Data analysis method, device and system of earthquake early warning network and medium

Abstract

The invention is suitable for the technical field of data processing, and provides a data analysis method, device and system of an earthquake early warning network and a medium, the method comprises the following steps: obtaining observation data of different observation instruments in the earthquake early warning network, the different observation instruments including at least two types of observation instruments; carrying out observation point matching on any two types of observation instruments according to the position of each observation instrument to obtain a plurality of groups of observation point pairs; extracting a characteristic quantity from the observation data of each observation point, and judging the observation difference of each group of observation point pairs according to the characteristic quantity; determining the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs; and determining a correction strategy of observation data of different observation instruments in the earthquake early warning network based on observation differences of all different types of observation instruments. According to the invention, the difference of observation data of different monitoring instruments in the earthquake early warning network can be analyzed, so that data correction is guided, and the accuracy of earthquake early warning is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a data analysis method, device, system and medium for an earthquake early warning network. Background Art

[0002] Earthquake early warning technology uses a dense network of earthquake monitoring stations near the epicenter to monitor earthquakes in real time. After an earthquake occurs, the three elements of the earthquake and the ground motion impact field are quickly estimated based on the seismic wave information first obtained by the stations near the epicenter. The principle that the propagation speed of electromagnetic waves is much faster than that of seismic waves is used to issue early warning information before the destructive seismic waves reach the target area. Earthquake early warning can provide people with more time to avoid danger and is an important means to reduce the loss of life and property.

[0003] In the related art, the earthquake early warning network is composed of three types of instruments: seismometers, strong motion meters, and intensity meters with different deployment conditions. The inventors of the present application found that seismometers are usually deployed on bedrock, strong motion meters and intensity meters are usually deployed in soil layers, and in actual engineering construction, intensity meters are usually deployed on the ground of houses next to base stations. Due to the different deployment conditions of the three types of instruments, differences in observation data may occur, resulting in inconsistent or even contradictory observation results. Therefore, accurately analyzing the differences between the three types of monitoring instruments is crucial to guiding data correction and improving the accuracy of earthquake early warning. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a data analysis method, device, system and medium for an earthquake early warning network to analyze the differences in observation data of different monitoring instruments in the earthquake early warning network, thereby guiding data correction and improving the accuracy of earthquake early warning.

[0005] A first aspect of an embodiment of the present invention provides a data analysis method for an earthquake early warning network, the method comprising: Acquiring observation data of different observation instruments in an earthquake early warning network, wherein the different observation instruments include at least two types of observation instruments; For any two types of observation instruments, the observation points are matched according to the positions of each observation instrument to obtain multiple groups of observation point pairs; Extracting characteristic quantities from the observation data of each observation point, judging the observation difference of each pair of observation points according to the characteristic quantities; determining the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points; Based on the observation differences of all different types of observation instruments, a correction strategy for the observation data of different observation instruments in the earthquake early warning network is determined.

[0006] In a possible implementation manner of the first aspect, performing observation point matching on any two types of observation instruments according to positions of the observation instruments to obtain multiple groups of observation point pairs includes: For any observation point of one type, select the observation point with the smallest distance less than a preset threshold from each observation point of another type to form a set of observation point pairs; The preset threshold is determined according to the distance between the two selected observation points and the epicenter.

[0007] In a possible implementation manner of the first aspect, extracting a feature value from the observation data of each observation point, and judging the observation difference of each pair of observation points according to the feature value, includes: Extract the peak acceleration from the observation data of each observation point, calculate the difference between the peak accelerations corresponding to the two observation points in each pair of observation points, and obtain the observation difference of each pair of observation points; Accordingly, determining the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs includes: The average value of the difference between the peak accelerations of each group of observation point pairs is calculated to obtain a statistic, and the observation difference between the two types of observation instruments is determined based on the statistic.

[0008] In a possible implementation manner of the first aspect, determining the observation difference between the two types of observation instruments according to the statistic includes: Determine the distribution of statistics through permutation experiments; Determining the confidence level of the statistic according to the statistic and the statistic distribution; If the confidence level is less than a preset confidence level threshold, it is determined that there is an observation difference between the two types of observation instruments; otherwise, it is determined that there is no observation difference between the two types of observation instruments.

