Title: SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty Show Chinese title

Title: SURGIN：基于代理的地下多相流生成反演及量化不确定性

Abstract: We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation capability, enabling real-time assimilation of unseen monitoring data without task-specific retraining. Specifically, SURGIN synergistically integrates a U-Net enhanced Fourier Neural Operator (U-FNO) surrogate with a score-based generative model (SGM), framing the conditional generation as a surrogate prediction-guidance process in a Bayesian perspective. Instead of directly learning the conditional generation of geological parameters, an unconditional SGM is first pretrained in a self-supervised manner to capture the geological prior, after which posterior sampling is performed by leveraging a differentiable U-FNO surrogate to enable efficient forward evaluations conditioned on unseen observations. Extensive numerical experiments demonstrate SURGIN's capability to decently infer heterogeneous geological fields and predict spatiotemporal flow dynamics with quantified uncertainty across diverse measurement settings. By unifying generative learning with surrogate-guided Bayesian inference, SURGIN establishes a new paradigm for inverse modeling and uncertainty quantification in parametric functional spaces. Show Chinese abstract

Abstract: 我们提出了一种直接反演建模方法，名为SURGIN，这是一种针对地下多相流数据同化的SURrogate引导生成反演框架。与现有反演方法需要为每种新的观测配置进行适应不同，SURGIN具有零样本条件生成能力，能够在不进行任务特定再训练的情况下实现实时同化未见过的监测数据。具体而言，SURGIN协同集成一个增强的U-Net傅里叶神经算子（U-FNO）代理模型与基于评分的生成模型（SGM），从贝叶斯视角将条件生成框架化为代理预测引导过程。而不是直接学习地质参数的条件生成，首先以自监督方式预训练一个无条件SGM以捕捉地质先验，随后通过利用可微分的U-FNO代理模型进行后验采样，以实现基于未见过的观测的高效前向评估。大量的数值实验表明，SURGIN能够很好地推断出异质地质场，并在各种测量设置下预测具有量化不确定性的时空流动动力学。通过将生成学习与代理引导的贝叶斯推理统一起来，SURGIN为参数函数空间中的反演建模和不确定性量化建立了一个新范式。