Alarm method and positioning system for earthquake early warning station network abnormity real-time monitoring

Abstract

The invention relates to the technical field of earthquake early warning, and discloses an earthquake early warning station network abnormity real-time monitoring alarm method and positioning system, and the method comprises the steps: obtaining M groups of original fluctuation signals based on M stations; extracting abnormal data of the original fluctuation signal, wherein the abnormal data comprises data delay, server time service, network transmission flow and zero drift; and if the synchronization of the abnormal data exceeds the limit, triggering an alarm. According to the method, original fluctuation signals are acquired based on M stations, four types of key abnormal data including data delay, server time service, network transmission flow and zero drift are extracted, and alarm information is output when the abnormal data synchronously exceed the limit, so that the abnormal conditions of signal acquisition, transmission and processing links can be comprehensively covered, and the reliability of the system is improved. Real-time capture of systematic signal abnormity of the earthquake early warning system is realized, and thus alarm is triggered rapidly.

Description

Technical Field

[0001] The present invention relates to the field of earthquake early warning technology, and more specifically, to an alarm method and positioning system for real-time monitoring of abnormalities in an earthquake early warning station network. Background Art

[0002] Existing technologies face significant challenges when conducting earthquake monitoring in areas with high mountains and valleys, dense faults, and significant terrain undulations. Traditional earthquake source location methods rely heavily on calculating seismic wave travel time differences and global velocity models. However, in such areas, mountain obstruction can lead to complex and variable seismic wave propagation paths, resulting in severe multipath effects and significant deviations from travel time difference calculations. Furthermore, the lateral inhomogeneity of near-surface velocities caused by terrain elevation differences can significantly deviate from the velocity model and actual conditions, further exacerbating location errors. Summary of the Invention

[0003] The present invention provides an alarm method and a positioning system for real-time monitoring of abnormalities in an earthquake early warning station network, which solve the technical problems raised in the background technology.

[0004] In a first aspect, a method for real-time monitoring of abnormalities in an earthquake early warning station network is provided, comprising: S1, obtain M groups of original fluctuation signals based on M stations; S2, extracts abnormal data from the original fluctuation signal, including data delay, server timing, network transmission flow and zero drift; S3: If the synchronization of abnormal data exceeds the limit, an alarm is triggered.

[0005] In a second aspect, a positioning system for real-time monitoring of anomalies in an earthquake early warning station network is applied to the aforementioned method for real-time monitoring and alarming of anomalies in an earthquake early warning station network, and responds to an earthquake alarm and performs the following steps: The data acquisition module is used to perform outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring of the target mountain based on M stations to obtain M groups of original fluctuation signals; The data processing module is used to perform Kirchhoff phase shift processing on each set of original wave signals using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station; The positioning estimation module is used to construct a closed wavefront curvature field based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, and to estimate the wavefront estimated radius, the estimated coordinates of the source plane and the estimated depth of the source center in sequence for the closed wavefront curvature field; The precise positioning module is used to construct a three-dimensional grid with the estimated coordinates of the source plane and the estimated depth of the source center as the initial position; for any grid in the three-dimensional grid, the source parameter residual of each grid in the three-dimensional grid is calculated based on the propagation direction and local wavefront curvature of each monitoring station, combined with the minimum variance unbiased estimation algorithm, and the grid with the smallest source parameter residual is taken as the final source position.

[0006] Furthermore, outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring include: Outer ring monitoring includes: constructing a first ring-shaped monitoring array around the ridge of the target mountain; Inner ring monitoring includes: constructing a second monitoring array in a ring around the basin of the target mountain area; Fault monitoring includes: constructing a linear third monitoring array at fixed horizontal intervals based on the active faults in the target mountain area; Vertical monitoring includes: constructing a linear fourth monitoring array at fixed vertical intervals from the ridge of the target mountain to the basin.

