基础模型辅助的全波形反演
Foundation Model-Assisted Full Waveform Inversion
AI总结：
该研究提出用预训练地震基础模型SeisLM的特征构建全波形反演早期目标函数，通过实验验证其可避免周期跳变，提升反演效果，优于传统及混合损失工作流。
AI中文摘要：
全波形反演（FWI）可恢复高分辨率地下速度模型，然而当初始模型不准确时，传统的波形差目标函数易受周期跳变影响。我们引入一种FWI目标函数，该函数通过预训练地震基础模型SeisLM生成的特征来比较模拟与观测地震记录。在反演过程中，SeisLM编码器保持冻结，对特征差异关于模拟记录求导以构建与标准伴随态框架兼容的伴随源。我们还测试了一种调度混合损失函数，其结合了SeisLM特征损失与传统L₂目标函数。时移诊断表明，预训练编码器生成的特征计算出的损失在正确对齐点周围具有比波形L₂目标函数或相同架构随机初始化编码器得到的特征损失更宽、更平滑的盆地。在Marmousi实验中，SeisLM和混合目标函数在反演早期阶段产生相似的改进，并为后续基于反射的L₂细化提供有用模型。在从横向不变线性梯度模型开始的二维逆冲实验中，SeisLM特征损失工作流优于传统和混合工作流，表明过早引入L₂贡献会重新引入周期跳变敏感性。在三维逆冲实验中，传统L₂反演在初始线性梯度附近停滞，而SeisLM特征损失引导反演走向背景模型，后续L₂细化可从中恢复主要构造。这些结果支持使用预训练地震网络生成的特征定义FWI早期阶段目标函数，而非完全替代波形域失配。
英文摘要：
Full waveform inversion (FWI) can recover high-resolution subsurface velocity models. Conventional waveform-difference objectives, however, are vulnerable to cycle skipping when the starting model is inaccurate. We introduce an FWI objective that compares features produced from modeled and observed seismic traces by SeisLM, a pretrained seismic foundation model. The SeisLM encoder remains frozen during inversion, and the feature discrepancy is differentiated with respect to the modeled traces to construct an adjoint source compatible with the standard adjoint-state framework. We also test a scheduled hybrid loss that combines the SeisLM feature loss with the conventional $L_2$ objective. Time-shift diagnostics show that the loss computed from features produced by the pretrained encoder has a broader and smoother basin around the correct alignment than either the waveform $L_2$ objective or the feature loss obtained from an encoder with the same architecture and randomly initialized parameters. In the Marmousi experiment, the SeisLM and hybrid objectives produce similar improvements during early-stage inversion and provide useful models for subsequent reflection-based $L_2$ refinement. In the 2D Overthrust experiment, which begins from a laterally invariant linear-gradient model, the SeisLM feature-loss workflow outperforms the conventional and hybrid workflows, indicating that introducing the $L_2$ contribution too early can reintroduce cycle-skipping sensitivity. In the 3D Overthrust experiment, conventional $L_2$ inversion stalls near the initial linear gradient, whereas the SeisLM feature loss guides the inversion toward a background model from which $L_2$ refinement recovers the principal structures. These results support using features produced by pretrained seismic networks to define early-stage FWI objectives rather than complete replacements for waveform-domain misfits.
