A method and system for monitoring the depth of shallow buried submarine cables

Abstract

The present invention provides a method and system for monitoring the depth of shallow submarine cables. This method models the relationship between seawater temperature and depth based on initial conditions, automatically adjusts ambient temperature parameters based on seasonal variations in the operating area, and modifies the temperature-depth relationship model. By sensing relative depth information based on changes in ambient temperature, the system replaces monitoring absolute depth values. This method effectively reduces the implementation difficulty of submarine cable operation and maintenance systems, reduces system computing resource usage, and improves the performance of shallow submarine cable depth monitoring systems.

Description

Technical Field

[0001] The present invention relates to a bottom cable operation and maintenance system, and in particular to a shallow buried submarine cable burial depth monitoring system and a monitoring method based on distributed optical fibers. Background Art

[0002] With the rapid development of offshore projects and submarine engineering, submarine operations are becoming increasingly difficult and costly, and the risks of diving are significant. Consequently, a shore-based monitoring system for submarine cable operations and maintenance is urgently needed. This system allows personnel to monitor the safety of underwater equipment and ensure the normal operation of submarine projects. Currently, the characteristic data available from submarine cables include vibration, stress, attenuation, and temperature. However, algorithms for directly estimating cable depth using these sensor data are immature and require high computing resources, posing significant challenges to the construction of shore-based monitoring systems and the effectiveness of cable depth monitoring. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for monitoring the buried depth of submarine cables. This method can estimate the buried depth of submarine cables by combining measured subsurface temperature data with a sea area ambient temperature model. This method can effectively reduce the difficulty of implementing a submarine cable operation and maintenance system and improve the performance of submarine cable buried depth monitoring. To this end, the technical solution adopted by the present invention is:

[0004] A method for monitoring the burial depth of shallow submarine cables is characterized by modeling a temperature-depth relationship model based on initial conditions, automatically adjusting the ambient temperature parameter factor according to seasonal differences in the application sea area, and correcting the temperature-depth relationship model. The temperature-depth relationship model is a relationship model between temperature and submarine cable burial depth; by sensing the relative value of the burial depth information through changes in the submarine cable working environment temperature, instead of monitoring the absolute value of the burial depth, the influence of errors introduced into the modeling process can be effectively weakened.

[0005] The initial model of temperature-depth relationship is as shown in equation (1):

[0006] θ t -k 2 θ Δz =0

[0007] θ(0,t)=θ y e iωt (1)

[0008] Where θ t is the temperature of seawater at time t, θ Δz is the seawater temperature at different depths z, k is the thermal diffusivity, ω is the circular frequency of mud temperature cycle, θ y is the annual mud surface temperature variation, θ(0,t) is the sea level temperature at time t;

[0009] The initial model equation of the temperature-depth relationship is a semi-infinite heat conduction equation without initial conditions. The solution of equation (1) is obtained using the separation of variables method:

[0010]

[0011] Formula (2) is the relationship model between the annual variation amplitude of mud temperature and depth and time, where z is the depth, z = 0 at the mud surface; t is time (s); θ y is the annual mud surface temperature variation, that is, the difference between the maximum and minimum mud surface temperature in a year; k is the thermal diffusivity (cm 2 s -1 ); is the circular frequency of annual variation of mud temperature;

[0012] The relationship model between the annual variation amplitude of mud temperature and depth and time shows that the key to studying the vertical distribution law of mud temperature in a specific area is to find the annual variation amplitude of mud surface temperature θ y , and thermal diffusivity k; the present invention uses the thermal diffusivity k value as a reference value for monitoring the buried depth of the submarine cable.

[0013] The mud temperature at point z below the seafloor is expressed as:

[0014]

[0015] Where T0 is the annual average temperature at a certain depth in the area;

[0016] According to formula (3), the trend of mud temperature changing with time is as follows: Figure 1 As shown in formula (3), it can be seen that the simulated mud temperature fluctuates within the annual cycle, which conforms to the cosine variation rule. Figure 2 The observed results of mud temperature changes over time at different depths in a certain area of ​​Bohai Sea are approximately consistent. It can be considered that the deep temperature simulation results are consistent with the observation results in Bohai Sea area, indicating that the deep temperature model is suitable for the Bohai Sea area.