[0007] Furthermore, Kirchhoff phase shift processing is performed on each set of original wave signals using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station, including: Each group of original fluctuation signals is segmented according to the preset sliding window H to obtain K sub-signals, and each sub-signal is subjected to mean and trend removal processing to obtain a standard fluctuation signal; Initialize the local wavefront model as follows: Establish a three-dimensional model of the target mountain; perform spherical fitting on the three-dimensional model to obtain a local wavefront model, and determine the spherical center coordinates of the local wavefront model; Based on the local wavefront model and the coordinates of the spherical center, the prior P-wave velocity and the sampling interval of the standard wave signal, the standard wave signal is phase-shifted according to a preset time delay to obtain a migration wave signal; Determine the center coordinates of the annular first monitoring array, connect the center coordinates to each monitoring station in the first monitoring array, and form a set of candidate propagation directions; superimpose the M groups of migration fluctuation signals in each candidate propagation direction to determine the superposition strength of each candidate propagation direction, and select the candidate propagation direction with the highest superposition strength as the propagation direction; A local coordinate system is constructed with the propagation direction as the normal, and the station coordinates of M monitoring stations are fitted with a surface in the local coordinate system to obtain the local wavefront curvature of each monitoring station.

[0008] Furthermore, a closed wavefront curvature field is constructed based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, including: Extract the geometric curve of the ridge of the target mountain to construct the monitoring outer ring; A virtual monitoring station is inserted at the midpoint of adjacent monitoring stations in the outer monitoring ring, and the local wavefront curvature of the virtual station is the average value of the local wavefront curvature of the adjacent monitoring stations; Repeatedly inserting virtual monitoring stations until the number of monitoring stations reaches a preset threshold value to form a local wavefront curvature set; The local wavefront curvature set is fitted to obtain a curvature function as a closed wavefront curvature field.

[0009] Furthermore, the closed wavefront curvature field is sequentially estimated to obtain the wavefront estimated radius, the source plane estimated coordinates, and the source center estimated depth, including: determining the wavefront estimation radius based on the annular average curvature of the closed wavefront curvature field; Loading a preset threshold number of wavefront estimation radii within the monitoring outer ring, and obtaining the source plane estimation coordinates by minimizing the intersection of the wavefront estimation radii; Determine the mean time of the timestamps of the original fluctuation signals of the first monitoring array and the mean time of the timestamps of the original fluctuation signals of the second monitoring array, and calculate the time difference between the mean times; Determine the vertical propagation time based on the time difference of the mean time; Calculate the estimated depth of the earthquake center ,as follows: in, represents the propagation speed of the P wave, represents the vertical propagation time, represents the wavefront estimation radius, It represents the average value of the Euclidean distance between each monitoring station in the first monitoring array and the estimated coordinates of the source plane.

[0010] Furthermore, a three-dimensional grid is constructed using the estimated coordinates of the earthquake source plane and the estimated depth of the earthquake source center as the initial position, including: A three-dimensional grid with a preset step size is constructed within a spatial range with the estimated coordinates of the earthquake source plane as the center, extending a first preset distance to the east, west, south and north, and with the estimated depth of the earthquake source center as the center, extending a second preset distance vertically up and down.

[0011] Furthermore, based on the propagation direction and local wavefront curvature of each monitoring station, the minimum variance unbiased estimation algorithm is combined to calculate the source parameter residuals of each grid in the three-dimensional grid, and the grid with the smallest source parameter residual is taken as the final source location, including: For any grid in the three-dimensional grid, a weight matrix is ​​formed according to the observed variance of the propagation direction and local wavefront curvature of each monitoring station. The minimum variance unbiased estimation algorithm is used to calculate the residual of the source parameters corresponding to the grid, and the grid with the smallest residual is selected as the final source position.

