Distributed marine earthquake monitoring and data processing system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, and particularly discloses a distributed marine earthquake monitoring and data processing system based on the Internet of Things, which comprises a seabed node earthquake sensor network, edge computing equipment, a distributed Internet of Things platform, a central data processing server, a multi-wave-source numerical simulation module and a visual early warning platform, wave field simulation of ocean earthquakes and seabed landslides is optimized through a data processing algorithm, the tsunami wave propagation path, intensity and expected arrival time are displayed in real time, and abnormal wave detection and early warning are carried out based on the wave field topology change rate; data are collected and transmitted in real time through the seabed node seismic sensor network and the Internet of Things platform, multi-wave source simulation and wave field topology change rate detection are integrated, and tsunami and seismic waves are monitored.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a distributed marine earthquake monitoring and data processing system based on the Internet of Things. Background Art

[0002] In the open literature (Wang Huazhong, Xiang Jian, Shi Yu, Basic Logic and Key Methods of OBN Seismic Data Imaging Processing, Petroleum Geophysical Exploration), the "two-width and one-height" seabed node seismic data acquisition technology and the seismic wave imaging technology represented by full waveform inversion / least squares reverse time migration are introduced. Compared with the traditional towed cable data acquisition technology, the seabed node seismic data acquisition technology has the advantages of wide azimuth illumination, high data signal-to-noise ratio, no detection ghost waves, the existence of measured downlink wave fields, and four-component observations in offshore oil and gas seismic exploration. In view of the characteristics of the seabed node seismic data acquisition technology data, it is pointed out that the mismatch between the wave phenomena (mainly P_SV waves) in the actually observed multi-wave seismic wave fields and the seismic wave propagation and simulation theories has led to the current multi-wave imaging results not meeting expectations. , and it is recommended to focus on studying the physical roots of the mismatch between wave phenomena in the actual observed multi-wave seismic wave field and seismic wave propagation and simulation theories, rather than developing more advanced vector wave imaging algorithms. The single source model is a commonly used numerical model for marine earthquake monitoring. It can provide a simplified framework for a preliminary understanding of the propagation characteristics and wave field behavior of seismic waves, help describe the seismic wave propagation process, and quickly generate data in the early stages of calculation to support on-site decision-making and identify the direct impact of a single source on the wave field. However, when marine earthquakes and submarine landslides occur at the same time, submarine landslides will cause multi-source fluctuations, resulting in wave interference and superposition phenomena, and it is impossible to effectively analyze the impact of different wave sources on the propagation characteristics of P_SV waves. At the same time, in the complex marine environment with changing water depth and undulating terrain, the adaptability of the single source model is insufficient. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distributed marine earthquake monitoring and data processing system based on the Internet of Things, which collects and transmits data in real time through an ocean floor node seismic sensor network and an Internet of Things platform, integrates multi-wave source simulation and wave field topology change rate detection, accurately monitors tsunamis and seismic waves, and timely reduces coastal disaster risks through a visualization platform and multi-channel early warning.

[0004] In the ocean, marine earthquakes and submarine landslides can cause large fluctuations in sea level, resulting in dramatic displacement of sea water, thus forming tsunami waves. Such tsunami waves are recorded at tide gauge observation stations, showing fluctuations over time. The sudden displacement of the seabed causes the sea level to show a sharp change. The subsequent back-and-forth movement of the sea water will form multiple peaks and troughs. This situation not only represents the direct impact caused by the location of the earthquake source or landslide, but also shows the differences in the propagation of tsunami waves at different locations. The threat of this geological disaster lies in the fact that tsunami waves cause serious floods and damage when they reach coastal areas. Therefore, an accurate multi-wave source superposition numerical simulation model is needed to help accurately capture the wave field evolution when submarine landslides and earthquakes occur simultaneously, and provide strong support for real-time monitoring and early warning of marine earthquakes and submarine landslides.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A distributed marine earthquake monitoring and data processing system based on the Internet of Things includes a seabed node seismic sensor network, an edge computing device, a distributed Internet of Things platform, a central data processing server, a multi-wave source numerical simulation module and a visual early warning platform. Each sensor node of the seabed node seismic sensor network is connected to the edge computing device and the central data processing server through the distributed Internet of Things platform. The multi-wave source numerical simulation module is integrated with the central data processing server to process the observation data from the sensor nodes on the seabed and optimize the wave field simulation of marine earthquakes and submarine landslides through a data processing algorithm. The visual early warning platform is connected to the central data processing server and integrated with other early warning systems to display the propagation path, intensity and expected arrival time of tsunami waves in real time and send emergency information to the coastal areas. The central data processing server performs abnormal wave detection and early warning based on the wave field topology change rate.

