Φ-GLMAE: global-local masked autoencoder for Φ-OTDR event classification

Abstract Phase-sensitive optical time domain reflectometry (Φ-OTDR) enhances diagnostic capabilities by providing detailed event classification essential for fiber infrastructure management, making it a critical tool in measurement science for real-time monitoring and fault detection in large-scale industrial and environmental systems. Recent deep learning techniques, including convolutional neural networks and long short-term memory networks, have shown promise for this task, though current supervised methods struggle to utilize vast unlabeled data and fail to effectively represent Φ-OTDR signals. To address these issues, we introduce the Φ-masked autoencoder (Φ-MAE) framework, which leverages MAE architecture in two stages: a pre-training phase using unlabeled data to extract robust representations and a fine-tuning phase for classification refinement with labeled data. Extending this, we propose the Φ-global-local MAE (Φ-GLMAE) framework, which integrates local and global patterns for improved feature extraction. Experimental evaluations on the BJTU-OTDR dataset demonstrate that our proposed Φ-GLMAE achieves an accuracy of 99.74 % , outperforming state-of-the-art approaches. In addition, under the 2 % labeled data condition, the proposed method with pre-training achieves 87.36 % accuracy, significantly outperforming the method without pre-training (64.90 % ) by 22.46 % . The source code will be made available at https://github.com/NBT-AILAB/global_local_mae .