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
Yiqun ZHAO, Zhiyu ZHANG(), Xue DONG
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.Supported by:
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;基金资助:
CLC Number:
Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks[J]. Journal of Computer Applications, 2024, 44(7): 2310-2318.
赵亦群, 张志禹, 董雪. 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[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2310-2318.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023070915
模型 | 模型大小/(km×km) | 震源/km | 速度/(km·s-1) | 网格大小 | 优化器激活函数 | 迭代次数 |
---|---|---|---|---|---|---|
均匀速度模型 | 2×2 | (0.5,1.0) | 2.0 | 101×101 | Adam/l-atan | 2 000 |
速度渐变模型 | 2×2 | (1,0.5.0) | 2.0~3.2 | 101×101 | L-BFGS-B/l-atan | 2 000 |
Marmousi模型 | 2×2 | (1.0,1.0) | 1.5~4.5 | 101×101 | L-BFGS-B/l-atan | 3 000 |
TTI模型 | 2×2 | (1.5,1.0) | 2.0~6.0 | 101×101 | L-BFGS-B/l-atan | 2 000 |
VTI模型 | 10×10 | (3.0,0.0) | 1.5~4.5 | 201×201 | L-BFGS-B/l-atan | 2 500 |
Tab. 1 Parameters for various models
模型 | 模型大小/(km×km) | 震源/km | 速度/(km·s-1) | 网格大小 | 优化器激活函数 | 迭代次数 |
---|---|---|---|---|---|---|
均匀速度模型 | 2×2 | (0.5,1.0) | 2.0 | 101×101 | Adam/l-atan | 2 000 |
速度渐变模型 | 2×2 | (1,0.5.0) | 2.0~3.2 | 101×101 | L-BFGS-B/l-atan | 2 000 |
Marmousi模型 | 2×2 | (1.0,1.0) | 1.5~4.5 | 101×101 | L-BFGS-B/l-atan | 3 000 |
TTI模型 | 2×2 | (1.5,1.0) | 2.0~6.0 | 101×101 | L-BFGS-B/l-atan | 2 000 |
VTI模型 | 10×10 | (3.0,0.0) | 1.5~4.5 | 201×201 | L-BFGS-B/l-atan | 2 500 |
网络(训练点) | 平均绝对误差/s | 平均相对误差/% | 最大绝对误差/s | 训练时间/min |
---|---|---|---|---|
DRC-PINN(25%) | 3.52E-07 | 6.48E-5 | 7.69E-07 | 3.65 |
PINN(25%) | 1.15E-05 | 8.49E-4 | 4.58E-05 | 8.31 |
DRC-PINN(50%) | 9.35E-09 | 1.06E-6 | 2.69E-08 | 5.87 |
PINN(50%) | 7.27E-06 | 3.16E-5 | 1.56E-05 | 14.38 |
DRC-PINN(75%) | 8.61E-09 | 9.83E-7 | 1.58E-08 | 9.39 |
Tab. 2 Performance comparison before and after improvement of PINN with different training points
网络(训练点) | 平均绝对误差/s | 平均相对误差/% | 最大绝对误差/s | 训练时间/min |
---|---|---|---|---|
DRC-PINN(25%) | 3.52E-07 | 6.48E-5 | 7.69E-07 | 3.65 |
PINN(25%) | 1.15E-05 | 8.49E-4 | 4.58E-05 | 8.31 |
DRC-PINN(50%) | 9.35E-09 | 1.06E-6 | 2.69E-08 | 5.87 |
PINN(50%) | 7.27E-06 | 3.16E-5 | 1.56E-05 | 14.38 |
DRC-PINN(75%) | 8.61E-09 | 9.83E-7 | 1.58E-08 | 9.39 |
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