When building 3D models of the subsurface, reconciling several geological and geophysical data of diverse nature, resolutions, coverage, or sensitivity, is challenging, both numerically and petrophysically. In this work, we propose a workflow for mapping selected geological features and characterise their uncertainty using a Bayesian Estimate Fusion algorithm. Different datasets such as 1D probabilistic models derived from geophysical data, drillholes and geological data are combined to produce probabilistic maps of selected geological boundaries, relying on petrophysical and geological assumptions. Leveraging large, high-quality geophysical datasets acquired in the eastern Gawler Craton in South Australia, we demonstrate the applicability of our approach with two examples: (1) we map in 3D the top of a stratigraphic unit in the cover, the Tregolana Shale, using 1D magnetotelluric (MT) and 1D Airborne Electromagnetic (AEM) probabilistic models, drill holes and surface geology; (2) we map the depth to basement using 1D probabilistic MT models, drill holes and interpreted structural information. Our results show that the different resolution, data sampling, depth of investigation and reliability of the utilised datasets can be combined in a complementary fashion, overcoming their respective limitations, to find solutions/models that satisfy all the datasets. We show that probabilistic workflows permit characterisation and reduce uncertainty when mapping the location of features of interest, but also permit the testing of geological hypotheses against other geophysical and geological data. These types of models are valuable to better characterise, interpret, and conceptualise the subsurface, enabling better exploration targeting and supporting efforts to discover new mineral deposits.

在构建地下 3D 模型时，协调不同性质、分辨率、覆盖范围或灵敏度的多个地质和地球物理数据在数值和岩石物理方面都具有挑战性。在这项工作中，我们提出了一种工作流程，用于绘制选定的地质特征并使用贝叶斯估计融合算法来表征其不确定性。根据岩石物理和地质假设，将不同的数据集（例如源自地球物理数据、钻孔和地质数据的一维概率模型）组合起来，生成选定地质边界的概率图。利用在南澳大利亚高勒克拉通东部获取的大型、高质量地球物理数据集，我们通过两个示例证明了我们的方法的适用性：(1) 我们以 3D 方式绘制了覆盖层中地层单元的顶部，Tregolana 页岩，使用一维大地电磁 (MT) 和一维机载电磁 (AEM) 概率模型、钻孔和表面地质；(2) 我们使用一维概率 MT 模型、钻孔和解释的结构信息来绘制地下室的深度。我们的结果表明，所用数据集的不同分辨率、数据采样、调查深度和可靠性可以以互补的方式组合，克服各自的局限性，找到满足所有数据集的解决方案/模型。我们表明，概率工作流程允许在绘制感兴趣特征的位置时进行表征并减少不确定性，而且还允许根据其他地球物理和地质数据测试地质假设。这些类型的模型对于更好地描述、解释、