[0012] The beneficial effects of the present invention are: 1. By acquiring the original fluctuation signal based on M stations, extracting four key abnormal data types, namely data delay, server timing, network transmission flow and zero drift, and outputting an alarm when the abnormal data synchronization exceeds the limit, it can fully cover the abnormal conditions in the signal acquisition, transmission and processing links, realize real-time capture of earthquake system signal anomalies, and thus quickly trigger an alarm.

[0013] 2. By constructing an outer ring, inner ring, fault and vertical station monitoring network that is adaptable to mountainous terrain, and combining Kirchhoff phase shift processing under the sliding window H to extract the propagation direction and local wavefront curvature, a full-process mechanism for closed wavefront curvature field inversion of earthquake sources is established. This can stably estimate the planar position and depth of the earthquake source without relying on traditional travel time difference and full-field velocity models, and further realize the earthquake source solution through the three-dimensional grid precise positioning process, thereby improving the response speed, positioning accuracy and reliability of the earthquake early warning system in complex terrain areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of an alarm method for real-time monitoring of abnormalities in an earthquake early warning station network according to the present invention; Figure 2 This is a module diagram of a positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to the present invention. DETAILED DESCRIPTION

[0015] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0016] Example 1: like Figure 1 As shown, an alarm method for real-time monitoring of abnormalities in an earthquake early warning station network includes: S1, obtain M groups of original fluctuation signals based on M stations; S2, extracts abnormal data from the original fluctuation signal, including data delay, server timing, network transmission flow and zero drift; S3: If the synchronization of abnormal data exceeds the limit, an alarm is triggered.

[0017] Specifically, data latency refers to the time delay between the original wave signal collected by the station's sensors and its transmission through the communication network to the data processing center. When an earthquake occurs, strong seismic waves can interfere with the communication link, or the station's transmission equipment can be temporarily overloaded by the vibration, significantly extending the signal transmission time beyond the normal preset range, resulting in abnormal data latency.

[0018] Specifically, server timing refers to the deviation between the local time of each monitoring station and the central server's standard time. During an earthquake, violent crustal vibrations can affect the time synchronization modules of stations or servers, causing the timestamps of the raw wave signals recorded by each station to deviate significantly from the server's standard time, exceeding the normal accuracy range and causing server timing anomalies.

[0019] Specifically, network traffic refers to the amount of data transmitted per unit time between stations and data processing centers, and its stability. During an earthquake, station sensors detect dramatic fluctuations (sudden changes in amplitude and frequency), resulting in a sudden surge in data volume. Furthermore, earthquakes can cause vibration failures in network equipment (such as routers and switches), leading to transmission interruptions or data loss, and sudden drops in traffic. Such abnormal fluctuations (surges or drops) in traffic that exceed normal ranges are considered network traffic anomalies.

[0020] Specifically, zero drift refers to the phenomenon in which the output signal of a seismic sensor deviates from its reference zero value when static (no significant vibration). During an earthquake, strong ground vibrations can temporarily shift the sensor's internal mechanical structures (such as the pendulum and piezoelectric element) or disrupt its performance. This can cause the offset to exceed the normal threshold, resulting in a zero drift anomaly and affecting the accurate interpretation of the original wave signal.

[0021] Specifically, when the four types of abnormal data, namely data delay, server timing, network transmission flow and zero point, exceed their respective preset thresholds within the same period, the system determines that it is a systematic signal abnormality caused by seismic activity and outputs an earthquake alarm. Specifically: data delay, server timing deviation, network transmission flow fluctuation, and zero point drift are all directly related to the occurrence of earthquakes: earthquake-induced crustal vibrations, electromagnetic interference or equipment shock will simultaneously affect signal transmission efficiency, time synchronization accuracy, data transmission stability and sensor baseline. Synchronization exceeding the limit indicates that the four types of abnormalities are not caused by a single equipment failure or accidental interference, but a systematic chain reaction caused by seismic activity. Triggering an alarm at this time can accurately respond to real earthquake events.

