Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing

TL;DR

This paper introduces a DAS-based anomaly detection framework for submarine cables, effectively identifying exposure-length variations with limited training data through a regression and SVM approach.

Contribution

It presents a novel regression-based feature extraction method combined with one-class SVM for reliable anomaly detection in submarine cables using DAS.

Findings

Anomaly scores decrease monotonically with exposure-length increase (r = -0.83).

Binary classification achieved an F1 score of 0.82 with small training datasets.

The framework effectively detects exposure-length variations under severe data limitations.

Abstract

This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fiber-optic sensing technologies. To address environmental variability and limited training data in offshore environments, a regression-based feature extraction method was introduced to derive low-dimensional latent representations that retain exposure length-dependent vibration characteristics while suppressing environmental influences. The extracted features were used for one-class Support Vector Machine (SVM)-based anomaly detection. The proposed framework was evaluated through wave-tank experiments with exposure lengths ranging from 2 to 10 m. Experimental results showed that anomaly scores decreased approximately monotonically with increasing exposure-length change, exhibiting a strong correlation ($r = -0.83$). The binary classification achieved an F1 score of 0.82 despite training with only small-sample datasets. These findings demonstrate that exposure-length variations can be reliably detected under severe data limitations, supporting the potential of DAS-based cable condition monitoring.