[0007] As a further solution of the present invention, the seabed node seismic sensor network includes a plurality of sensor nodes arranged on the seabed, which collect multi-wave seismic wave data in real time and provide four-component observation data consisting of vertical displacement, horizontal displacement, pressure and shear wave.

[0008] As a further solution of the present invention, each sensor node in the seafloor node seismic sensor network is equipped with an edge computing device for real-time processing of four-component observation data collected by the sensor, quickly performing preliminary numerical simulation and data analysis, and responding to seafloor landslides and marine earthquakes.

[0009] As a further solution of the present invention, the central data processing server aggregates the data of all sensor nodes, and combines full waveform inversion and least squares reverse time migration technology to perform seismic wave imaging processing, execute multi-wave source superposition numerical simulation, and analyze the wave field evolution when submarine landslides and earthquakes occur simultaneously.

[0010] As a further solution of the present invention, the numerical simulation analysis process of the multi-wave source numerical simulation module includes:

[0011] Step 1, data preparation: real-time reception of earthquake data and landslide event data from the seafloor node seismic sensor network. The earthquake data includes the earthquake source location, magnitude, and source time. The landslide event data includes the location, scale, and duration of the landslide. These parameters are used to construct the landslide wave source. The water depth data and terrain data provided by the ocean depth map are resampled to generate a uniformly spaced water depth grid with an arc length of 10 seconds. The gridded data is refined to a time step of 0.5 seconds. The grid data is combined with the seafloor source location data to form a three-dimensional wave field model. According to the triggering time of the earthquake and the landslide, the initial energy release and displacement change conditions of the source and the landslide wave source are set. The simulation area adopts the absorbing boundary condition.

[0012] Step 2, construct a multi-source wave field model: select the finite volume method, and construct a multi-source wave field model based on the data prepared in step 1 to simulate the primary wave source of the earthquake and the secondary wave source generated by the landslide. The superposition model of the multi-source wave field calculates the wave field intensity of each wave source at the same time point through the linear superposition principle;

[0013] Step 3, perform numerical simulation: the time step is 0.5 seconds, and numerical calculation is performed in combination with the CFL convergence condition judgment number. At each time step, the wave field state of each grid point is updated by numerical methods, and the displacement, velocity and energy distribution of the wave are calculated. The wave field update process includes wave propagation, reflection and refraction. When the seismic wave propagation lags, the propagation path of the secondary wave is calculated by simulating the time and position of the landslide. The mutual interference effect between the wave sources is calculated in real time, and the phases and amplitudes of multiple wave fields are superimposed to generate the final wave propagation path. The propagation of seismic waves generated by the primary wave source and the secondary wave source in the ocean is predicted;

[0014] Step 4, output simulation result data: record the wave field information of each grid node at each time step, the wave field information includes displacement, velocity, acceleration, stress and strain of the wave, and store the wave field information in the central data processing server.

[0015] As a further solution of the present invention, in step 3, the CFL convergence condition judgment number is based on the Courant number reflecting the relationship between the wave propagation distance and the grid unit size within a time step. The Courant number is less than or equal to 1, and the current time step is selected as 0.5 seconds.

[0016] As a further solution of the present invention, in the central data processing server, the wave field data acquired in real time by the sensor node is used to extract the wave crest and trough positions, and the contour extraction algorithm is used to generate a closed curve in the wave field. The wave field topology change rate is based on the geometric topological structure of the wave field, and the evolution of the spatial geometric characteristics of the wave field is tracked. The formula of the wave field topology change rate is:

[0017]

[0018] Wherein: t is the time variable, x and y are the spatial positions of the wave field, A(t) is the area of ​​the closed annular region formed by the crest and trough in the wave field at time t, s is the integral variable of the boundary curve of the closed annular region, k(s) is the curvature of the curve at position s in the wave field, ∫k(s)ds is the curvature calculation of the boundary curve of the closed annular region, T(t,x,y) is the wave field topology change rate at position (x,y) at time t, and the wave field topology change rate is compared with the preset threshold for abnormal wave field detection and early warning.

[0019] As a further solution of the present invention, the distributed Internet of Things platform includes Internet of Things device nodes, communication network modules and data gateways. The Internet of Things device nodes are responsible for collecting earthquake and landslide data and performing preprocessing. The communication network module transmits data through low-power wide area networks, satellites and optical fiber communications. The data gateway manages data exchange between devices and performs protocol conversion, data encryption and data filtering tasks.

