深切V型峡谷物理驱动人工智能波动模拟

Wave simulation of symmetric V-shaped canyon based on physics-informed deep learning method

高山峡谷区场地地震效应是地震工程领域研究热点。V形峡谷引起的圆柱形SH波散射和衍射波函数的级数解已较为成熟,并为众多河谷区重大工程提供了合理、科学的地震动输入。采用物理驱动深度学习方法结合与解析结果的对比分析,近一步明确了V型河谷地形地震反应特性及复杂波场空间分布。此方法主要关注稀疏样本及可诠释性人工智能,结合强形式自动微分和软约束边界条件嵌入,建立深度神经网络实现半无限域地震传播模型。采用时间域分解策略,实现不同给定波场工况下V型河谷高精度预测。通过与解析解对比,评估了所提出的物理驱动人工智能方法的精度和效率。结果表明,物理驱动人工智能方法可应用于地形效应分析,柱面SH波在V型峡谷底端发生显著衰减与振荡,边缘区呈现放大效应。

The seismic effects of alpine and canyon sites are a research hotspot in the field of earthquake engineering.The series solution of the two-dimensional scattering and diffraction wave functions of cylindrical SH waves caused by V-shaped canyons is relatively mature and provides reasonable and scientific ground motion input for many major projects in river valleys.In this study,the physics-informed deep learning method combined with the comparative analysis of the analytical results is used to further clarify the seismic response characteristics and complex wave field spatial distribution of the V-shaped river valley.The method mainly focuses on sparse samples and interpretable artificial intelligence,and establishes a deep neural network to realize the semi-infinite seismic propagation model by combining the strong formal automatic differentiation with the soft constraint boundary condition embedding,and realizes high-precision prediction of V-shaped river valleys under different given wave field conditions by adopting the time domain decomposition strategy.By comparing with the analytical solution,the accuracy and efficiency of the proposed physics-driven artificial intelligence method are evaluated.The results show that the physics-driven artificial intelligence method can be applied to the analysis of terrain effects,and the cylindrical SH waves are significantly attenuated at the bottom of the V-shaped canyon,and the edge area shows an amplification effect.