The article presents an algorithm utilizing optical fibers and Distributed Acoustic Sensing (DAS) technology for monitoring anchor-induced damage events in submarine cables. The algorithm enables feature-threshold-based identification of anchor hazards in data-scarce scenarios, circumventing the high costs associated with anchor damage data acquisition while enhancing the algorithm's broad applicability.

Based on the characteristic attributes of anchor damage events (high intensity, long-range impact, and prolonged duration), the algorithm processes two-dimensional spatiotemporal vibration data obtained through DAS measurements of submarine cable fibers. It first extracts event data profiles using average power feature masking, then calculates contour area and anchor damage confidence to filter profiles. Subsequently, morphological feature curves are computed for profiles at equally spaced time intervals, enabling identification of anchor intrusion events through intensity and temporal distribution analysis of these curves.

Simulation experiments demonstrate significant differences in morphological feature curves between normal interference and simulated anchor damage events. The combined computation of multiple feature curves effectively distinguishes profile defects in event contours. By setting morphological feature thresholds, anchor damage events can be rapidly identified. In the tests using one anchor damage dataset alongside two non-damage datasets, the algorithm effectively distinguished the anchor damage data by employing filtering techniques based on the intensity and duration of feature curves, as well as by detecting profile defects in these curves.

The submarine cable anchor damage monitoring algorithm proposed in this article primarily utilizes the OpenCV computer vision framework for its implementation. It features fast computation speed and high accuracy, meeting the real-time monitoring requirements for submarine cable anchor damage detection. The morphological feature curves used in the algorithm effectively represent the temporal distribution of event contours. As the core feature of the anchor damage algorithm, they expand the application scenarios of morphological features and hold significant practical value.