混合模型提升高对比度介质中波散射的模拟精度。
Hybrid operator learning of wave scattering maps in high-contrast media
- 将波传播分解为背景平滑部分与高对比度散射修正两部分分别建模
- 在高频赫姆霍兹问题上相位和振幅误差显著低于单一FNO或Transformer
- 适合需要高精度波场模拟的地震成像与反演任务
在非均质介质中对波传播与散射（即波速和源到波场的映射）进行代理建模，在地震成像与反演等应用中具有重要潜力。高对比度场景（如含盐体的地下模型）表现出强烈的散射和相位敏感性，挑战现有神经算子。我们提出一种混合架构，将散射算子分解为两个独立贡献：平滑背景传播与高对比度散射修正。背景部分由傅里叶神经算子（FNO）学习，生成全局耦合的特征标记以编码背景波传播；这些标记随后输入视觉变压器，利用注意力机制建模主导强空间相互作用的高对比度散射修正。在具有强对比度的高频赫姆霍兹问题上评估，该混合模型相比独立的FNO或变压器显著提升了相位和振幅精度，并展现出有利的精度-参数缩放特性。
原文摘要 · Abstract (English)
Surrogate modeling of wave propagation and scattering (i.e. the wave speed and source to wave field map) in heterogeneous media has significant potential in applications such as seismic imaging and inversion. High-contrast settings, such as subsurface models with salt bodies, exhibit strong scattering and phase sensitivity that challenge existing neural operators. We propose a hybrid architecture that decomposes the scattering operator into two separate contributions: a smooth background propagation and a high-contrast scattering correction. The smooth component is learned with a Fourier Neural Operator (FNO), which produces globally coupled feature tokens encoding background wave propagation; these tokens are then passed to a vision transformer, where attention is used to model the high-contrast scattering correction dominated by strong, spatial interactions. Evaluated on high-frequency Helmholtz problems with strong contrasts, the hybrid model achieves substantially improved phase and amplitude accuracy compared to standalone FNOs or transformers, with favorable accuracy-parameter scaling.
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