TransformDAS: Mapping Φ-OTDR Signals to Riemannian Manifold for Robust Classification

• URL: http://arxiv.org/abs/2502.02428v1
• Date: Tue, 04 Feb 2025 15:53:30 GMT
• Title: TransformDAS: Mapping Φ-OTDR Signals to Riemannian Manifold for Robust Classification
• Authors: Jiaju Kang, Puyu Han, Yang Chun, Xu Wang, Luqi Gong,
• Abstract summary: Phase-sensitive optical time-domain reflectometry (Phi-OTDR) is a widely used distributed fiber optic sensing system in engineering. Machine learning algorithms for Phi-OTDR event classification require high volumes and quality of datasets. One promising approach to address this issue is to augment existing data using generative models combined with a small amount of real-world data.
• Score: 2.845523526282637
• License:
• Abstract: Phase-sensitive optical time-domain reflectometry ({\Phi}-OTDR) is a widely used distributed fiber optic sensing system in engineering. Machine learning algorithms for {\Phi}-OTDR event classification require high volumes and quality of datasets; however, high-quality datasets are currently extremely scarce in the field, leading to a lack of robustness in models, which is manifested by higher false alarm rates in real-world scenarios. One promising approach to address this issue is to augment existing data using generative models combined with a small amount of real-world data. We explored mapping both {\Phi}-OTDR features in a GAN-based generative pipeline and signal features in a Transformer classifier to hyperbolic space to seek more effective model generalization. The results indicate that state-of-the-art models exhibit stronger generalization performance and lower false alarm rates in real-world scenarios when trained on augmented datasets. TransformDAS, in particular, demonstrates the best classification performance, highlighting the benefits of Riemannian manifold mapping in {\Phi}-OTDR data generation and model classification.

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