[0009] In a possible implementation manner of the first aspect, the preset threshold is determined according to the following method: Calculate the average distance between the two selected observation points and the epicenter; The preset threshold is determined according to the distance average value; wherein the preset threshold is negatively correlated with the distance average value.

[0010] In a possible implementation manner of the first aspect, after acquiring observation data of different observation instruments in the earthquake early warning network, the method further includes: The observation data from different observation instruments are preprocessed and converted into the same type of data.

[0011] In a possible implementation manner of the first aspect, the at least two types of observation instruments include: a seismometer, a strong motion detector, and an intensity meter.

[0012] A second aspect of an embodiment of the present invention provides a data analysis device for an earthquake early warning network, the device comprising: An acquisition module, used to acquire observation data of different observation instruments in the earthquake early warning network, wherein the different observation instruments include at least two types of observation instruments; A matching module is used to match the observation points of any two types of observation instruments according to the positions of each observation instrument to obtain multiple groups of observation point pairs; A calculation module, used to extract characteristic quantities from the observation data of each observation point, and determine the observation difference of each pair of observation points according to the characteristic quantities; and determine the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points; The processing module is used to determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network based on the observation differences of all different types of observation instruments.

[0013] The third aspect of an embodiment of the present invention provides an earthquake early warning network system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method in the first aspect or any one of the implementations of the first aspect are implemented.

[0014] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method in the above-mentioned first aspect or any one of the implementation methods of the first aspect.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In view of the problem that the observation instruments and deployment conditions of the earthquake early warning network are diverse, and the observation data of different observation instruments may be different, the embodiment of the present invention first matches the observation points of any two types of observation instruments according to the positions of each observation instrument to obtain multiple groups of observation point pairs to ensure the effectiveness of the comparison; then, extract the characteristic quantity from the observation data of each observation point, and judge the observation difference of each group of observation point pairs according to the characteristic quantity; determine the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs; finally, based on the observation difference of all different types of observation instruments, determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network. The present invention can accurately detect the data differences caused by different deployment conditions of different types of observation instruments, provide a basis for the correction of observation data, and thus ensure the accuracy of earthquake early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 It is a schematic flow chart of a data analysis method for an earthquake early warning network provided by an embodiment of the present invention; Figure 2 Schematic diagram of the change of PGA of the observation point of the seismometer and the intensity meter according to the epicenter distance provided by the embodiment of the present invention, wherein: Figure 2 (a) is the north-south direction diagram, Figure 2 (b) is the east-west direction map, Figure 2 (c) is the vertical direction diagram; Figure 3 is the distribution of the permutation experiment results of the mean difference of the matching data of the seismometer and the intensity meter provided in the embodiment of the present invention, wherein: Figure 3 (a) is the north-south direction diagram, Figure 3 (b) is the east-west direction map, Figure 3 (c) is the vertical direction diagram; Figure 4 Schematic diagram of the change of PGA of the observation point of the seismometer and the strong motion instrument according to the embodiment of the present invention with the epicenter distance, wherein: Figure 4 (a) is the north-south direction diagram, Figure 4 (b) is the east-west direction map, Figure 4 (c) is the vertical direction diagram; Figure 5 is the distribution of permutation test results of the mean difference of matching data between the seismometer and the strong motion instrument provided by the embodiment of the present invention, wherein: Figure 5 (a) is the north-south direction diagram, Figure 5 (b) is the east-west direction map, Figure 5 (c) is the vertical direction diagram; Figure 6 Schematic diagram of the change of PGA of the observation point of the intensity meter and the strong motion instrument according to the embodiment of the present invention with the epicenter distance, wherein: Figure 6 (a) is the north-south direction diagram, Figure 6 (b) is the east-west direction map, Figure 6 (c) is the vertical direction diagram; Figure 7 is the distribution of the permutation test result of the mean difference of the matching data of the intensity meter and the strong motion instrument provided by the embodiment of the present invention, wherein: Figure 7 (a) is the north-south direction diagram, Figure 7 (b) is the east-west direction map, Figure 7 (c) is the vertical direction diagram; Figure 8 It is a structural schematic diagram of a data analysis device for an earthquake early warning network provided by an embodiment of the present invention; Fig. 9 It is a structural diagram of an earthquake early warning network system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0019] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0020] In related technologies, the earthquake early warning network is composed of a large number of base stations, basic stations and general stations, of which 70% are general stations composed of intensity meters, and more rapid response tasks need to be completed by intensity meter data. However, since the deployment conditions of intensity meters are completely different from those of traditional seismometers, the large-scale use of intensity meters has improved the early warning response speed while also bringing new problems to the use of early warning data.