[0020] As a further solution of the present invention, the visual early warning platform marks abnormal areas on the 3D visualization interface based on the wave field topology change rate, and sends multi-level early warning signals to users according to the wave field topology change rate. When the wave field topology change rate is greater than 80% of a preset threshold and less than a preset threshold, a warning signal is issued; when the wave field topology change rate is greater than or equal to the preset threshold, an emergency warning signal is issued; when the wave field topology change rate is greater than the preset threshold and the increase in the energy density of the wave field is greater than 35% of the energy density of the previous time step, a disaster-level alarm signal is issued.

[0021] Compared with the prior art, in order to solve the technical problem, the present invention has the following technical effects: the present invention realizes real-time data collection and transmission through the combination of the seabed node seismic sensor network and the distributed Internet of Things platform, ensures a rapid response to seabed earthquakes and landslide events, and integrates the multi-wave source numerical simulation module with the central data processing server, which can handle the wave field evolution when seabed landslides and earthquakes occur simultaneously, overcomes the limitations of the traditional single-source model in complex environments, and adopts the wave field topology change rate detection method, which can more accurately capture abnormal changes in the wave field, significantly improves the detection sensitivity of tsunamis and seismic waves, and can display the wave propagation path and intensity in real time. The early warning information is transmitted to the coast through multiple channels, ensuring that coastal areas can take preventive measures in time to effectively reduce disaster risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of seabed deformation caused by submarine landslide and earthquake in the present invention;

[0023] Figure 2 This is a comparison chart of the tsunami waveform simulation and observation at multiple sites of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, in the ocean, marine earthquakes and submarine landslides can cause large fluctuations in sea level, resulting in violent displacement of sea water, thus forming tsunami waves. Such tsunami waves are recorded at tide gauge observation stations, showing fluctuations over time. The sudden displacement of the seabed causes the sea level to show a sharp change. The subsequent back-and-forth movement of the sea water will form multiple peaks and troughs. This situation not only represents the direct impact caused by the location of the earthquake source or landslide, but also shows the differences in the propagation of tsunami waves at different locations. The threat of this geological disaster is that tsunami waves will cause serious floods and damage when they reach coastal areas. Therefore, an accurate multi-wave source superposition numerical simulation model is needed to help accurately capture the wave field evolution when submarine landslides and earthquakes occur simultaneously, and provide strong support for real-time monitoring and early warning of marine earthquakes and submarine landslides.

[0026] The present invention proposes a distributed marine earthquake monitoring and data processing system based on the Internet of Things, which includes a seabed node seismic sensor network, an edge computing device, a distributed Internet of Things platform, a central data processing server, a multi-wave source numerical simulation module and a visual early warning platform. Each sensor node of the seabed node seismic sensor network is connected to the edge computing device and the central data processing server through the distributed Internet of Things platform. The multi-wave source numerical simulation module is integrated with the central data processing server to process the observation data from the sensor nodes on the seabed, and optimize the wave field simulation of marine earthquakes and submarine landslides through a data processing algorithm. The visual early warning platform is connected to the central data processing server and integrated with other early warning systems to display the propagation path, intensity and expected arrival time of tsunami waves in real time, and send emergency information to the coastal areas. The central data processing server performs abnormal wave detection and early warning based on the wave field topology change rate.

[0027] It should be noted that the seafloor node seismic sensor network includes several sensor nodes arranged on the seafloor, which collect multi-wave seismic wave data in real time and provide four-component observation data consisting of vertical displacement, horizontal displacement, pressure and shear wave.

[0028] Through the real-time acquisition of the seafloor node seismic sensor network, the system can obtain four-component observation data consisting of vertical displacement, horizontal displacement, pressure and shear wave, so as to comprehensively monitor the propagation characteristics of seafloor earthquakes and landslide waves. This multi-dimensional data acquisition improves the accuracy and integrity of wave field monitoring, supports complex multi-wave source numerical simulation, and helps accurately analyze the interaction between earthquakes and landslides. The system can not only detect the propagation path of tsunami waves earlier and more accurately, but also accurately capture abnormal fluctuations through the analysis of the wave field topology change rate, significantly improving the sensitivity and accuracy of early warning. Combined with real-time four-component data, the system can respond quickly, generate reliable early warning signals, reduce false alarms, and provide decision support for disaster response. In addition, the rich data source can optimize the numerical simulation process and make the simulation results closer to the actual situation, thus providing all-round technical support for early warning and disaster response of tsunamis and earthquakes. It is significantly better than the traditional single data observation method and solves the problem of insufficient accuracy of traditional technology under complex wave fields.

