915

• Frontier and comprehensive applications • Previous Articles    

Anisotropic travel time computation method based on dense residual connection physical information neural networks

Yiqun ZHAO, Zhiyu ZHANG(), Xue DONG   

  1. College of Automation and Information Engineering,Xi’an University of Technology,Xi’an Shaanxi 710048,China
  • Received:2023-07-11 Revised:2023-10-02 Accepted:2023-10-13 Online:2023-10-26 Published:2024-07-10
  • Contact: Zhiyu ZHANG
  • About author:ZHAO Yiqun, born in 1998, M. S. candidate. His research interests include deep learning, seismic signal processing.
    DONG Xue, born in 1997, M. S. candidate. Her research interests include seismic signal processing.
    First author contact:ZHANG Zhiyu, born in 1966, Ph. D., professor. His research interests include deep learning, signal processing.
  • Supported by:
    National Natural Science Foundation of China(U21A20485)

基于密集残差物理信息神经网络的各向异性旅行时计算方法

赵亦群, 张志禹(), 董雪   

  1. 西安理工大学 自动化与信息工程学院,西安 710048
  • 通讯作者: 张志禹
  • 作者简介:赵亦群(1998—),男,河南安阳人,硕士研究生,主要研究方向:深度学习、地震信号处理;
    董雪(1997—),女,甘肃天水人,硕士研究生,主要研究方向:地震信号处理。
    第一联系人:张志禹(1966—),男,山西朔州人,教授,博士,主要研究方向:深度学习、信号处理;
  • 基金资助:
    国家自然科学基金资助项目(U21A20485)

Abstract:

In order to solve problems that the travel time calculation by Physical Information Neural Network (PINN) is only applied to isotropic media at present, the error is large and the efficiency is low when far away from the seismic source, and finite difference method, shooting method and bending method have high computational cost on multiple seismic sources and high-density grids, a method of calculating the travel time using Dense Residual Connection PINN (DRC-PINN) in anisotropic media was proposed. Firstly, the eikonal equation after anisotropic factorization was derived as the loss function term. Secondly, the local adaptive arctangent function was selected as the activation function and Limited-memory Broyden-Fletcher-Goldfarb-Shanno-B (L-BFGS-B) was used as the optimizer. Finally, the network was trained in a segmented manner, the deep dense residual network was trained first, their parameters were frozen, and then the shallow dense residual network with physical meaning was trained, so that the network was evaluated and the travel time was obtained. The experimental results show that the maximum absolute error of the proposed method is 0.015 8 μs in the uniform velocity model, and the average absolute errors in other velocity models are reduced by two orders of magnitude, and the efficiency is doubled compared with that of the original model. The proposed method is obviously better than fast sweeping method.

Key words: deep learning, Physical Information Neural Network (PINN), anisotropy, travel time, eikonal equation

摘要:

针对目前利用物理信息神经网络计算旅行时只是应用在各向同性介质上、在远离震源时误差较大和效率低等问题,而有限差分法、试射法和弯曲法等方法在多震源、高密度网格上计算成本高等问题,提出一种密集残差物理信息神经网络计算各向异性介质旅行时的方法。首先推导了各向异性因式分解后的程函方程作为损失函数项;其次引入局部自适应反正切函数为激活函数和L-BFGS-B(Limited-memory Broyden-Fletcher-Goldfarb-Shanno-B)作为优化器;最后在网络中采用分段式训练的方式,先训练深层密集残差网络,然后冻结其参数,再训练具有物理意义的浅层密集残差网络,从而评估网络得到旅行时。实验结果表明,所提方法在均匀速度模型下的旅行时最大绝对误差达到了0.015 8 μs,其他速度模型下平均绝对误差平均下降了两个数量级,在效率方面也平均提高了1倍,明显优于快速扫描法。

关键词: 深度学习, 物理信息神经网络, 各向异性, 旅行时, 程函方程

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