[0021] The physical quantities observed and the deployment site conditions corresponding to various instruments are shown in Table 1. The broadband seismometers of the base station are generally deployed on the bedrock (or underground) in a free field to reduce the impact of noise; to prevent the limitation of seismic instruments near the epicenter of a large earthquake, a strong earthquake instrument is deployed at the seismometer station at the same time, and the deployment conditions of the two types of instruments are the same; the deployment conditions of other strong earthquake observation points are free field soil layer observation. In order to simplify the installation of the intensity meter, the intensity meter is uniformly installed in a small room next to the communication base station, generally on the ground in the room. If there is no level ground, it is installed on the wall, but the height does not exceed 30cm.

[0022] Table 1 Observation point information of different types of instruments

[0023] It can be seen that the deployment conditions of the three types of instruments are different. Ignoring the differences in instrument performance (because the warning data used is as close to the epicenter as possible, the seismic signal is much larger than the noise background of the instrument, so the data differences caused by instrument performance can be ignored), the deployment site conditions will cause differences in observation data. How to confirm the impact of this difference on the calculation of earthquake warning parameters and perform consistency correction when using seismic data from instruments with different deployment methods is an urgent problem to be solved.

[0024] See also Figure 1 As shown, an embodiment of the present invention provides a data analysis method for an earthquake early warning network, comprising: Step S101, obtaining observation data of different observation instruments in an earthquake early warning network, where the different observation instruments include at least two types of observation instruments.

[0025] In this embodiment, the at least two types of observation instruments may include but are not limited to: a seismometer, a strong motion detector and an intensity meter.

[0026] In the earthquake early warning network, multiple observation instruments of each type are deployed. Each observation instrument serves as an observation point and obtains a series of observation data.

[0027] Among them, the observation data of the seismometer is velocity, and the observation data of the strong earthquake instrument and intensity meter is acceleration. In order to facilitate comparative analysis, the observation data of different observation instruments can be preprocessed and converted into the same type of data. For example, differential operation can be performed on the observation data of the seismometer.

[0028] Step S102: for any two types of observation instruments, observation point matching is performed according to the positions of the respective observation instruments to obtain multiple groups of observation point pairs.

[0029] In order to confirm whether there are significant differences between the observation data of seismometers, strong motion sensors and intensity meters due to the influence of deployment conditions, this embodiment matches the observation points according to the positions of each observation instrument to eliminate the influence of other factors (such as epicentral distance, geological and topographic conditions near the observation point, seismic wave propagation path, propagation direction, etc.).

[0030] Optionally, this step can be implemented by: For any observation point of one type, the observation point with the smallest distance from it and a distance less than a preset threshold is selected from each observation point of another type to form a set of observation point pairs; wherein the preset threshold is determined according to the distance between the two selected observation points and the epicenter.

[0031] In this embodiment, the matching method is to make the observation points of the two types of observation instruments as close as possible, and the threshold of the degree of closeness between them is related to the distance between the observation points and the epicenter. For example, the distance between the two observation points is less than 1 / 6 of the epicenter distance, and the two points with the smallest distance are selected from all pairs that meet the conditions as the matching points of the two types of instruments. Thus, all observation points of the two types of instruments that can be matched are searched to form observation point pairs. Since the two observation points are close enough, the impact of the epicenter distance, propagation path, propagation direction, etc. on the data is basically the same, and only the deployment conditions will affect the data.

[0032] The preset threshold value may be determined in the following manner: calculating the average distance between the two selected observation points and the epicenter; and determining the preset threshold value based on the average distance value.

