数学学科Seminar第3028讲 面向全波形反演的鲁棒物理引导扩散方法

创建时间:  2026/04/24  邵奋芬   浏览次数:   返回

报告题目 (Title):Robust Physics-Guided Diffusion for Full-Waveform Inversion

(面向全波形反演的鲁棒物理引导扩散方法)

报告人 (Speaker):燕雄斌(兰州大学)

报告时间 (Time):2026年4月25日(周六)13:30-14:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):纪丽洁

主办部门:理学院数学系

摘要:We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normalization, thereby improving robustness to amplitude imbalance and time/phase misalignment. On the inference side, we introduce a preconditioned guided reverse-diffusion scheme that adapts the guidance strength and spatial scaling throughout the reverse-time dynamics, yielding a more stable and effective data-consistency guidance step than standard diffusion posterior sampling (DPS). Numerical experiments on OpenFWI datasets demonstrate improved reconstruction quality over deterministic optimization baselines and standard DPS under comparable computational budgets.

上一条:数学学科Seminar第3029讲 面向流固耦合问题组件式模型降阶的高阶隐式龙格-库塔时间积分方法

下一条:数学学科Seminar第3027讲 带有随机输入的PDEs的高效数值方法


数学学科Seminar第3028讲 面向全波形反演的鲁棒物理引导扩散方法

创建时间:  2026/04/24  邵奋芬   浏览次数:   返回

报告题目 (Title):Robust Physics-Guided Diffusion for Full-Waveform Inversion

(面向全波形反演的鲁棒物理引导扩散方法)

报告人 (Speaker):燕雄斌(兰州大学)

报告时间 (Time):2026年4月25日(周六)13:30-14:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):纪丽洁

主办部门:理学院数学系

摘要:We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normalization, thereby improving robustness to amplitude imbalance and time/phase misalignment. On the inference side, we introduce a preconditioned guided reverse-diffusion scheme that adapts the guidance strength and spatial scaling throughout the reverse-time dynamics, yielding a more stable and effective data-consistency guidance step than standard diffusion posterior sampling (DPS). Numerical experiments on OpenFWI datasets demonstrate improved reconstruction quality over deterministic optimization baselines and standard DPS under comparable computational budgets.

上一条:数学学科Seminar第3029讲 面向流固耦合问题组件式模型降阶的高阶隐式龙格-库塔时间积分方法

下一条:数学学科Seminar第3027讲 带有随机输入的PDEs的高效数值方法