Title: Physics-informed waveform inversion using pretrained wavefield neural operators Show Chinese title

Title: 基于预训练波场神经算子的物理信息波形反演

Abstract: Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is often hindered, considering the limited data, by its null space resulting in low-resolution models, and more importantly, by its computational cost, especially if needed for real-time applications. Recent attempts to accelerate FWI using learned wavefield neural operators have shown promise in efficiency and differentiability, but typically suffer from noisy and unstable inversion performance. To address these limitations, we introduce a novel physics-informed FWI framework to enhance the inversion in accuracy while maintaining the efficiency of neural operator-based FWI. Instead of relying only on the L2 norm objective function via automatic differentiation, resulting in noisy model reconstruction, we integrate a physics constraint term in the loss function of FWI, improving the quality of the inverted velocity models. Specifically, starting with an initial model to simulate wavefields and then evaluating the loss over how much the resulting wavefield obeys the physical laws (wave equation) and matches the recorded data, we achieve a reduction in noise and artifacts. Numerical experiments using the OpenFWI and Overthrust models demonstrate our method's superior performance, offering cleaner and more accurate subsurface velocity than vanilla approaches. Considering the efficiency of the approach compared to FWI, this advancement represents a significant step forward in the practical application of FWI for real-time subsurface monitoring. Show Chinese abstract

Abstract: 全波形反演（FWI）对于重建高分辨率地下模型至关重要，但考虑到数据有限，其受到零空间的阻碍，导致低分辨率模型，更重要的是，其计算成本较高，尤其是在需要实时应用的情况下。 最近尝试使用学习的波场神经算子来加速FWI，在效率和可微性方面显示出潜力，但通常存在噪声大和不稳定反演性能的问题。 为了解决这些限制，我们引入了一个新的物理信息FWI框架，在保持基于神经算子的FWI效率的同时，提高反演的准确性。 除了仅依赖自动微分的L2范数目标函数，导致噪声模型重建外，我们在FWI的损失函数中集成了一个物理约束项，提高了反演速度模型的质量。 具体来说，从初始模型开始模拟波场，然后评估生成的波场遵守物理定律（波动方程）并匹配记录数据的程度，从而减少噪声和伪影。 使用OpenFWI和Overthrust模型的数值实验表明，我们的方法表现出优越的性能，提供了比传统方法更清洁和准确的地下速度模型。 考虑到该方法与FWI相比的效率，这一进展在FWI的实际应用中迈出了重要的一步，用于实时地下监测。