[0029] It should be noted that each sensor node in the seafloor node seismic sensor network is equipped with an edge computing device to process the four-component observation data collected by the sensor in real time, perform preliminary numerical simulation and data analysis, and respond to submarine landslides and marine earthquakes.

[0030] Each sensor node in the seafloor node seismic sensor network is equipped with an edge computing device that can process four-component observation data in real time, perform preliminary numerical simulation and data analysis, reduce data transmission delays, and improve the real-time performance and response speed of the system. This setting generates early warning information when a seafloor landslide or earthquake occurs, improving the efficiency and accuracy of the overall monitoring system.

[0031] It should be noted that the central data processing server aggregates the data from all sensor nodes, and combines full waveform inversion and least squares reverse time migration technology to perform seismic wave imaging processing, execute multi-wave source superposition numerical simulation, and analyze the wave field evolution when submarine landslides and earthquakes occur simultaneously.

[0032] Combining full waveform inversion and least squares reverse time migration technology to perform seismic wave imaging processing and perform multi-wave source stacking numerical simulation, the specific implementation process is as follows:

[0033] Step 1: Data aggregation and preprocessing: The central data processing server aggregates the four-component observation data from each sensor node through the distributed Internet of Things platform, including vertical displacement, horizontal displacement, pressure and shear wave. These data also contain information from multiple wave sources (such as earthquake sources and landslide wave sources), and performs data cleaning and format unification.

[0034] Step 2: Full waveform inversion: Use the preliminary observation data to generate a rough formation velocity model (including seafloor topography, ocean water layer thickness and seismic wave propagation velocity), use the initial model for forward calculation, simulate the propagation process of seismic waves, and generate simulated seismic waveforms. Compare the simulated waveforms with the actual observed waveforms, calculate the errors (such as EPS values), and iteratively optimize the model through the least squares method to gradually reduce the errors, thereby generating a more accurate underground velocity model. Full waveform inversion usually requires multiple iterative calculations to ensure that the final underground model can reflect the real geological structure.

[0035] Step three, least squares reverse time migration processing: Based on the formation velocity model generated by full waveform inversion, reverse time migration gradually images the seismic signal to the source position by backpropagating seismic waves, and uses the least squares method to correct the errors of the migration imaging results to reduce the impact of complex wave field phenomena such as multiple waves, reflection waves and refraction waves in the seismic data on the imaging accuracy. After multiple iterations, high-precision seismic wave field imaging results are finally generated to reflect the wave field structure of submarine landslides and earthquakes.

[0036] Through full waveform inversion technology, the system can generate more accurate underground velocity models and capture subtle differences in different geological structures. Multiple iterations of model optimization gradually reduce the error between the simulated waveform and the actual observed waveform, ensuring that the generated stratigraphic model can reflect the real geological structure, significantly improving the resolution and accuracy of seismic wave imaging, especially in complex seabed environments, solving complex wave phenomena such as multiple waves and reflected waves that are difficult to handle with traditional methods. Combining full waveform inversion with least squares reverse time migration technology, the system can handle multi-source wave fields, including the superposition effect of seabed earthquake sources and landslide wave sources. The mutual interference, reflection, and refraction of seismic waves and landslide waves can be accurately analyzed to generate high-precision imaging results, solving the limitation that simple wave field simulation is difficult to capture complex waves. The least squares reverse time migration technology can reduce the interference of reflected waves and multiple waves in imaging by backpropagating seismic waves and iteratively correcting errors, reducing the imaging errors caused by complex wave fields in traditional methods, and further improving the accuracy and reliability of imaging results, especially in multi-wave source scenarios. It can restore the submarine wave field structure, not only process earthquake or landslide wave sources separately, but also analyze the evolution of the wave field when submarine landslides and earthquakes occur simultaneously through numerical simulation of multiple wave source data. The system can predict the propagation path, interaction and energy distribution of the wave field, providing a more reliable data basis for responding to complex submarine disaster events. Accurate seismic wave imaging and multi-wave source simulation results can provide more detailed wave field information for the early warning system, help to timely detect dangerous waves caused by tsunamis and earthquakes, and generate more effective early warning signals. In addition, based on the data of wave field simulation, decision makers can make more reasonable emergency responses and optimize disaster prevention plans and resource allocation in coastal areas.