[0022] Example 2: like Figure 2 As shown, a positioning system for real-time monitoring of abnormalities in an earthquake early warning station network is applied to the above-mentioned method for real-time monitoring and alarming of abnormalities in an earthquake early warning station network, responding to an earthquake alarm and performing the following steps: The data acquisition module is used to perform outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring of the target mountain based on M stations to obtain M groups of original fluctuation signals; The data processing module is used to perform Kirchhoff phase shift processing on each set of original wave signals using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station; The positioning estimation module is used to construct a closed wavefront curvature field based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, and to estimate the wavefront estimated radius, the estimated coordinates of the source plane and the estimated depth of the source center in sequence for the closed wavefront curvature field; The precise positioning module is used to construct a three-dimensional grid with the estimated coordinates of the source plane and the estimated depth of the source center as the initial position; for any grid in the three-dimensional grid, the source parameter residual of each grid in the three-dimensional grid is calculated based on the propagation direction and local wavefront curvature of each monitoring station, combined with the minimum variance unbiased estimation algorithm, and the grid with the smallest source parameter residual is taken as the final source position.

[0023] In one embodiment of the present invention, outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring include: Outer ring monitoring includes: constructing a first ring-shaped monitoring array around the ridge of the target mountain; Inner ring monitoring includes: constructing a second monitoring array in a ring around the basin of the target mountain area; Fault monitoring includes: constructing a linear third monitoring array at fixed horizontal intervals based on the active faults in the target mountain area; Vertical monitoring includes: constructing a linear fourth monitoring array at fixed vertical intervals from the ridge of the target mountain to the basin.

[0024] Specifically, a circular monitoring array is constructed around the ridge of the target mountain. Ridge contours are extracted using a digital elevation model, and a closed curve (the outer monitoring ring) is fitted. Monitoring stations are then deployed at equal intervals based on an average arc length (e.g., 8 km). Due to the high altitude and shallow bedrock of the ridge, the ambient noise power spectrum is lower than that of the valley floor. This circular layout ensures that seismic P waves from any incident direction are simultaneously observed by multiple monitoring stations.

[0025] A circular array is constructed around a basin at the base of a mountain. The basin boundary is fitted using a digital elevation model and remote sensing imagery. Monitoring stations are then deployed at equal intervals based on an average arc length (e.g., 5 km). Low-velocity sediments in the basin amplify the high-frequency components of P waves.

[0026] A linear array is constructed at fixed horizontal intervals (e.g., 2 km) along the strike of an active fault in the target mountainous area. The fault's centerline is extracted from the geological map and digital elevation model, and the total length of the fault is calculated before equally spaced monitoring stations are assigned. The linear array can capture travel time differences, rupture velocity, and extension direction at the rupture front.

[0027] From the ridge to the basin, a vertical profile array is constructed at fixed vertical intervals (e.g., 0.625 m). Elevation zones are divided by calculating the vertical height difference from the ridge to the basin. A profile line perpendicular to the ridge is selected to extract the coordinates of the monitoring stations corresponding to each elevation zone.

[0028] Mountainous terrain is highly rugged, and a single array cannot provide omnidirectional sensing, spectrum coverage, fault tracking, and vertical segmentation. The raw waveform data collected by the four-layer array achieves spatial complementarity and data redundancy.

[0029] It should be noted that the original fluctuation signal refers to the unprocessed three-component (east-west, north-south, and vertical) ground vibration time history data directly collected by the sensors of the monitoring station.

