用神经算子加速地震波反演，提升真实数据处理效率
Ambient Noise Full Waveform Inversion with Neural Operators
- 用神经算子替代传统数值方法求解地震波动方程
- 在洛杉矶盆地真实数据上实现快速反演，避免周期跳跃问题
- 结合PyTorch优化器，适合大规模地震成像研究者
地震波传播的数值模拟对揭示速度结构和提升地震风险评估至关重要。然而，传统的有限差分或有限元方法计算成本高昂。近期研究表明，一类新型机器学习模型——神经算子，可比传统方法快多个数量级地求解弹性波动方程。全波形反演正是这一加速模拟的重要受益者。神经算子作为端到端可微算子，结合自动微分，为反向状态法提供了替代方案。PyTorch内置的先进优化技术使神经算子在全波形反演优化过程中更具灵活性，有效缓解了周期跳跃问题。本研究首次将神经算子应用于真实地震数据集的全波形反演，数据来自洛杉矶都会区圣加布里埃尔、奇诺和圣伯纳迪诺盆地的多个节点测线。
原文摘要 · Abstract (English)
Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end-to-end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint-state method. State-of-the-art optimization techniques built into PyTorch provide neural operators with greater flexibility to improve the optimization dynamics of full waveform inversion, thereby mitigating cycle-skipping problems. In this study, we demonstrate the first application of neural operators for full waveform inversion on a real seismic dataset, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.
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