[0037] It should be noted that the numerical simulation analysis process of the multi-wave source numerical simulation module includes:

[0038] Step 1, data preparation: real-time reception of earthquake data and landslide event data from the seafloor node seismic sensor network. The earthquake data includes the earthquake source location, magnitude, and source time. The landslide event data includes the location, scale, and duration of the landslide. These parameters are used to construct the landslide wave source. The water depth data and terrain data provided by the ocean depth map are resampled to generate a uniformly spaced water depth grid with an arc length of 10 seconds. The gridded data is refined to a time step of 0.5 seconds. The grid data is combined with the seafloor source location data to form a three-dimensional wave field model. According to the triggering time of the earthquake and the landslide, the initial energy release and displacement change conditions of the source and the landslide wave source are set. The simulation area adopts the absorbing boundary condition.

[0039] Step 2, construct a multi-source wave field model: select the finite volume method, and construct a multi-source wave field model based on the data prepared in step 1 to simulate the primary wave source of the earthquake and the secondary wave source generated by the landslide. The superposition model of the multi-source wave field calculates the wave field intensity of each wave source at the same time point through the linear superposition principle;

[0040] Step 3, perform numerical simulation: the time step is 0.5 seconds, and numerical calculation is performed in combination with the CFL convergence condition judgment number. At each time step, the wave field state of each grid point is updated by numerical methods, and the displacement, velocity and energy distribution of the wave are calculated. The wave field update process includes wave propagation, reflection and refraction. When the seismic wave propagation lags, the propagation path of the secondary wave is calculated by simulating the time and position of the landslide. The mutual interference effect between the wave sources is calculated in real time, and the phases and amplitudes of multiple wave fields are superimposed to generate the final wave propagation path. The propagation of seismic waves generated by the primary wave source and the secondary wave source in the ocean is predicted;

[0041] Step 4, output simulation result data: record the wave field information of each grid node at each time step, the wave field information includes displacement, velocity, acceleration, stress and strain of the wave, and store the wave field information in the central data processing server.

[0042] It should be noted that in step 3, the CFL convergence condition judgment number is based on the Courant number, which reflects the relationship between the wave propagation distance and the grid unit size within a time step. The Courant number is kept less than or equal to 1, and the current time step is selected as 0.5 seconds.

[0043] By receiving earthquake data and landslide data, the system can construct a three-dimensional wave field model to simulate the complex interaction of earthquake and landslide wave sources. The wave field of each wave source is calculated by linear superposition, and complex phenomena such as wave reflection and refraction are processed during the simulation process. This accurate simulation can effectively reflect the wave field evolution process when submarine landslides and earthquakes occur simultaneously, thereby improving the prediction accuracy of wave propagation paths, wave heights and wave velocities. In step 3, the CFL convergence condition judgment number ensures the stability of the numerical simulation by controlling the Courant number (CFL number). By keeping the Courant number less than or equal to 1, the system can accurately update the wave field state in each time step when the time step is 0.5 seconds, avoiding instability or errors in numerical calculations, ensuring the consistency and accuracy of the simulation in time and space, and making the calculation results more reliable. Due to the use of the finite volume method, the numerical simulation process can efficiently handle the wave field updates of multiple wave sources, especially in real-time calculations, combining the energy release and mutual interference effects of each wave source, helping the system to dynamically track the propagation and evolution of the wave field in complex earthquake and landslide scenarios, and providing timely and reliable data for subsequent early warning analysis. By generating a uniformly spaced water depth grid refined to 10 seconds of arc and setting the time step to 0.5 seconds, the numerical simulation can carefully depict the local changes in the wave field, enabling the system to better handle wave propagation under complex marine terrain conditions, improving the resolution of the simulation and enhancing the ability to capture details. The system can accurately predict the propagation path of submarine landslides and seismic waves in the ocean by calculating key parameters such as wave displacement, velocity, and energy distribution in real time. The superposition calculation of phase and amplitude further helps analyze the contribution of different wave sources to the wave field. The system can predict the propagation and arrival time of waves in different areas, providing a scientific basis for disaster prevention and early warning. At each time step, the system records the wave field status of each grid node, including displacement, velocity, acceleration, stress, and strain information, and stores it in the central data processing server. This rich wave field information not only supports subsequent analysis and research, but also provides a reliable historical data reference for long-term disaster monitoring.

[0044] It should be noted that in the central data processing server, the wave field data obtained in real time by the sensor nodes are used to extract the wave crest and trough positions, and the contour extraction algorithm is used to generate closed curves in the wave field. The wave field topology change rate is based on the geometric topological structure of the wave field, and tracks the evolution of the spatial geometric characteristics of the wave field. The formula for the wave field topology change rate is:

[0045]

[0046] Wherein, t is the time variable, x and y are the spatial positions of the wave field, A(t) is the area of ​​the closed annular region formed by the peaks and troughs in the wave field at time t, s is the integral variable of the boundary curve of the closed annular region, k(s) is the curvature of the curve at position s in the wave field, ∫k(s)ds is the curvature calculation of the boundary curve of the closed annular region, T(t,x,y) is the wave field topology change rate at position (x,y) at time t, and the wave field topology change rate is compared with the preset threshold for abnormal wave field detection and early warning.

