DBSCAN and OPTICS are powerful algorithms for identifying clusters of points in domains where few assumptions can be made about the structure of the data. In this paper, we leverage these strengths and introduce a new algorithm, LINSCAN, designed to seek lineated clusters that are difficult to find and isolate with existing methods. In particular, by embedding points as normal distributions approximating their local neighborhoods and leveraging a distance function derived from the Kullback Leibler Divergence, LINSCAN can detect and distinguish lineated clusters that are spatially close but have orthogonal covariances. We demonstrate how LINSCAN can be applied to seismic data to identify active faults, including intersecting faults, and determine their orientation. Finally, we discuss the properties a generalization of DBSCAN and OPTICS must have in order to retain the stability benefits of these algorithms.

翻译：DBSCAN与OPTICS是在对数据结构假设极少的情况下识别点簇的强大算法。本文基于这些算法的优势，提出一种新算法LINSCAN，旨在发现并分离现有方法难以捕捉的线状聚类簇。具体而言，通过将数据点嵌入为近似其局部邻域的正态分布，并利用基于Kullback-Leibler散度推导的距离函数，LINSCAN能够检测并区分空间邻近但协方差方向正交的线状聚类簇。我们展示了如何将LINSCAN应用于地震数据以识别活动断层（包括交叉断层）并确定其走向。最后，我们探讨了DBSCAN与OPTICS的泛化算法为保持其稳定性优势所必须具备的性质。