Probabilistic seismic inverse modeling often requires the prediction of both spatially correlated geological heterogeneities (e.g., facies) and continuous parameters (e.g., rock and elastic properties). Generative adversarial networks (GANs) provide an efficient training-image-based simulation framework capable of reproducing complex geological models with high accuracy and comparably low generative cost. However, their application in stochastic geophysical inversion for multivariate property prediction is limited, as representing multiple coupled properties requires large and unstable networks with high memory and training demands. A more recent variant of GANs with spatially adaptive denormalization (SPADE-GAN) enables the direct conditioning of facies spatial distributions on local probability maps. Leveraging on such features, an iterative geostatistical inversion algorithm is proposed, SPADE-GANInv, integrating a pre-trained SPADE-GAN with geostatistical simulation, for the prediction of facies and multiple correlated continuous properties from seismic data. The SPADE-GAN is trained to reproduce realistic facies geometries, while sequential stochastic co-simulation predicts the spatial variability of the facies-dependent continuous properties. At each iteration, a set of subsurface realizations is generated and used to compute synthetic seismic data. The realizations providing the highest similarity coefficient to the observed data are used to update the subsurface probability models in the next iteration. The method is demonstrated on both 2-D synthetic scenarios and field data, targeting the prediction of facies, porosity, and acoustic impedance from full-stack seismic data. Results show that the algorithm enables accurate multivariate prediction, mitigates the impact of biased prior data, and accommodates additional local conditioning such as well logs.

翻译：概率地震反演建模通常需要同时预测空间相关的地质非均质性（如岩相）和连续参数（如岩石与弹性属性）。生成对抗网络（GANs）提供了一种基于训练图像的高效模拟框架，能够以高精度和相对较低的生成成本复现复杂地质模型。然而，其在多元属性预测的随机地球物理反演中的应用受到限制，因为表示多个耦合属性需要庞大且不稳定的网络，具有较高的内存和训练需求。具有空间自适应去归一化的较新GAN变体（SPADE-GAN）能够将岩相空间分布直接约束于局部概率图。利用此特性，本文提出了一种迭代地质统计反演算法SPADE-GANInv，该算法将预训练的SPADE-GAN与地质统计模拟相结合，用于从地震数据中预测岩相及多个相关的连续属性。SPADE-GAN被训练用于复现真实的岩相几何形态，而序贯随机协同模拟则预测岩相依存的连续属性的空间变异性。在每次迭代中，生成一组地下实现并用于计算合成地震数据。与观测数据相似度系数最高的实现被用于更新下一次迭代中的地下概率模型。该方法在二维合成场景和实际数据上均得到验证，目标是从全叠加地震数据中预测岩相、孔隙度和声波阻抗。结果表明，该算法能够实现精确的多元属性预测，减轻有偏先验数据的影响，并能兼容额外的局部约束条件（如测井数据）。