[0033] This embodiment does not limit the specific calculation formula of the preset threshold. However, it should be pointed out that the preset threshold is negatively correlated with the average distance. That is, when the distance between the observation point and the epicenter is far, the accuracy of the data decreases, and the observation point pair that needs to be selected is closer to improve the accuracy of the comparison.

[0034] Step S103, extracting feature quantities from the observation data of each observation point, judging the observation difference of each pair of observation points according to the feature quantities; and determining the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points.

[0035] Since the waveform data of earthquake events recorded by observation instruments are relatively complex, in order to compare the two sets of data, it is necessary to extract feature quantities from the event waveforms and use the feature quantities to represent the observation data for subsequent data analysis. Peak acceleration (PGA) is the most commonly used feature quantity representing the earthquake amplitude characteristics. Here, PGA is used as the feature quantity of the event waveform to construct two sets of matching data. Of course, other feature quantities can also be selected, such as peak velocity (PGV), peak displacement (PGD), root mean square acceleration (RMS), etc. However, the calculation and extraction of these feature quantities are relatively complex, and here only PGA is used as an application example.

[0036] Optionally, a feature quantity is extracted from the observation data of each observation point, and the observation difference of each pair of observation points is determined based on the feature quantity, including: The peak acceleration is extracted from the observation data of each observation point, and the difference in the peak accelerations corresponding to the two observation points in each pair of observation points is calculated to obtain the observation difference of each pair of observation points.

[0037] Accordingly, according to the observation difference of each set of observation point pairs, the observation difference of the two types of observation instruments is determined, including: Calculate the average of the differences in peak acceleration for each pair of observation points M Diff, and obtain a statistic, based on which the observed difference between the two types of observation instruments is determined.

[0038] M Diff The calculation method is as follows:

[0039] In the formula, n Indicates the number of matching points. k and j They represent two different deployment modes.

[0040] Comparing the differences between the two types of observation instruments is based on M Diff As a statistic, when the statistic is large enough or small enough, it indicates that there is a significant difference between the two sets of data. The basis for judging whether the statistic is large enough or small enough is the position of the actual observed statistic in the statistical distribution. If the actual observed statistic deviates from the position where the vast majority of statistical distributions are located, that is, the probability of the actual observed statistic is less than a certain confidence level (such as 5%), then it is considered that the probability of the actual observed statistic is extremely small, and there is a significant difference between the two sets of data; otherwise, the difference between the two is considered insignificant.

[0041] The permutation experiment is a means of obtaining the distribution of statistics. Its basic idea is: assuming that there is no difference between the two groups of data, the two groups of data are randomly regrouped to obtain a possible value of the statistic. The distribution of the statistic can be obtained by repeated sampling using a computer. Then observe the position of the actual observed statistic on the statistical distribution obtained by the permutation experiment. If the probability of the actual observed statistic is extremely small (less than the confidence level set in advance), it can be confirmed that the assumption that there is no difference between the two groups of data is not valid, and there is actually a difference between the two groups, because if there is no difference between the two groups of data, the probability of the actual observed statistic repeating should be very large.

[0042] Compare the actual observed statistics with the statistical distribution obtained by the permutation experiment, and use the 5% confidence level to test whether there is a significant difference between the two groups of data. If the probability of the actual observed statistics is less than the set confidence level, there is a significant difference between the two groups of data; otherwise, there is no significant difference between the two groups of data.

[0043] Step S104, based on the observation differences of all different types of observation instruments, a correction strategy for the observation data of different observation instruments in the earthquake early warning network is determined.

[0044] By analyzing the impact of deployment conditions on observation data, it is possible to determine whether the observation data of the earthquake early warning network needs to be corrected, and it is also possible to guide the correction of the observation data. When different types of observation instruments are used together, the question of how to connect them has important indicative significance. Since the correction strategy is not the focus of this embodiment, it is not described in detail here.