[0047] By extracting the positions of wave crests and troughs and generating closed curves in the wave field, the system can track the spatial geometric characteristics of the wave field in real time. By calculating the wave field topology change rate, the system can sensitively detect subtle changes in the wave field, especially when the wave field structure changes significantly after an earthquake or landslide event. Compared with traditional energy or amplitude detection methods, the wave field topology change rate is more accurate and can identify early abnormal fluctuations and provide more timely warnings. Through the curvature integral ∫k(s)ds in the formula, the system can accurately capture the changes in the wave field geometry, especially the dynamic changes in the positions of wave crests and troughs. This curvature calculation effectively describes the bending and deformation in the wave field, enabling the system to adapt to complex multi-source wave fields, especially when earthquakes and landslides occur simultaneously, which helps to identify mutually interfering waveforms. Based on the change in time t, the system can dynamically track the evolution of the wave field. The wave field topology change rate T(t,x,y) is a key indicator, and the geometric change trend in the wave field is calculated in real time as time advances. This real-time tracking function ensures that the system can identify potential tsunami, earthquake or landslide fluctuations in the first place when the wave field changes significantly, and provide rapid response and warning. By comparing with the preset threshold, the wave field topology change rate provides a method to quantify abnormal wave fields. When the change rate exceeds the set threshold, the system triggers the abnormal wave field detection and warning mechanism. Compared with the traditional amplitude threshold, this method can identify wave field anomalies earlier and more accurately, improve the sensitivity and accuracy of warning, and reduce false alarms. In complex multi-source wave fields, the geometric changes of the wave field are difficult to detect by simple waveform parameters. Through the analysis of the geometric topological structure, the wave field topological change rate effectively avoids misjudgment in complex wave fields, especially when reflected waves, refracted waves or multi-source interference, the technology can accurately judge the abnormal state of the wave field by the change of geometric features. The wave field topological change rate is based on the geometric topological structure of the wave field, so that the system can not only judge the energy and wave height changes of the wave, but also provide information on the spatial structure changes of the wave field. This provides more dimensional data support for disaster assessment and analysis, helps to more comprehensively understand the wave field changes caused by earthquakes or landslides, and provides a basis for subsequent decision-making and response measures.

[0048] It should be noted that the distributed IoT platform includes IoT device nodes, communication network modules and data gateways. IoT device nodes are responsible for collecting earthquake and landslide data and preprocessing them. Communication network modules transmit data through low-power wide area networks, satellites and optical fiber communications. Data gateways manage data exchange between devices and perform protocol conversion, data encryption and data filtering tasks. The visual warning platform marks abnormal areas on the 3D visualization interface based on the wave field topology change rate, and sends multi-level warning signals to users based on the wave field topology change rate. When the wave field topology change rate is greater than 80% of the preset threshold and less than the preset threshold, a warning signal is issued. When the wave field topology change rate is greater than or equal to the preset threshold, an emergency warning signal is issued. When the wave field topology change rate is greater than the preset threshold and the increase in the energy density of the wave field is greater than 35% of the energy density of the previous time step, a disaster-level alarm signal is issued.

[0049] In order to clearly illustrate the technical advantages of the technical solution of the present invention, a detailed description is given based on the specific contents of the embodiments.

[0050] Select five sensor nodes A, B, C, D, and E in the seabed node seismic sensor network. A is located 10 kilometers near the earthquake source or landslide source. It is a core node located in the earthquake fault area and is used to monitor the initial fluctuations of the earthquake source and the instantaneous impact of the landslide. The water depth is 2000 meters. B is a secondary node closer to the earthquake source or landslide source and is used to monitor the wave propagation near the earthquake source. It is 25 kilometers away from the earthquake source and has a water depth of 1800 meters. C is a deep sea area node far away from the earthquake source and is 100 kilometers away from the earthquake source. It is mainly used for monitoring long-distance wave field propagation and has a water depth of 3500 meters. D is a node close to the coastal area and monitors the wave characteristics of tsunami waves when they are about to reach the land. It is 150 kilometers away from the earthquake source and has a water depth of 800 meters. E is an intermediate monitoring point between the earthquake source and the coastal area and is used to monitor the mid-way propagation process of tsunami waves. It is 60 kilometers away from the earthquake source and has a water depth of 2500 meters. The following is obtained based on the monitored data and numerical simulation data: Figure 2 Comparison chart shown.

