An optimal strategy for building realistic geological models must combine different geophysical techniques, each with its advantages and limitations. However, dealing with multiple geophysical datasets to derive a geological interpretation is not straightforward since geophysical parameters are not always functionally related. In this work, we propose an innovative approach consisting of using machine learning techniques to jointly interpret three geophysical datasets (a pseudo-3D resistivity model, a 3D velocity model, and 4 well-logs). These datasets, among others, were acquired to characterize the suitability of an evaporitic sequence for hosting a temporary storage facility of hazardous radioactive waste, which was planned in Villar de Cañas (Spain). Our strategy consisted of integrating both models in a single 3D bi-parametric grid that nested the velocity and resistivity values in each node. We then used a supervised learning algorithm to lithologically classify each node according to a training set based on the borehole data, which acts as ground truth. The training set is composed of classifiers that lithologically label resistivity-velocity pairs. However, the very shallow nodes lack classifiers due to the poor well-log coverage at the top part of the evaporitic sequence. To fill this gap, we computed an unsupervised cluster analysis that provided new classes to complete the training set. Finally, the supervised classification was applied, providing a new 3D lithology model that is far more consistent with the geology than the models derived from each parameter independently. The 3D model also revealed geological features previously unknown, notably the existence of an inactive fault. The proposed method can be applied to integrate and jointly interpret any kind of multidisciplinary datasets in a wide range of geoscientific problems, including natural resource exploration, geological storage, environmental monitoring, civil engineering practice, and hazard assessment.

建立现实地质模型的最佳策略必须结合不同的地球物理技术，每种技术都有其优势和局限性。然而，处理多个地球物理数据集以得出地质解释并不是一件容易的事，因为地球物理参数并不总是与功能相关。在这项工作中，我们提出了一种创新的方法，该方法包括使用机器学习技术来共同解释三个地球物理数据集（伪3D电阻率模型，3D速度模型和4个测井曲线）。采集了这些数据集，以表征蒸发序列适合容纳危险放射性废物临时存储设施的计划，该计划已在西班牙比利亚尔·德·卡尼亚斯进行了规划。我们的策略包括将两个模型集成在单个3D双参数网格中，该网格在每个节点中嵌套了速度和电阻率值。然后，我们使用监督学习算法，根据基于井眼数据的训练集对每个节点进行岩性分类，这是地面真实性。训练集由用岩性标记电阻率-速度对的分类器组成。但是，由于蒸发序列顶部的测井覆盖率差，非常浅的节点缺乏分类器。为了填补这一空白，我们计算了一种无监督的聚类分析，该分析提供了新的课程来完成训练集。最后，采用监督分类，提供了一个新的3D岩性模型，与独立于每个参数的模型相比，该3D岩性模型与地质条件更加一致。3D模型还揭示了以前未知的地质特征，特别是存在非活动断层。所提出的方法可用于整合和联合解释广泛的地球科学问题中的任何种类的多学科数据集，包括自然资源勘探，地质存储，环境监测，土木工程实践和危害评估。