An Localization and Classification Recognition Technology of DAS Signal based on Classifier Confidence

CAI Chao; QIN Zhihang; LI Lu; GUI Hengli; LIU Hanqing; HE Tao; GAN Yilin; YAN Zhijun; SUN Qizhen

doi:10.13756/j.gtxyj.2025.240119

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An Localization and Classification Recognition Technology of DAS Signal based on Classifier Confidence

更新时间：2025-10-15

- An Localization and Classification Recognition Technology of DAS Signal based on Classifier Confidence
- Study on Optical Communications Issue 5, (2025)
- 作者机构：
  
  1.国网湖北省电力有限公司武汉供电公司，武汉　430074
  2.国网湖北省电力有限公司，武汉　430074
  3.华中科技大学　光学与电子信息学院，武汉　430074
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.240119
  CLC： TP273.5;TP212.9
- Received：11 June 2024，
  
  Revised：2024-08-15，
  
  Published：10 October 2025
- 稿件说明：
移动端阅览
蔡超，秦知航，李璐，等. 基于分类器置信度的DAS信号定位和识别技术[J]. 光通信研究，2025(5): 240119.

Cai C, Qin Z H, Li L, et al. An Localization and Classification Recognition Technology of DAS Signal based on Classifier Confidence[J]. Study on Optical Communications, 2025(5): 240119.
蔡超，秦知航，李璐，等. 基于分类器置信度的DAS信号定位和识别技术[J]. 光通信研究，2025(5): 240119. DOI： 10.13756/j.gtxyj.2025.240119.

Cai C, Qin Z H, Li L, et al. An Localization and Classification Recognition Technology of DAS Signal based on Classifier Confidence[J]. Study on Optical Communications, 2025(5): 240119. DOI： 10.13756/j.gtxyj.2025.240119.

摘要

【目的】

为解决分布式光纤振动传感系统中目标事件模式识别率低和事件源定位准确性弱等问题。

【方法】

文章提出了基于相邻传感单元的分类器置信度分布联合分析方法，利用分布式声波传感（DAS）技术的光纤分布式特点，联立分布式光纤上多个相邻传感单元对外界目标信号数据进行分析，将每个传感单元所探测到的外界振动信号数据送入支持向量机（SVM）分类器进行计算，得到各个传感单元对应的预测事件置信度值。在事件模式识别端，根据各个传感单元对应的预测事件置信度值分布，结合反向传播（BP）神经网络实现目标事件的模式分类；在事件源定位端，提出了一种基于事件置信度空间距离定位算法，根据事件置信度空间距离加权公式对相邻传感单元对应的事件置信度空间分布进行计算，实现外界事件的定位。

【结果】

对文章所提算法进行了相应的实验测试，测试结果表明，该算法对外界4类扰动事件平均识别准确率高达94.85%，且事件源的定位精度高于90%。

【结论】

文章所提技术解决了DAS系统中目标事件类型识别准确率低、事件源精准定位困难的问题。

Abstract

【Objective】

In order to solve the problems of low events recognition rate and weak positioning accuracy in distributed fiber vibration sensor system

a pattern recognition algorithm based on classifier confidence distribution of adjacent sensor nodes is proposed.

【Methods】

According to the spatial distribution feature of Distributed Acoustic Sensing (DAS) technology

the target signals are analyzed by the combination of some adjacent sensor nodes. The external vibration signals received by sensor nodes are transmitted to Support Vector Machine (SVM) classifier for further process. And the probabilities prediction of target events for each sensor node can be calculated by SVM classifier. In the recognition stage

the target events can be identified by the combination of probabilities prediction of target events and Back Propagation (BP) neural network. In the events location stage

the spatial distance localization algorithm based on probabilities prediction distribution of target eventsis proposed. The external event locations are pinpointed through a distance-weighted probability distribution of predicted target events.

【Results】

Finally

the testing of this method is carried out. The experimental results demonstrated that a high average recognition accuracy rate of 94.85% for four disturbance events with a high positioning accuracy of over 90% can be achieved by the proposed scheme.

【Conclusion】

The proposed method solves the problems of low target events classification accuracy and imprecise event source positioning in DAS systems.

关键词

Keywords

references

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张均伟 , 蓝波 , 黄嘉庚 , 等 . 基于分布式光纤传感的光缆防外破监测研究 [J ] . 光通信研究 , 2022 ( 5 ): 53 - 57 .

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张俊楠 , 娄淑琴 , 梁生 . 基于SVM算法的Φ-OTDR分布式光纤扰动传感系统模式识别研究 [J ] . 红外与激光工程 , 2017 , 46 ( 4 ): 422003 .

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钱君霞 , 郭家兴 . 光纤振动信号的特征提取与识别方法综述 [J ] . 光通信研究 , 2024 ( 6 ): 230116 .

Qian J X , Guo J X . Overview of Feature Extraction and Recognition Methods for Fiber Optic Vibration Signals [J ] . Study on Optical Communications , 2024 ( 6 ): 230116 .

Ma P , Liu K , Jiang J , et al . Probabilistic Event Discrimination Algorithm for Fiber Optic Perimeter Security Systems [J ] . Journal of Lightwave Technology , 2018 , 36 ( 11 ): 2069 - 2075 .

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Wu H , Yang M , Yang S , et al . A Novel DAS Signal Recognition Method based on Spatiotemporal Information Extraction with 1DCNNs-BiLSTM Network [J ] . IEEE Access , 2020 , 8 : 119448 - 119457 .

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