We present a new graph-Laplacian based semi-supervised clustering method. This new approach can be viewed as an extension/improvement of previously published work, both in terms of areas of applicability and computational speed. Our clustering method is capable of handling very large datasets with millions of data points using very limited amounts of labelled data. In this work, we apply our clustering method to 3D oil prospectivity, based on amplitude-versus-angle inversion parameters and borehole information. We cluster the synthetic Life of Field dataset, which has a fault-block constrained central oil reservoir, where we also perform a cross-validation check of the predictive power of our method. Furthermore, we cluster a field dataset, which is characterized by a stratigraphic trapped channelling system. In both cases we find appealing results.

我们提出了一种新的基于图-拉普拉斯的半监督聚类方法。在适用性和计算速度方面，这种新方法都可以看作是对先前发表的工作的扩展/改进。我们的聚类方法能够使用数量非常有限的标记数据来处理具有数百万个数据点的大型数据集。在这项工作中，我们基于幅度与角度反演参数和井眼信息将聚类方法应用于3D石油前景。我们对综合的Life of Field数据集进行聚类，该数据集具有受断块约束的中央储油层，在此我们还对方法的预测能力进行了交叉验证检查。此外，我们对一个现场数据集进行了聚类，其特征是地层圈闭的通道系统。