[0017] The annual variation amplitude of mud temperature at different depths is:

[0018]

[0019] According to formula (4), the annual variation amplitude of mud temperature is simulated, and the annual variation amplitude of mud surface temperature is taken as θ y =10(℃), depth z=0~10m, thermal diffusivity k is taken from the online data of Bohai Sea survey data k=0.0019(cm 2 s -1 The annual variation curve of mud temperature with burial depth is as follows: Figure 3 shown.

[0020] The diffusion coefficient k is expressed as:

[0021]

[0022] From the above formula, we can see that as long as we know and measure the mud temperature change cycle (here we only consider the annual cycle of mud temperature) and the corresponding mud temperature values ​​at two different depths, we can calculate the thermal diffusivity at different depths according to the diffusion rate k equation.

[0023] According to the survey results, the k value calculation results show that the k value changes differently at different depths.

[0024] The 0-0.5m layer is larger, and the layer below 0.5m is smaller, and the difference is not big. This is related to the inaccurate measurement of the water-mud interface temperature (it is difficult to place the temperature sensor exactly on the water-mud interface).

[0025] When calculating the k value, although the k value is related to the depth factor, the k value below the depth of 2m is approximately taken as the k value of the 1~2m layer.

[0026] The shallow buried submarine cable depth monitoring system is divided into three main subsystems: data source, background service and real-time application. The overall framework of the shallow buried submarine cable depth monitoring system is as follows: Figure 4 shown.

[0027] Initial data is first transmitted to the shallow submarine cable depth monitoring system through a data source system. This data source primarily includes meteorological data, socket data streams, and a field-measurement database. Meteorological data provides real-time temperature information for the sea area. This data is periodically uploaded to the shallow submarine cable depth monitoring system to update the parameters of the temperature-depth relationship model. The socket data stream uploads sensor data in real time. The shallow submarine cable depth monitoring system's temperature-depth model data center uses this sensor-uploaded temperature data to build the model. The field-measurement database stores initial equipment deployment information, including the optical cable placement GPS, initial cable depth, and initial mud temperature. This provides initial information for the depth model data center and serves as a reference for optimizing the temperature-depth relationship model.

[0028] Then the data flow enters the background service system, which includes three functional modules: data access module, data real-time verification processing module and temperature depth relationship model data center.

[0029] The data access module is responsible for unifying various types of raw data formats into a common system data format and uploading the data to the real-time verification processing module. Raw data includes meteorological data, sensor data, and field measurement databases.

[0030] The real-time data verification processing module is responsible for real-time data collection from data sources, and performs data classification and data preprocessing. The data formats collected in real time include files, databases, network data streams, etc.

[0031] The Temperature-Depth Model Data Center is responsible for modeling the temperature-depth relationship and monitoring depth anomalies. It models the received distributed data streams, monitors real-time parameters to predict and optimize the model, and uses early warning algorithms to assess depth data. If the depth at a specific location decreases continuously over a period of time, a safety warning will be issued, indicating a potential risk of fiber optic cable leakage. If the depth changes significantly over a short period of time, an abnormality alarm will be issued, requiring engineering personnel to conduct equipment status inspections to identify destructive operations or equipment failures.

[0032] Finally, the temperature-depth model data center calculation results are fed into the real-time application system. This system monitors and controls submarine cable depth information, providing a monitoring platform for shore-based personnel. The real-time application system consists of two components: a backend detection system and an online monitoring system. The backend monitoring system primarily implements model prediction and optimization functions, as well as data analysis results, providing professional R&D personnel with system status monitoring and data analysis. The online monitoring system provides a real-time visualization platform for submarine cable depth status, providing equipment maintenance personnel with monitoring images, equipment status, and alarm and warning information.

[0033] The present invention can effectively reduce the difficulty of implementing a submarine cable operation and maintenance system, reduce the system's computing resource usage, and improve the performance of a shallow-buried submarine cable depth monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the change of mud temperature over time for the shallow buried submarine cable according to the present invention.

[0035] Figure 2 This is a schematic diagram of the observation results of the mud temperature at different depths in the Bohai Sea area changing with time according to the present invention.

[0036] Figure 3 This is a schematic diagram of the annual variation of mud temperature of the shallow buried submarine cable according to the present invention.