[0045] It can be seen that in view of the fact that the observation instruments and deployment conditions of the earthquake early warning network are diverse, and the observation data of different observation instruments may be different, the embodiment of the present invention first matches the observation points of any two types of observation instruments according to the positions of each observation instrument to obtain multiple groups of observation point pairs to ensure the effectiveness of the comparison; then, extract the characteristic quantity from the observation data of each observation point, and judge the observation difference of each group of observation point pairs according to the characteristic quantity; determine the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs; finally, based on the observation difference of all different types of observation instruments, determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network. The present invention can accurately detect the data differences caused by different deployment conditions of different types of observation instruments, provide a basis for the correction of observation data, and thus ensure the accuracy of earthquake early warning.

[0046] The solution of the present application is described below through a specific embodiment.

[0047] There are 1606 earthquake early warning stations in a loess region. The observed physical quantities and deployment site conditions of various instruments are shown in Table 1. Among them, there are 97 seismometers; 97 of the 399 strong motion observation points are deployed in parallel with seismometers, and the deployment conditions of the two types of instruments are the same. The deployment conditions of the other 302 strong motion observation points are free-field soil layer observation; there are 1110 intensity meters.

[0048] (1) According to the conditions for searching matching points in the above embodiment, 97 seismometer observation points and 1110 intensity meter observation points were searched to obtain 93 groups of matching seismometer and intensity meter stations. Since the seismometer observation quantity is velocity, it is necessary to perform a differential operation first when comparing it with the intensity meter acceleration, and then extract the PGA value. In addition, there is a limiting problem for seismic records near the epicenter. The matching constraint conditions given in this embodiment limit the selection of stations near the epicenter. After checking the original waveform records, there is no limiting data. The scatter plot of the PGA obtained from the seismometer and intensity meter matching stations as the epicenter distance changes is shown in the figure below: Figure 2 As shown (in the figure, N, E, and Z represent north-south, east-west, and vertical directions respectively), Figure 2 (a) Figure 2 (b) and Figure 2 The horizontal axis of (c) is the epicentral distance (unit: km), and the vertical axis is PGA (unit: gal).

[0049] from Figure 2 The scatter plot of PGA versus epicentral distance clearly shows the changing pattern of the two groups of PGA as the epicentral distance increases. For some observation points, the PGA obtained by the intensity meter is significantly greater than the PGA of the seismometer, but there are also some observation points where the difference between the two is not obvious. For most observation points, the PGAs observed by the two instruments are mixed together, making it difficult to distinguish whether the PGA of one instrument is clearly greater than that of the other. The two groups of matching data were subjected to 10,000 permutation experiments using the method described in the above embodiment, with the mean difference as the statistic, and the statistical distribution of the three-way PGA permutation experiment was obtained as shown in the figure. Figure 3 As shown (in the figure, N, E, and Z are the same as before, representing north-south, east-west, and vertical directions; the two dotted lines in the figure represent the corresponding statistical positions at a 2.5% confidence level, and the solid line represents the position of the statistical mean difference calculated from the actual observed data), Figure 3 (a) Figure 3 (b) and Figure 3 The horizontal axis of (c) is the mean difference, and the vertical axis is the number. The actual observed statistics and their occurrence probability levels obtained from the permutation experiment results are shown in Table 2.

[0050] Table 2 Actual observed statistics and occurrence probability of seismometer and intensity meter matching points

[0051] Figure 3 The statistics in the three directions of north-south, east-west, and vertical, that is, the average value of the difference between the matching PGAs, all show obvious normal distributions when obtained through 10,000 permutation experiments, assuming that there is no difference between the two sets of data. Figure 3 The actual observed statistics indicated in the figure, that is, the solid line in the figure, correspond to the M Diff The value has never appeared in 10,000 permutation experiments, and the p-values ​​(probability of occurrence of the actual observed statistic) of each item in Table 2 are all less than one in ten thousand. This shows that there is a significant difference between the two sets of matching data, namely the seismometer and intensity meter PGA. Figure 3The statistical distribution diagram shows the positions of the 2.5% confidence level on both sides (the dotted lines in the figure are combined into a 5% confidence level), because here we only consider whether there is a significant difference between the two sets of data. The actual observed statistics appearing outside the dotted line indicate that there is a significant difference. If you need to consider the significant difference between the two sets of data greater than or less than, you can use a one-sided 5% confidence level for testing. In fact, even if you use a one-sided 5% confidence level to test whether the intensity meter PGA is significantly greater than the seismometer PGA, you can still get a conclusion that there is a significant difference, because the average value of the difference between the intensity meter and the seismometer has a probability of less than one in ten thousand in 10,000 permutation experiments. Therefore, a relatively accurate conclusion can be drawn: at a 5% confidence level, the intensity meter PGA is significantly greater than the seismometer PGA.