[0051] like Figure 2As shown, by comparing and analyzing the actual monitoring data of the five stations A, B, C, D, and E in the figure with the numerical simulation results, the technical effect of the technical solution of the invention can be clearly seen, especially the outstanding performance in multi-source wave field simulation and early warning capabilities. EPS (error) is the error index between the numerical simulation results and the actual observation data, which measures the degree of fit between the simulation data and the real data. In the results of points A and B, eps = 0.45 (A), eps = 0.47 (B). These two stations are located near the source of the earthquake or the source of the landslide, and the fluctuation amplitude of the waveform is large (the maximum fluctuation exceeds 200cm). There is a large difference between the numerical simulation results and the actual data, and the eps value is high. This shows that in the area close to the earthquake source, the wave field is complex, and the seismic waves and landslide waves interfere with each other, resulting in an error between the numerical simulation and the actual data. The eps of point E is 0.0022. The station is located in the mid-propagation area. The numerical simulation results are highly consistent with the actual observation data, with extremely small errors and very low eps values. The amplitude is small, and the maximum fluctuation is about 10cm. This shows that in the medium-distance area, the numerical simulation can accurately capture the waveform, indicating that the model has a very good simulation effect in this area. The eps of sites C and D are close to 0. Sites C and D are far away from the epicenter, located in the deep sea and coastal areas, with a small waveform fluctuation range (within ±20cm). The numerical simulation results are highly consistent with the actual observation data, and the simulation error is extremely small. This shows that the numerical simulation model also has high accuracy in predicting and capturing long-distance wave fields. The eps values ​​of sites A and B are high, indicating that the numerical simulation is more difficult in areas close to the epicenter; while the eps values ​​of sites E, C, and D are very small, indicating that the accuracy of numerical simulation is very high in areas far away from the epicenter.

[0052] pass Figure 2 As shown, it can be seen that the numerical simulation technology in the present invention performs well in different distance areas. In the area close to the earthquake source, the wave field is complex and has multi-source interference, but in the medium and long distance areas, the numerical simulation can accurately capture the propagation of the waveform, indicating that the model is very reliable and accurate in processing long-distance wave propagation. The combination of numerical simulation and full waveform inversion technology performs well in complex wave fields, and effectively reduces the error in far-field wave propagation, which can provide accurate and reliable data support for tsunami warning.

[0053] The present invention realizes real-time data collection and transmission through the combination of the seabed node seismic sensor network and the distributed Internet of Things platform, ensuring a rapid response to seabed earthquakes and landslide events. The multi-wave source numerical simulation module is integrated with the central data processing server, which can process complex multi-source wave field data, especially the wave field evolution when seabed landslides and earthquakes occur simultaneously, overcoming the limitations of traditional single-source models in complex environments. The adopted wave field topology change rate detection method can more accurately capture abnormal changes in the wave field, significantly improving the detection sensitivity of tsunamis and seismic waves. The visual early warning platform displays the wave propagation path and intensity in real time, and the early warning information is transmitted to the coast through multiple channels, ensuring that coastal areas can take preventive measures in time to effectively reduce disaster risks.

[0054] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0055] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed marine earthquake monitoring and data processing system based on the Internet of Things, comprising a seafloor node seismic sensor network, an edge computing device, a distributed Internet of Things platform, a central data processing server, a multi-wave source numerical simulation module and a visual early warning platform, characterized in that: Each sensor node of the seabed node seismic sensor network is connected to the edge computing device and the central data processing server through a distributed Internet of Things platform. The multi-wave source numerical simulation module is integrated with the central data processing server to process the observation data from the sensor nodes on the seabed and optimize the wave field simulation of marine earthquakes and submarine landslides through data processing algorithms. The visual early warning platform is connected to the central data processing server and integrated with other early warning systems to display the propagation path, intensity and expected arrival time of tsunami waves in real time and send emergency information to the coastal areas. The central data processing server performs abnormal wave detection and early warning based on the wave field topology change rate.

2. A distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 1, characterized in that: The seafloor node seismic sensor network includes several sensor nodes arranged on the seafloor, which collect multi-wave seismic wave data in real time and provide four-component observation data consisting of vertical displacement, horizontal displacement, pressure and shear wave.