[0037] Figure 4 It is a schematic diagram of the overall framework of the shallow buried submarine cable depth monitoring system of the present invention. DETAILED DESCRIPTION

[0038] The core technology of the shallow submarine cable depth monitoring system is temperature-depth modeling (temperature-depth relationship model). The process of establishing the temperature-depth relationship model is affected by two factors: the depth z of the subsurface and the change in the regional ambient temperature θ. Therefore, the shallow submarine cable depth monitoring system is based on the subsurface depth and the ambient temperature as variables to establish the model.

[0039] 1. Establishment of deep temperature model under mud

[0040] Mud temperature changes mainly depend on changes in near-bottom seawater temperature, and the periodic changes in seawater temperature depend on changes in solar radiation (periodic heat source).

[0041] Because Earth's surface temperature varies daily, annually, and through multi-year cycles, seawater and mud temperatures also experience corresponding cyclical variations. Strictly speaking, mud temperature is influenced not only by seawater temperature but also by geothermal heat from the Earth's interior. However, this influence is minimal and is generally ignored.

[0042] Experience and field observations show that among the several periodic changes in mud temperature mentioned above, the annual temperature change is the most significant, that is, the change characteristics of mud temperature with a one-year cycle are the most significant. Therefore, the seabed mud temperature changes adopted in the temperature-depth relationship model of the present invention only consider its annual change cycle.

[0043] Because the Earth's surface temperature has not changed much over thousands of years, there is good reason not to consider its initial conditions when studying changes in mud temperature.

[0044] Considering the seabed as a plane and the depth of the earth as semi-infinite, to simplify the problem, the mud temperature change can be approximately regarded as a heat conduction problem with harmonic changes (the main frequency is annual changes). The initial model is shown in the following equation

[0045] θ t -k 2 θ zz =0

[0046] θ(0,t)=θ y e iωt (1)

[0047] Where k is the thermal diffusivity, θ y is the annual mud surface temperature variation.

[0048] The initial model equation is a semi-infinite heat conduction equation with no initial conditions. The solution of the above equation is obtained using the separation of variables method:

[0049]

[0050] The above formula is the relationship model between the annual variation amplitude of mud temperature and depth and time, where z is the depth, z=0 at the mud surface; t is the time (s); θ y is the annual mud surface temperature variation, that is, the difference between the maximum and minimum mud surface temperature in a year; k is the thermal diffusivity (cm 2 s -1 ); is the annual circular frequency of mud temperature variation.

[0051] The relationship model between the annual variation amplitude of mud temperature and depth and time shows that the key to studying the vertical distribution law of mud temperature in a specific area is to find the annual variation amplitude of mud surface temperature θ y , and thermal diffusivity k.

[0052] The mud temperature at point z below the seabed can be expressed as:

[0053]

[0054] Where T0 is the annual average temperature at a certain depth in the area.

[0055] The annual variation amplitude of mud temperature at different depths is:

[0056]

[0057] 2 Calculation of key factors of deep temperature model

[0058] From the relationship model of the annual variation amplitude of mud temperature with depth and time, it can be seen that the key to studying the vertical distribution law of mud temperature in a specific area is to find the annual variation amplitude of mud surface temperature θ y , and thermal diffusivity k.

[0059] Annual soil surface temperature variation θ in a certain area y Can be obtained by observation.

[0060] The thermal diffusivity k is calculated indirectly through the measured mud temperature data.

[0061] Based on the relationship model between the annual variation of mud temperature amplitude, depth and time, it is found that k can be expressed as a function of the harmonic temperature fluctuation amplitudes θ1 and θ2, with its variation period being τ. For two different depths z1 and z2 (both measured from the seabed), the diffusion rate k can be expressed as

[0062]

[0063] It can be seen from the above formula that as long as the mud temperature change cycle (here only the annual cycle of mud temperature is considered) and the corresponding mud temperature values ​​at two different depths are known and measured, the thermal diffusivity at different depths can be calculated according to the diffusion rate k equation.

[0064] According to the survey results, the k value calculation results show that the k value changes differently at different depths.

[0065] The 0-0.5m layer is larger, and the layer below 0.5m is smaller, and the difference is not big. This is related to the inaccurate measurement of the water-mud interface temperature (it is difficult to place the temperature sensor exactly on the water-mud interface).

[0066] Therefore, when calculating the k value, although the k value is related to the depth factor, the k value below the depth of 2m is approximately taken as the k value of the 1~2m layer.

