Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect
【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. A...
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| Format: | Article |
| Language: | zho |
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《光通信研究》编辑部
2025-08-01
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| Series: | Guangtongxin yanjiu |
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| Online Access: | http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.250097/ |
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| author | WU Kun WU Mingnian CHEN Qingqing WU Zhengming XIE Shuhong WANG Daogen LI Songlin |
| author_facet | WU Kun WU Mingnian CHEN Qingqing WU Zhengming XIE Shuhong WANG Daogen LI Songlin |
| author_sort | WU Kun |
| collection | DOAJ |
| description | 【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%. |
| format | Article |
| id | doaj-art-2707ca4f641e4c618069c195d2ac0715 |
| institution | DOAJ |
| issn | 1005-8788 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | 《光通信研究》编辑部 |
| record_format | Article |
| series | Guangtongxin yanjiu |
| spelling | doaj-art-2707ca4f641e4c618069c195d2ac07152025-08-20T03:06:13Zzho《光通信研究》编辑部Guangtongxin yanjiu1005-87882025-08-01250097-07122986657Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin EffectWU KunWU MingnianCHEN QingqingWU ZhengmingXIE ShuhongWANG DaogenLI Songlin【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%.http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.250097/distributed fiber optic temperature strain sensingBOTDAsubmarine cable buried depthnonlinear relationBPNN |
| spellingShingle | WU Kun WU Mingnian CHEN Qingqing WU Zhengming XIE Shuhong WANG Daogen LI Songlin Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect Guangtongxin yanjiu distributed fiber optic temperature strain sensing BOTDA submarine cable buried depth nonlinear relation BPNN |
| title | Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect |
| title_full | Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect |
| title_fullStr | Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect |
| title_full_unstemmed | Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect |
| title_short | Research on Submarine Cable Buried Depth Monitoring Technology based on Brillouin Effect |
| title_sort | research on submarine cable buried depth monitoring technology based on brillouin effect |
| topic | distributed fiber optic temperature strain sensing BOTDA submarine cable buried depth nonlinear relation BPNN |
| url | http://www.gtxyj.com.cn/zh/article/doi/10.13756/j.gtxyj.2025.250097/ |
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