Simulating a depositional (or stratigraphic) sequence conditionally on borehole data is a long-standing problem in hydrogeology and in petroleum geostatistics. This paper presents a new rule-based approach for simulating depositional sequences of surfaces conditionally on lithofacies thickness data. The thickness of each layer is modeled by a transformed latent Gaussian random field allowing for null thickness thanks to a truncation process. Layers are sequentially stacked above each other following the regional stratigraphic sequence. By choosing adequately the variograms of these random fields, the simulated surfaces separating two layers can be continuous and smooth. Borehole information is often incomplete in the sense that it does not provide direct information about the exact layer that some observed thickness belongs to. The latent Gaussian model proposed in this paper offers a natural solution to this problem by means of a Bayesian setting with a Markov chain Monte Carlo (MCMC) algorithm that can explore all possible configurations that are compatible with the data. The model and the associated MCMC algorithm are validated on synthetic data and then applied to a subsoil in the Venetian Plain with a moderately dense network of cored boreholes.

在井眼数据上有条件地模拟沉积（或地层）层序是水文地质学和石油地质统计学中的一个长期存在的问题。本文提出了一种新的基于规则的方法，用于基于岩相厚度数据有条件地模拟表面的沉积序列。每层的厚度都由经过变换的潜在高斯随机场建模，这得益于截断过程，从而使厚度为零。层按照区域地层顺序依次堆叠。通过适当地选择这些随机字段的变差函数，模拟的表面分离两个层可以是连续的和平滑的。从不提供有关某些观测厚度所属的确切层的直接信息的意义上来说，钻孔信息通常是不完整的。本文提出的潜在高斯模型通过使用马尔可夫链蒙特卡罗（MCMC）算法的贝叶斯设置为该问题提供了自然的解决方案，该算法可以探索与数据兼容的所有可能的配置。该模型和相关的MCMC算法在合成数据上进行了验证，然后应用于带有中等密度的带芯钻孔网络的威尼斯平原地基。