[0067] The above embodiment is only a preferred technical solution of the present invention. Those skilled in the art should understand that the technical solutions or parameters in the embodiment can be modified or replaced without departing from the principle and essence of the present invention, and all should be covered by the protection scope of the present invention.

Claims

1. A method for monitoring the depth of shallow buried submarine cables, characterized in that The relationship between seabed temperature and depth is modeled based on initial conditions, and the ambient temperature parameter factor is automatically adjusted according to seasonal differences in the application sea area to revise the temperature-depth relationship model. The relative value of the burial depth information is perceived through changes in the working environment temperature, replacing the monitoring of the absolute value of the burial depth. The initial model of the relationship between seafloor temperature and depth is as follows: i t -k 2 i Δz =0 θ(0,t)=θ y e iωt (1) Where θ t is the temperature of seawater at time t, θ Δz is the seawater temperature at different depths z, k is the thermal diffusivity, ω is the circular frequency of mud temperature cycle, θ y is the annual mud surface temperature variation, θ(0,t) is the sea level temperature at time t; The initial model equation of seabed temperature depth is a semi-infinite heat conduction equation without initial conditions. The solution of equation (1) is obtained using the separation of variables method: Formula (2) is the relationship model between the annual variation amplitude of mud temperature and depth and time, where z is the depth, z = 0 at the mud surface; t is time (s); θ y is the annual mud surface temperature variation, that is, the difference between the maximum and minimum mud surface temperature in a year; k is the thermal diffusivity (cm 2 s -1 ); is the circular frequency of annual variation of mud temperature; The thermal diffusivity k value is used as a reference value for monitoring the buried depth of submarine cables; The mud temperature at point z below the seafloor is expressed as: Where T0 is the annual average temperature at a certain depth in the area; The annual variation amplitude of mud temperature at different depths is: The diffusion coefficient k is expressed as:

2. The monitoring method according to claim 1, characterized in that: The k value varies at different depths. The 0-0.5m layer is larger, the layers below 0.5m are smaller and the difference is not much. Below 2m depth, k is approximately the same as the k value of the 1-2m layer.

3. A shallow submarine cable depth monitoring system for implementing the monitoring method according to any one of claims 1 or 2, characterized in that: The shallow submarine cable depth monitoring system includes three main subsystems: data source, background service and real-time application; Initial data is transmitted to the shallow submarine cable depth monitoring system through a data source system. The data source system mainly includes meteorological data, socket data streams, and a measured database. The meteorological data provides real-time temperature information for the sea area. The shallow submarine cable depth monitoring system periodically uploads data to update the temperature-depth relationship model parameters. The socket data stream uploads sensor data in real time. The measured database stores initial equipment deployment information, including the optical cable deployment GPS, initial cable burial depth, and initial mud temperature. This provides initial information for the depth model data center and a reference for optimizing the temperature-depth relationship model. The data flow enters the backend service system, which includes three functional modules: data access module, data real-time verification processing module, and temperature depth relationship model data center; The data access module is responsible for unifying various types of raw data formats into the system's common data format and uploading the data to the real-time verification processing module. The raw data includes meteorological data, sensor data, and measured databases. The data real-time verification processing module is responsible for real-time collection of data generated by the data source, and performs data classification and data preprocessing; the data formats collected in real time include files, databases, and network data streams; The temperature-depth model data center is responsible for temperature-depth relationship modeling and depth anomaly monitoring. The data center models the received distributed data streams, detects real-time parameters to predict and optimize the temperature-depth relationship model, and issues early warning judgments on burial depth data based on early warning algorithms. When the burial depth at a certain location continuously decreases over a period of time, a safety warning is issued, indicating the risk of optical cable leakage. When the burial depth changes significantly in a short period of time, an abnormal alarm will be issued; The calculation results of the temperature-depth model data center flow to the real-time application system; the real-time application system is responsible for monitoring the submarine cable burial depth information and system control, providing a monitoring platform for shore-based staff; the real-time application system consists of two parts: the background detection system and the online monitoring system; the background monitoring system mainly realizes the model prediction and model optimization functions as well as the data analysis results, providing professional R&D personnel with the basis for system working status monitoring and data analysis; the online monitoring system realizes the real-time status visualization monitoring platform of the submarine cable burial depth, and provides equipment maintenance personnel with monitoring images, equipment status and alarm and early warning information results.

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