[0030] In one embodiment of the present invention, Kirchhoff phase shift processing is performed on each set of original wave signals using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station, including: Each group of original fluctuation signals is segmented according to the preset sliding window H to obtain K sub-signals, and each sub-signal is subjected to mean and trend removal processing to obtain a standard fluctuation signal; Initialize the local wavefront model as follows: Establish a three-dimensional model of the target mountain; perform spherical fitting on the three-dimensional model to obtain a local wavefront model, and determine the spherical center coordinates of the local wavefront model; Based on the local wavefront model and the coordinates of the spherical center, the prior P-wave velocity and the sampling interval of the standard wave signal, the standard wave signal is phase-shifted according to a preset time delay to obtain a migration wave signal; Determine the center coordinates of the annular first monitoring array (i.e., the centroid of the outer monitoring ring) and connect the center coordinates to each monitoring station in the first monitoring array to form a set of candidate propagation directions; superimpose M groups of migration fluctuation signals on each candidate propagation direction to determine the superposition strength of each candidate propagation direction, and select the candidate propagation direction with the highest superposition strength as the propagation direction; A local coordinate system is constructed with the propagation direction as the normal, and the station coordinates of M monitoring stations are fitted with a surface in the local coordinate system to obtain the local wavefront curvature of each monitoring station.

[0031] It should be noted that the candidate propagation direction The calculation formula of the superposition strength is as follows: in, Indicates the time sampling point index, represents the component index of the migration fluctuation signal, Indicates the monitoring station index, represents the i-th monitoring station, Quantity, time Migration fluctuation signal.

[0032] It should be noted that the local coordinate system includes three axes: 、 and ; in, represents an arbitrarily chosen non-zero vector, , Indicates the direction of propagation.

[0033] For the coordinates of the mth monitoring station in the local coordinate system , the surface fitting is as follows: in, represents the local wavefront curvature of the mth monitoring station and is solved by the least squares method .

[0034] Specifically, the original fluctuation signal is sliced ​​using a fixed window length H (e.g., 1 second) to generate K sub-signals. The DC offset (de-averaging) and linear drift (de-trending) of each sub-signal are then removed segment by segment, resulting in a standardized fluctuation signal. This improves the signal-to-noise ratio (SNR) of subsequent phase shifts. De-averaging and de-trending can be performed using a DC offset correction algorithm (de-averaging) and a linear regression detrending algorithm (de-trending), respectively.

[0035] A three-dimensional spatial grid model incorporating the terrain's undulations was constructed based on the digital elevation model, station coordinates, and topographic and geological data. Within the local mountain scale, the terrain surface was approximated using a sphere using the least squares method to obtain a local wavefront model and the coordinates of the sphere's center.

[0036] The preset delay of the jth standard fluctuation signal The calculation of is as follows: in, represents the station coordinates of the jth monitoring station, represents the coordinates of the sphere center, represents the propagation speed of the P wave, preferably 8 kilometers per second; according to The standard fluctuation signal is time-domain shifted to generate a migration fluctuation signal.

[0037] In one embodiment of the present invention, a closed wavefront curvature field is constructed based on the local wavefront curvature of the station corresponding to the outer ring monitoring, including: Extract the geometric curve of the ridge of the target mountain to construct the monitoring outer ring (ensure the geometric closure of the outer ring and provide spatial boundary constraints for the curvature field); A virtual monitoring station will be inserted at the midpoint of adjacent monitoring stations in the outer monitoring ring. The local wavefront curvature of the virtual station is the average of the local wavefront curvatures of the adjacent monitoring stations (the sampling points are encrypted to suppress curvature aliasing caused by excessive azimuth spacing and improve subsequent fitting accuracy); Repeatedly insert virtual monitoring stations until the number of monitoring stations reaches a preset threshold to form a local wavefront curvature set (providing sufficiently dense curvature samples to meet the grid density requirements for continuous function fitting and avoid undersampling errors); The local wavefront curvature set is fitted to obtain the curvature function as a closed wavefront curvature field (the discrete curvature samples are converted into a smooth, closed analytical field, providing a differentiable mathematical basis for subsequent wavefront radius estimation and source location).

[0038] It should be noted that a contour tracing algorithm was used to extract discrete ridge points from the digital elevation model. These points were then connected to form a closed geometric curve using spline interpolation. The monitoring stations in the outer ring were projected onto this closed geometric curve (the outer monitoring ring).

