用解耦潜空间优化提升地震反演的精度与鲁棒性
Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion
- 分离物理变量与潜变量，分别优化数据拟合与地质先验
- 在OpenFWI等数据集上超越传统方法与现有扩散模型
- 可直接迁移至复杂地质构造，对噪声和初始化不敏感
全波形反演（FWI）通过求解严重不适定、非凸的偏微分方程约束优化问题，从地震记录中恢复地下速度结构。传统正则化方法虽能稳定反演，但难以还原真实地质构造；近期基于扩散模型的方法虽提升现实性，却在数据保真度与先验一致性间存在脆弱权衡。本文提出解耦潜空间优化（DLO），将标准潜空间优化公式重构为对辅助物理变量与潜变量的二次惩罚目标。数据拟合梯度在物理空间中作用，扩散采样仅通过解码后的先验样本贡献，同时保留经典FWI的平滑速度初始化。在OpenFWI基准测试中，DLO在无噪、有噪及缺失道数据条件下均优于传统正则化方法与现有扩散基方法。训练于70×70 OpenFWI模型的先验可直接迁移至Marmousi与Overthrust基准，成功恢复复杂断层结构，且对初始化平滑与测量噪声保持鲁棒。
原文摘要 · Abstract (English)
Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers stabilize the inversion but fail to reproduce realistic geological structures; recent diffusion-prior methods improve realism at the cost of a fragile trade-off between data fidelity and prior consistency. We propose Decoupled Latent Optimization (DLO), which relaxes the standard latent-optimization formulation into a quadratic-penalty objective over an auxiliary physical variable and a latent variable. The data-fidelity gradient acts in physical space, the diffusion sampler contributes only through a decoded prior sample, and the standard smoothed-velocity initialization of classical FWI is preserved. On the OpenFWI benchmark, DLO outperforms classical regularizers and existing diffusion-based methods under clean, noisy, and missing-trace acquisitions. The prior, trained on 70*70 OpenFWI models, transfers directly to the Marmousi and Overthrust benchmarks, where DLO recovers intricate fault structures and remains robust to initialization smoothing and measurement noise.
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