LINSCAN -- A Linearity Based Clustering Algorithm
This addresses the problem of identifying complex geological structures like faults for seismologists, representing an incremental improvement over existing clustering methods.
The paper introduces LINSCAN, a clustering algorithm that detects lineated clusters, such as intersecting faults in seismic data, by using normal distributions and Kullback-Leibler Divergence to distinguish spatially close clusters with orthogonal covariances.
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.