[0039] The preset quantity threshold is based on expert settings.

[0040] The periodic cubic spline interpolation algorithm is used to fit the local wavefront curvature set to generate a closed wavefront curvature field. , , represents any point in a closed geometric curve, is a continuously differentiable function.

[0041] In one embodiment of the present invention, the closed wavefront curvature field is sequentially estimated to estimate the wavefront radius, the source plane coordinates, and the source center depth, including: The wavefront estimation radius is determined based on the circumferential average curvature of the closed wavefront curvature field (local curvature fluctuations are eliminated by circumferential averaging, and the wavefront estimation radius is used to characterize the degree of overall wavefront curvature); Loading a preset threshold number of wavefront estimation radii within the monitoring outer ring, and obtaining the source plane estimation coordinates by minimizing the intersection of the wavefront estimation radii; Determine the mean time of the timestamps of the original fluctuation signals of the first monitoring array and the second monitoring array, and calculate the time difference between the mean times (the mean of the time difference between the inner and outer rings is used to suppress the noise interference of a single station. The vertical time is sensitive to the focal depth and provides a key time parameter for depth inversion). Determine the vertical propagation time based on the time difference of the mean time; Calculate the estimated depth of the earthquake center ,as follows: in, represents the propagation speed of the P wave, represents the vertical propagation time, represents the wavefront estimation radius, It represents the average value of the Euclidean distance between each monitoring station in the first monitoring array and the estimated coordinates of the source plane.

[0042] It should be noted that the circumferential average curvature is calculated based on the circumferential integral average ,Will Converted to wavefront estimation radius: ; It should be noted that one end of the wavefront estimation radius is connected to the monitoring station, and the other end is located inside the closed geometric curve. The position of the other end of each wavefront estimation radius is adjusted to minimize the intersection of the wavefront estimation radii (implemented by a genetic algorithm). Then, the other ends of all wavefront estimation radii are sequentially connected to form a closed geometric figure. The center of mass of the closed geometric figure is determined using the center of mass method and used as the estimated coordinate of the source plane.

[0043] It should be noted that the vertical propagation time is half of the time difference of the mean time.

[0044] It should be noted that Determined by spherical geometry.

[0045] In one embodiment of the present invention, a three-dimensional grid is constructed with the estimated coordinates of the earthquake source plane and the estimated depth of the earthquake source center as the initial position, including: A three-dimensional grid with a preset step size is constructed within a spatial range with the estimated coordinates of the earthquake source plane as the center, extending a first preset distance to the east, west, south and north, and with the estimated depth of the earthquake source center as the center, extending a second preset distance vertically up and down.

[0046] It should be noted that the estimated coordinates of the source plane are ; Horizontal range calculation: East-West: Left boundary , right boundary ; North-South: Lower Boundary , upper boundary ; Form a horizontal two-dimensional rectangular area based on the east-west and north-south directions: in, Indicates half of the first preset distance.

[0047] Vertical range calculation: Nether , upper bound ; The vertical range is: .

[0048] The preset step sizes include: x-direction step size , y-direction step length , z-direction step size ; In a horizontal 2D rectangular area, by step and Divide evenly; by step length within the vertical range Divide evenly to form a three-dimensional grid.

[0049] In one embodiment of the present invention, based on the propagation direction and local wavefront curvature of each monitoring station, the minimum square error unbiased estimation algorithm is used to calculate the source parameter residual of each grid in the three-dimensional grid, so that the grid with the smallest source parameter residual is used as the final source position, including: For any grid in the three-dimensional grid, a weight matrix is ​​formed according to the observed variance of the propagation direction and local wavefront curvature of each monitoring station. The minimum variance unbiased estimation algorithm is used to calculate the residual of the source parameters corresponding to the grid, and the grid with the smallest residual is selected as the final source position.

