Geological sampling data often need to be regionally segmented to mark blocks with different geological properties in mathematical geosciences. This paper uses the directed graph model of graph theory to model sampling data and calculate the segmentation of the directed graph by the improved maximum flow minimum cut (max-flow min-cut) algorithm. The proposed energy expressions reflect the continuity of geological properties and the smoothness of segmentation boundaries. A major advantage of this method is that it allows geological experts to share prior knowledge regarding the partitioning of a mineralization area as the seeds of the directed graph. Incorporating this knowledge in the segmentation result via a very intuitive and simple outline operation greatly improves the interactivity and editability of the algorithm. For the integrity of the process framework, this paper also proposes incorporating the characteristic scale of the Hilbert–Huang transformation (HHT) framework as the characteristic parameter to calculate the initial seed, which can achieve automatic regional segmentation.

地质采样数据通常需要在区域上进行分割，以标记数学地球科学中具有不同地质属性的块。本文使用图论的有向图模型对采样数据进行建模，并通过改进的最大流最小割（max-flow min-cut）算法计算有向图的分割。所提出的能量表达式反映了地质性质的连续性和分段边界的平滑性。此方法的主要优点是，它使地质专家可以共享有关将成矿区域划分为有向图的种子的先验知识。通过非常直观和简单的轮廓操作将这些知识结合到分割结果中，可以大大提高算法的交互性和可编辑性。