Optical fiber distributed acoustic sensing signal identification method, system, device and medium

By combining transfer learning and semi-supervised learning methods, and utilizing the ResNet-34 network and the improved FixMatch algorithm, the problems of transferability and unlabeled sample utilization of fiber optic distributed acoustic sensors in cross-scene recognition were solved, achieving higher recognition accuracy and faster model adaptation.

CN117636871BActive Publication Date: 2026-07-21UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Filing Date
2023-12-06
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing fiber optic distributed acoustic sensor (DAS) signal recognition models lack transferability when recognizing across scenes, and semi-supervised learning methods fail to make full use of unlabeled samples, resulting in poor recognition performance.

Method used

By combining transfer learning and semi-supervised learning, a signal recognition model is constructed using the ResNet-34 network. Knowledge distillation and adaptive knowledge consistency (AKC) are used for transfer learning. The feature distribution constraints of labeled and unlabeled samples are utilized, and the improved FixMatch algorithm is combined for semi-supervised training to enhance the model's adaptability in cross-scene recognition.

Benefits of technology

It improves the accuracy of cross-scene recognition and the ability to quickly deploy models, and can effectively utilize source domain knowledge and labeled or unlabeled samples in the target domain to achieve higher recognition accuracy and faster model convergence.

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Abstract

The application discloses a kind of optical fiber distributed acoustic sensing signal identification method, system, equipment and medium, its purpose is to improve the limitation of transfer learning and semi-supervised learning method, solve the problem of DAS cross-scene identification.It includes: constructing DAS signal identification network model, signal identification network model uses ResNet-34 network;Signal identification network model is pre-trained using source domain ImageNet image dataset training, then the two-dimensional time-frequency diagram dataset in DAS some typical scene is used to the signal identification network model training adjustment, obtain source model;On the basis of source model, the two-dimensional time-frequency diagram dataset corresponding to other application scenarios of DAS is used to the signal identification network model transfer learning and semi-supervised training, to obtain the final identification model.
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