[0050] Specifically, obtain the line connecting the station coordinates of each monitoring station to the center coordinates of the first monitoring array, and calculate the cosine similarity between the line direction and the propagation direction. ; , is a positive integer; The station , local wavefront curvature and and The corresponding observation variance and , using the inverse of the observed variance as the weight, construct a diagonal weight matrix , ; Among them, the observation variance and Acquisition based on historical time periods; For the grid , calculate the theoretical cosine similarity of each station under the source hypothesis by ray tracing and local wavefront curvature .

[0051] Combine the differences between the observed and predicted values ​​into a residual vector .

[0052] The residual vector is weighted by the diagonal weight matrix to obtain the grid residual .

[0053] Traverse the grid residuals of all grids and select the grid with the smallest grid residual as the final source location.

[0054] A method for real-time monitoring and alarming of earthquake early warning station network anomalies, implementing any one of the positioning systems for real-time monitoring of earthquake early warning station network anomalies, comprising: Step 1: Perform outer ring monitoring, inner ring monitoring, fault monitoring, and vertical monitoring on the target mountain based on M stations to obtain M groups of original fluctuation signals; Step 2: For each set of original wave signals, Kirchhoff phase shift processing is performed using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station; Step 3: Construct a closed wavefront curvature field based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, and estimate the wavefront estimated radius, source plane estimated coordinates, and source center estimated depth of the closed wavefront curvature field in sequence; Step 4: Construct a three-dimensional grid with the estimated coordinates of the source plane and the estimated depth of the source center as the initial position; for any grid in the three-dimensional grid, calculate the source parameter residual of each grid in the three-dimensional grid based on the propagation direction and local wavefront curvature of each monitoring station, combined with the minimum variance unbiased estimation algorithm, and take the grid with the smallest source parameter residual as the final source position.

[0055] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for real-time monitoring of abnormalities in an earthquake early warning station network, characterized in that: include: S1, obtain M groups of original fluctuation signals based on M stations; S2, extracts abnormal data from the original fluctuation signal, including data delay, server timing, network transmission flow and zero drift; S3: If the synchronization of abnormal data exceeds the limit, an alarm is triggered.

2. A positioning system for real-time monitoring of earthquake early warning station network anomalies, applied to the method for real-time monitoring and alarming of earthquake early warning station network anomalies according to claim 1, characterized in that: In response to an earthquake warning, perform the following steps: The data acquisition module is used to perform outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring of the target mountain based on M stations to obtain M groups of original fluctuation signals; The data processing module is used to perform Kirchhoff phase shift processing on each set of original wave signals using a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station; The positioning estimation module is used to construct a closed wavefront curvature field based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, and to estimate the wavefront estimated radius, the estimated coordinates of the source plane and the estimated depth of the source center in sequence for the closed wavefront curvature field; A precise positioning module is used to construct a three-dimensional grid using the estimated coordinates of the earthquake source plane and the estimated depth of the earthquake source center as the initial position; For any grid in the three-dimensional grid, the source parameter residuals of each grid in the three-dimensional grid are calculated according to the propagation direction and local wavefront curvature of each monitoring station, combined with the minimum variance unbiased estimation algorithm, and the grid with the smallest source parameter residual is taken as the final source position.

3. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 2, characterized in that: Outer ring monitoring, inner ring monitoring, fault monitoring and vertical monitoring, including: Outer ring monitoring includes: constructing a first ring-shaped monitoring array around the ridge of the target mountain; Inner ring monitoring includes: constructing a second monitoring array in a ring around the basin of the target mountain area; Fault monitoring includes: constructing a linear third monitoring array at fixed horizontal intervals based on the active faults in the target mountain area; Vertical monitoring includes: constructing a linear fourth monitoring array at fixed vertical intervals from the ridge of the target mountain to the basin.

4. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 3, characterized in that: For each set of original wave signals, Kirchhoff phase shift processing is performed with a preset sliding window H to obtain the propagation direction and local wavefront curvature of each monitoring station, including: Each group of original fluctuation signals is segmented according to the preset sliding window H to obtain K sub-signals, and each sub-signal is subjected to mean and trend removal processing to obtain a standard fluctuation signal; Initialize the local wavefront model as follows: Establish a three-dimensional model of the target mountain; perform spherical fitting on the three-dimensional model to obtain a local wavefront model, and determine the spherical center coordinates of the local wavefront model; Based on the local wavefront model and the coordinates of the spherical center, the prior P-wave velocity and the sampling interval of the standard wave signal, the standard wave signal is phase-shifted according to a preset time delay to obtain a migration wave signal; Determine the center coordinates of the annular first monitoring array, connect the center coordinates to each monitoring station in the first monitoring array, and form a set of candidate propagation directions; superimpose the M groups of migration fluctuation signals in each candidate propagation direction to determine the superposition strength of each candidate propagation direction, and select the candidate propagation direction with the highest superposition strength as the propagation direction; A local coordinate system is constructed with the propagation direction as the normal, and the station coordinates of M monitoring stations are fitted with a surface in the local coordinate system to obtain the local wavefront curvature of each monitoring station.

5. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 4, characterized in that: The closed wavefront curvature field is constructed based on the local wavefront curvature of the stations corresponding to the outer ring monitoring, including: Extract the geometric curve of the ridge of the target mountain to construct the monitoring outer ring; A virtual monitoring station is inserted at the midpoint of adjacent monitoring stations in the outer monitoring ring, and the local wavefront curvature of the virtual station is the average value of the local wavefront curvature of the adjacent monitoring stations; Repeatedly inserting virtual monitoring stations until the number of monitoring stations reaches a preset threshold value to form a local wavefront curvature set; The local wavefront curvature set is fitted to obtain a curvature function as a closed wavefront curvature field.

6. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 5, characterized in that: The closed wavefront curvature field is estimated in sequence to estimate the wavefront radius, the source plane coordinates and the source center depth, including: determining the wavefront estimation radius based on the annular average curvature of the closed wavefront curvature field; Loading a preset threshold number of wavefront estimation radii within the monitoring outer ring, and obtaining the source plane estimation coordinates by minimizing the intersection of the wavefront estimation radii; Determine the mean time of the timestamps of the original fluctuation signals of the first monitoring array and the mean time of the timestamps of the original fluctuation signals of the second monitoring array, and calculate the time difference between the mean times; Determine the vertical propagation time based on the time difference of the mean time; Calculate the estimated depth of the earthquake center ,as follows: in, represents the propagation speed of the P wave, represents the vertical propagation time, represents the wavefront estimation radius, It represents the average value of the Euclidean distance between each monitoring station in the first monitoring array and the estimated coordinates of the source plane.

7. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 6, characterized in that: Using the estimated coordinates of the source plane and the estimated depth of the source center as the initial position, a three-dimensional grid is constructed, including: A three-dimensional grid with a preset step size is constructed within a spatial range with the estimated coordinates of the earthquake source plane as the center, extending a first preset distance to the east, west, south and north, and with the estimated depth of the earthquake source center as the center, extending a second preset distance vertically up and down.

8. A positioning system for real-time monitoring of abnormalities in an earthquake early warning station network according to claim 7, characterized in that: Based on the propagation direction and local wavefront curvature of each monitoring station, the minimum variance unbiased estimation algorithm is used to calculate the source parameter residuals of each grid in the three-dimensional grid, and the grid with the smallest source parameter residual is taken as the final source location, including: For any grid in the three-dimensional grid, a weight matrix is ​​formed according to the observed variance of the propagation direction and local wavefront curvature of each monitoring station. The minimum variance unbiased estimation algorithm is used to calculate the residual of the source parameters corresponding to the grid, and the grid with the smallest residual is selected as the final source position.

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