基于布里渊效应的海缆埋深监测技术研究（特邀）

吴坤; 吴明埝; 陈青青; 吴正明; 谢书鸿; 王道根; 李松林

doi:10.13756/j.gtxyj.2025.250097

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基于布里渊效应的海缆埋深监测技术研究（特邀）

专题：海洋通信与传感网络 | 更新时间：2025-08-16

- 基于布里渊效应的海缆埋深监测技术研究（特邀）
- Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect
- 光通信研究 2025年第4期
- 作者机构：
  
  1.中天电力光缆有限公司，江苏　南通　226400
  2.江苏中天科技股份有限公司，江苏　南通　226400
- 作者简介：
  
  吴坤，工程师。E-mail：1010330922@qq.com
- 基金信息：
  
  江苏省科技项目产业前瞻与关键核心技术资助项目(BE2022130)
- DOI：10.13756/j.gtxyj.2025.250097
  中图分类号： TN929
- 收稿日期：2025-03-18，
  
  修回日期：2025-04-23，
  
  纸质出版日期：2025-08-10
- 稿件说明：
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吴坤，吴明埝，陈青青，等. 基于布里渊效应的海缆埋深监测技术研究[J]. 光通信研究，2025(4): 250097.

Wu K, Wu M N, Chen Q Q, et al. Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect[J]. Study on Optical Communications, 2025(4): 250097.
吴坤，吴明埝，陈青青，等. 基于布里渊效应的海缆埋深监测技术研究[J]. 光通信研究，2025(4): 250097. DOI： 10.13756/j.gtxyj.2025.250097.

Wu K, Wu M N, Chen Q Q, et al. Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect[J]. Study on Optical Communications, 2025(4): 250097. DOI： 10.13756/j.gtxyj.2025.250097.

摘要

【目的】

传统依赖外部设备离散式测量海缆埋深的模式，存在监测成本高昂、空间覆盖离散和时效性滞后，难以实时获取海缆线路埋深状态的痛点，以及基于热传导方程的物理建模方法受限于海底多相介质耦合传热的难题，文章旨在开发一种基于布里渊光时域分析（BOTDA）的海底电缆埋深分析与计算方法，以实现更高效和便捷的海缆埋深状态监测。

【方法】

文章提出了一种融合BOTDA与反向传播神经网络（BPNN）的智能化监测方法，研究将海底电缆内置通信光纤重构为分布式温度传感器阵列，结合机器学习技术突破传统技术框架。利用BOTDA设备采集24 km海缆的布里渊散射中心频率偏移量数据，经频移-温度/应变耦合方程转换获得全线路温度分布数据并建立标准化数据集，以此构建一个BPNN模型，通过机器学习自动挖掘温度分布特征以实现海缆温度与埋深状态之间的映射关系。该模型以实测温度数据作为输入，通过现场实验和历史数据校准模型参数，经过训练和优化后，输出预测的埋深状态。

【结果】

文章所提BPNN模型能够有效捕捉海底电缆温度变化与埋深之间的非线性关系，获取线路海缆的埋深状态随距离变化的关系，以此来实现海缆埋深状态的预测。

【结论】

研究表明，基于分布式光纤温度应变传感技术和BPNN的埋深测量方法可以实现海底电缆埋深状态的精准监测，其中检测的准确率可达97%。

Abstract

【Objective】

The traditional discrete measurement mode of submarine cable burial depth

which relies on external devices

has the drawbacks of high monitoring costs

discrete spatial coverage

and delayed timeliness. It is difficult to obtain the real-time burial status of the submarine cable route. Additionally

the physical modeling method based on heat conduction equation is limited by the problem of coupled heat transfer in multiphase media under the sea floor. This study aims to develop a submarine cable burial depth analysis and calculation method using Brillouin Optical Time Domain Analysis (BOTDA) to enable efficient and convenient monitoring of burial depth.

【Methods】

The article proposes an intelligent monitoring method that integrates BOTDA with Backpropagation Neural Network (BPNN). It investigates the reconstruction of the communication fibers embedded in submarine cables into a distributed temperature sensor array and leverages machine learning techniques to break through the traditional technological framework. Using BOTDA equipment

the center frequency offset data of Brillouin scattering from a 24 km long submarine cable is collected. This data is then converted into temperature distribution data for the entire cable route through a frequency shift-temperature/strain coupling equation

and a standardized dataset is established. Based on this dataset

a BPNN model is constructed. Through machine learning

the model automatically identifies temperature distribution characteristics to establish a mapping relationship between the temperature of the submarine cable and its burial depth status. The model takes the measured temperature data as input and calibrates the model parameters through field experiments and historical data. After training and optimization

it outputs the predicted burial depth status.

【Results】

The proposed BPNN model can effectively capture the nonlinear relationship between the temperature change and the buried depth of the submarine cable

and obtain the relationship between the buried depth state of the submarine cable with the distance

so as to realize the prediction of the buried depth state of the submarine cable.

【Conclusion】

The results show that the burial depth measurement method based on distributed optical fiber temperature-strain sensing technology and BPNN can achieve precise monitoring of the submarine cable burial depth status

with the detection accuracy reaching 97%.

关键词

Keywords

references

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