[0052] (2) When comparing seismic data and strong earthquake data, it is necessary to exclude the situation where the two are deployed under the same deployment conditions, that is, the situation where strong earthquake instruments are deployed at the same time at the seismometer observation point. Here, only the data of seismometers deployed on bedrock and strong earthquake instruments deployed in soil layers are compared. 89 groups of matching seismometer stations and strong earthquake instrument stations that meet the conditions were searched out of 302 strong earthquake instruments and 97 seismometers. The scatter plot of the two groups of matching data PGA changes with the epicenter distance is shown in Figure 4 As shown, Figure 4 (a) Figure 4 (b) and Figure 4 The horizontal axis of (c) is the epicentral distance (unit: km), and the vertical axis is PGA (unit: gal).

[0053] Similar to matching data between seismometers and intensity meters, Figure 4 In the data of seismometer and strong motion detector, some data show that the PGA of strong motion detector is greater than that of seismometer, but in most cases, the two sets of matching data points are mixed together and difficult to distinguish. Therefore, a permutation experiment is conducted on the two sets of data.

[0054] Construct the matching data of seismometer and strong motion detector, and conduct 10,000 replacement experiments to obtain the distribution of the average value of the difference between the strong motion detector and the seismometer PGA. Figure 5 The distribution of the three-dimensional statistics and the actual observed statistical locations are given. Figure 5 (a) Figure 5 (b) and Figure 5 The horizontal axis of (c) is the mean difference, and the vertical axis is the number. Table 3 gives the actual observed statistics and their occurrence probability p.

[0055] Table 3 Actual statistics and occurrence probability of seismometer and strong motion detector matching data

[0056] according to Figure 5, the average value of the difference between the PGA of the strong seismometer and the seismometer, and its distribution in the three directions is still normally distributed. The average value of the difference between the actual observed PGA in the three directions, that is, the statistic used in this embodiment, is located at the extreme right end of the 2.5% confidence level on the right, indicating that the probability of the actual observed statistic is much smaller than the confidence level set in advance. According to the actual observed statistics given in Table 3 and the performance that the probability of the statistic appearing in 10,000 permutation experiments is less than 5 / 10,000, it shows that the PGA observed by the strong seismometer and the seismometer is significantly different. Moreover, the actual observed statistics in the three directions are all positive. When using a one-sided 5% confidence level, it can be tested that the PGA of the strong seismometer is greater than that of the seismometer. Therefore, according to the test results of the method of this embodiment, it can be confirmed that the PGA of the strong seismometer is greater than the PGA data of the seismometer under the same observation background but different deployment conditions.

[0057] (3) We searched for matching sites for 302 strong earthquake stations deployed in the soil layer and 1110 intensity meters deployed in small houses next to the base stations, and obtained 281 sets of matching data. The scatter plot of the matching PGA data with the change of epicenter distance is shown in Figure 6 As shown, Figure 6 (a) Figure 6 (b) and Figure 6 The horizontal axis of (c) is the epicentral distance (unit: km), and the vertical axis is PGA (unit: gal).

[0058] from Figure 6 It can be seen that the PGAs observed by the strong seismometer and the intensity meter are almost all mixed together. Intuitively, there is almost no obvious difference between the two sets of data, and it is easy to think that there is no difference between the two sets of data. The two sets of data were permuted 10,000 times, and the distribution of the mean value of the difference between the two sets of PGA was obtained as follows Figure 7 As shown, Figure 7 (a) Figure 7 (b) and Figure 7 The horizontal axis of (c) is the mean difference, and the vertical axis is the number. The actual observed statistics and their occurrence probability results are given in Table 4.

