AI 中文总结
该研究开发基于物理知识的时移地震反演框架，针对现有方法无法捕捉储层反常输运特征的问题，引入时空分数扩散数据生成器，训练卷积神经网络，经验证能准确重建异常，扩展了4D地震反演适用性。
AI 中文摘要
我们开发了一个基于物理知识的框架，用于时移（4D）地震反演，以估计与地下流体运移相关的生产引起的速度变化。现有的深度学习反演方法通常在高斯扩散模型生成的地震数据上训练，无法捕捉非均质地下储层的反常输运特征。为克服这一限制，我们引入了基于时空分数扩散的数据生成器，其中时空分数阶独立控制记忆效应和非局部输运，椭圆各向异性表示优先流。通过对该参数空间采样，生成了涵盖正常、亚扩散、超扩散和中间输运状态的广泛物理一致速度扰动集合。然后训练卷积神经网络将时移地震残差直接映射到速度更新。通过广泛的蒙特卡洛验证，表明该网络能准确重建所有输运状态下的生产引起的异常，重建误差主要由异常幅度和界面锐度而非扩散状态本身决定。结果表明，将反常输运物理嵌入训练分布可大幅扩展摊销4D地震反演的适用性，能在现有方法的高斯假设之外稳健恢复重尾和各向异性储层特征。
英文摘要
We develop a physics-informed framework for learned time-lapse (4D) seismic inversion that estimates production-induced velocity changes associated with subsurface fluid migration. Existing learned inversion methods are commonly trained on seismic data generated from Gaussian diffusion models, which fail to capture the anomalous transport characteristic of heterogeneous subsurface reservoirs. To overcome this limitation, we introduce a data generator based on space-time fractional diffusion, in which the temporal and spatial fractional orders independently control memory effects and nonlocal transport, while elliptical anisotropy represents preferential flow. By sampling this parameter space, we generate a broad ensemble of physically consistent velocity perturbations spanning normal, subdiffusive, superdiffusive, and intermediate transport regimes. We then train a convolutional neural network to map time-lapse seismic residuals directly to velocity updates. Through extensive Monte Carlo validation, we show that the network accurately reconstructs production-induced anomalies across all transport regimes, with reconstruction errors governed primarily by anomaly amplitude and interface sharpness rather than by the diffusion regime itself. Our results demonstrate that embedding anomalous-transport physics into the training distribution substantially extends the applicability of amortized 4D seismic inversion, enabling robust recovery of heavy-tailed and anisotropic reservoir signatures beyond the Gaussian assumptions underlying existing approaches.
