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.

翻译：全波形反演通过求解严重病态的非凸PDE约束优化问题，从地震记录中恢复地下速度场。经典正则化方法虽能稳定反演过程，但无法重现逼真的地质结构；近期基于扩散先验的方法虽提升了复原真实感，却以牺牲数据保真度与先验一致性之间的脆弱平衡为代价。本文提出解耦潜在优化方法，将标准潜在优化形式松弛为辅助物理变量与潜变量上的二次罚目标。数据保真度梯度作用于物理空间，扩散采样器仅通过解码先验样本贡献信息，同时保留了经典全波形反演的标准平滑速度初始化方案。在OpenFWI基准测试中，DLO在干净、含噪及缺失道集三种采集条件下均优于经典正则化方法与现有扩散方法。该先验模型在70×70 OpenFWI模型上训练后可直接迁移至Marmousi和Overthrust基准测试，不仅成功恢复复杂断层构造，且对初始平滑化及测量噪声具有稳健性。