[0059] Table 4 Actual statistics and occurrence probability of intensity meter and strong motion instrument matching data

[0060] according to Figure 7 , the average value of the difference between the intensity meter and the strong motion meter PGA is still normally distributed in the three directions. Figure 7In the figure, the actual observed statistics (vertical solid line) relative to the bilateral 2.5% confidence level (bilateral vertical dashed line) indicate that there is a significant difference between the two groups of data. According to the data given in Table 4, although the probability of the actual observed statistics of the average difference between the PGA of the two groups of intensitometers and strong seismometers in 10,000 permutation experiments is slightly higher than the probability of the actual observed statistics of the average difference between the PGA of the intensitometer and seismometer (Table 2) and the actual observed statistics of the average difference between the PGA of the strong seismometer and seismometer (Table 3), the probability of the actual observed statistics in the three directions in Table 4 is still far less than the 5% confidence level. This result shows that there is a significant difference between the PGA of the intensitometer and the strong seismometer. Moreover, further use of a one-sided test can confirm that the observed PGA of the intensitometer is greater than the corresponding PGA of the strong seismometer.

[0061] (4) The three types of instruments / three deployment methods of the earthquake early warning network were grouped in pairs and matching data was obtained. The permutation experimental results of the three groups of matching data showed that: There is a significant difference between the PGA observed by the intensity meter and the seismometer, and the PGA of the intensity meter is significantly greater than the PGA of the seismometer. There is a significant difference between the PGA observed by the strong earthquake meter and the seismometer, and the PGA of the strong earthquake meter is significantly greater than the PGA of the seismometer. There is a significant difference between the PGA observed by the intensity meter and the strong earthquake meter, and the PGA of the intensity meter is significantly greater than the PGA of the strong earthquake meter. That is, the PGA of the intensity meter>the PGA of the strong earthquake meter>the PGA of the seismometer.

[0062] The different deployment conditions of the three types of instruments in the early warning observation network actually reflect the amplification effect of different strata, especially loess strata, on seismic waves. According to the investigation results of the earthquake site structure and exposed strata after the earthquake, the surface strata in the earthquake zone are modern loess accumulated since the Quaternary period. According to the common deployment conditions of the three types of instruments in Table 1, the seismometer is deployed on the bedrock, so it is less affected by the surface soil layer, and the recorded ground motion PGA is relatively small. Although both the strong seismometer and the intensity meter are deployed in the soil layer, the depth of the two into the soil layer is different; the base of the strong seismometer needs to be fixed to a deeper part of the ground through steel bars, so the strong seismometer is relatively less affected by the shallow loess layer on the surface; in comparison, the intensity meter is installed on the floor of the small room next to the base station, and the installation base foundation is shallower than the strong seismometer, which will be more affected by the surface loess layer. From the analysis of the differences in the deployment conditions of the two types of instruments, the strong seismometer and the intensity meter, it can be seen that the seismic motion recording data of the intensity meter will be more affected by the loess layer. The results of comparing the PGA of the strong seismometer and the PGA of the intensity meter using this method can confirm that the PGA observation data of the seismic motion of the intensity meter is significantly larger than the PGA observation data of the strong seismometer. Therefore, it can be confirmed that the larger seismic motion record of the intensity meter is related to the amplification effect of the loess layer. Therefore, the difference in PGA of the strong seismometer and the intensity meter under different deployment conditions can be explained by the amplification effect of the loess layer on seismic waves; in addition, the amplification effect of the loess layer is also the main reason why the earthquake damage is particularly severe although the magnitude is not high. The comparison and analysis results of the observation data show that the different deployment methods of the earthquake early warning network in the loess area will have a significant impact on the seismic motion recording data. Since there are significant differences in the seismic motion data recorded by instruments deployed in different ways in the loess area, it is bound to affect the calculation of earthquake early warning parameters (such as magnitude), and then affect the estimation of the distribution of the earthquake damage impact field. Therefore, it is an unavoidable task to perform consistency correction when using seismic motion data of instruments with different deployment methods. In addition, when using PGA to estimate the intensity impact field or surface earthquake damage, it is also necessary to pay attention to the differences between the seismic motion data to avoid inconsistent or even contradictory results due to data differences.

