.png){kind=icon}
返回
Wave propagation modeling using machine learning-based finite difference scheme
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jcp.2025.113870.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The staggered-grid finite-difference (SGFD) method is essential in wave forward modeling, waveform inversion, and seismic imaging. However, the numerical dispersion that can lead to reduced accuracy in simulations may arise from either coarse spatial discretization or a suboptimal SGFD scheme. Given the high computational cost associated with finer spatial steps, employing the optimal SGFD scheme offers a feasible and effective approach for dispersion suppression. However, the commonly used SGFD schemes are limited by a narrow maximum wavenumber range, reducing their dispersion suppression efficacy. To address this issue, a machine learning-based SGFD scheme is presented in this study. A composite objective function that combines the sum of the absolute error and the maximum absolute error is proposed, aiming to broaden the maximum wavenumber range while minimizing the cumulative error. A physics- consistent neural network is constructed by specifying weights, biases, activation functions, layer connections, and loss function, enabling the back-propagation of the proposed objective function within the machine learning framework to yield globally optimal SGFD coefficients.
Keyword:
Machine learning
Staggered-grid finite difference scheme
Wave propagation modeling
期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
机构
引用论文
.png){kind=icon}
分享收藏A family of low dispersive and low dissipative explicit schemes for flow and noise computations
PREAI
分享收藏The Use of Nitrogen Dioxide to Improve the Superconducting Properties of YBa2Cu3O7使用二氧化氮改善YBa2Cu3O7的超导性能
PREAI
分享收藏分享收藏
查看更多内容