3. A distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 2, characterized in that: Each sensor node in the seafloor node seismic sensor network is equipped with an edge computing device to process the four-component observation data collected by the sensor node in real time, quickly perform preliminary numerical simulation and data analysis, and respond to seafloor landslides and marine earthquakes.

4. A distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 3, characterized in that: The central data processing server aggregates the data from all sensor nodes, performs seismic wave imaging processing in combination with full waveform inversion and least squares reverse time migration techniques, performs multi-wave source superposition numerical simulation, and analyzes the wave field evolution when submarine landslides and earthquakes occur simultaneously.

5. The distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 2 is characterized in that: The numerical simulation analysis process of the multi-wave source numerical simulation module includes: Step 1, data preparation: real-time reception of earthquake data and landslide event data from the seafloor node seismic sensor network. The earthquake data includes the earthquake source location, magnitude, and source time. The landslide event data includes the location, scale, and duration of the landslide. The landslide wave source is constructed. The water depth data and terrain data are resampled to generate a 10-second arc uniformly spaced water depth grid. The gridded data is refined to a time step of 0.5 seconds. The grid data is combined with the seafloor source location data to form a three-dimensional wave field model. According to the triggering time of the earthquake and the landslide, the initial energy release and displacement change conditions of the source and the landslide wave source are set. The simulation area adopts the absorbing boundary condition; Step 2, construct a multi-source wave field model: select the finite volume method, and construct a multi-source wave field model based on the data prepared in step 1, simulate the main wave source of the earthquake and the secondary wave source generated by the landslide, and calculate the wave field intensity of each wave source at the same time point by the linear superposition principle; Step 3, perform numerical simulation: the time step is 0.5 seconds, and numerical calculation is performed in combination with the CFL convergence condition judgment number. At each time step, the wave field state of each grid point is updated by numerical methods, and the displacement, velocity and energy distribution of the wave are calculated. The wave field update process includes wave propagation, reflection and refraction. When the seismic wave propagation lags, the propagation path of the secondary wave is calculated by simulating the time and position of the landslide. The mutual interference effect between the wave sources is calculated in real time, and the phases and amplitudes of multiple wave fields are superimposed to generate the final wave propagation path. The propagation of seismic waves generated by the primary wave source and the secondary wave source in the ocean is predicted; Step 4, output simulation result data: record the wave field information of each grid node at each time step, the wave field information includes displacement, velocity, acceleration, stress and strain of the wave, and store the wave field information in the central data processing server.

6. A distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 5, characterized in that: In step 3, the CFL convergence condition judgment number is based on the Courant number, which reflects the relationship between the wave propagation distance and the grid unit size within a time step. The Courant number is less than or equal to 1, and the current time step is selected as 0.5 seconds.

7. The distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 1 is characterized in that: In the central data processing server, the wave field data obtained in real time by the sensor nodes are used to extract the wave crest and trough positions. The contour extraction algorithm is used to generate closed curves in the wave field. The wave field topology change rate is based on the geometric topological structure of the wave field and tracks the evolution of the spatial geometric characteristics of the wave field. The formula for the wave field topology change rate is: Wherein, t is the time variable, x and y are the spatial positions of the wave field, A(t) is the area of ​​the closed annular region formed by the wave crest and the wave trough in the wave field at time t, s is the integral variable of the boundary curve of the closed annular region, k(s) is the curvature of the curve at position s in the wave field, ∫k(s)ds is the curvature calculation of the boundary curve of the closed annular region, and T(t,x,y) is the wave field topology change rate at position (x,y) at time t; the wave field topology change rate is compared with the preset threshold value for abnormal wave field detection and early warning.

8. The distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 1 is characterized in that: The distributed Internet of Things platform includes Internet of Things device nodes, communication network modules and data gateways; the Internet of Things device nodes are responsible for collecting earthquake and landslide data and performing preprocessing; the communication network module transmits data through low-power wide area networks, satellites and optical fiber communications; the data gateway manages data exchange between devices and performs protocol conversion, data encryption and data filtering tasks.

9. The distributed marine earthquake monitoring and data processing system based on the Internet of Things according to claim 7, characterized in that: The visual early warning platform marks abnormal areas on the 3D visualization interface based on the wave field topology change rate, and sends multi-level early warning signals to users according to the wave field topology change rate. When the wave field topology change rate is greater than 80% of the preset threshold and less than the preset threshold, a warning signal is issued; when the wave field topology change rate is greater than or equal to the preset threshold, an emergency warning signal is issued; when the wave field topology change rate is greater than the preset threshold and the increase in the energy density of the wave field is greater than 35% of the energy density of the previous time step, a disaster-level alarm signal is issued.

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