[0063] In combination with the above embodiments, the present invention aims at the complex data analysis application scenario in which the observation instrument composition and deployment conditions of the earthquake early warning network are diverse, and the observation data is affected by multiple factors at the same time. A data analysis method using a computer simulation replacement experiment to test whether there are differences in the observation data under different deployment conditions is proposed. Through this method, it can be confirmed whether the deployment conditions will have a significant impact on the observation data. Before using this method for data comparison and analysis, it is first necessary to match the observation data of two types of different deployment conditions. The purpose of this step operation is to make the influencing factors of the two sets of data only have differences in deployment conditions, while other influencing factors are basically the same, so as to eliminate the influence of other factors on the data difference. As described in the above embodiments, the seismic data will be affected by a series of factors such as epicentral distance, recording point geology, terrain conditions, propagation path, propagation direction, etc., and the matching data can ensure that these conditions are basically consistent due to the restrictions of matching constraints, so that only the deployment conditions, the factor to be studied, have different effects on the data, and then confirm whether the deployment conditions will really have a significant impact on the data through data differences. This step operation puts forward relatively high requirements on the data, requiring enough data to meet the matching conditions so that the obtained matching data is representative and the obtained conclusion is credible. The earthquake early warning observation network makes this operation possible due to its dense distribution of observation points. Therefore, the data analysis conclusions obtained are reliable and representative. The permutation experiment in the operation of this method is a key step in obtaining the distribution of statistics. The selection of statistics in this step needs to be determined according to the goal of data analysis. For example, the purpose of this embodiment is to confirm whether there are significant differences between two groups of early warning observation data under different deployment conditions, so the difference between the two groups of matching data is used. In addition, since a single matching data is not representative, the mean of the difference between all matching data is used as a statistic. After determining the statistic, the statistics of the actual observed data can be calculated. Then, assuming that there is no difference between the two groups of data, a computer is used to perform random grouping several times to obtain various possible values ​​of the statistic, thereby obtaining the distribution of the statistic. If the actual observed statistics appear in an area where the distribution is relatively concentrated (such as in this article Figure 3 , Figure 5 , Figure 7 The position between the 2.5% confidence level on both sides indicates that the value is very likely to appear, which is consistent with the statistical distribution obtained by assuming that there is no difference between the two sets of data, indicating that the hypothesis is established. However, if the actual observed statistic appears at an extreme position, it means that the probability of this situation is extremely low, which is inconsistent with the understanding that the probability of this statistic should be very high when assuming that there is no difference between the two sets of data. Therefore, the premise of the hypothesis is not established, proving that there is a significant difference between the two sets of data.

[0064] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0065] Figure 8 : is a schematic diagram of the structure of a data analysis device 80 for an earthquake early warning network provided by an embodiment of the present invention, the device comprising: The acquisition module 81 is used to acquire observation data of different observation instruments in the earthquake early warning network, where the different observation instruments include at least two types of observation instruments.

[0066] The matching module 82 is used to match the observation points of any two types of observation instruments according to the positions of the observation instruments to obtain multiple groups of observation point pairs.

[0067] The calculation module 83 is used to extract feature quantities from the observation data of each observation point, and judge the observation difference of each pair of observation points according to the feature quantities; and determine the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points.

[0068] The processing module 84 is used to determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network based on the observation differences of all different types of observation instruments.

[0069] As a possible implementation manner, the calculation module 83 is specifically used for: Extract the peak acceleration from the observation data of each observation point, calculate the difference between the peak accelerations corresponding to the two observation points in each pair of observation points, and obtain the observation difference of each pair of observation points; As a possible implementation manner, the calculation module 83 is specifically used for: The average value of the difference between the peak accelerations of each pair of observation points is calculated to obtain a statistic, and the observation difference between the two types of observation instruments is determined based on the statistic.

[0070] As a possible implementation manner, the calculation module 83 is specifically used for: Determine the distribution of statistics through permutation experiments; Determine the confidence level of the statistic based on the statistic and its distribution; If the confidence level is less than a preset confidence level threshold, it is determined that there is an observation difference between the two types of observation instruments; otherwise, it is determined that there is no observation difference between the two types of observation instruments.

[0071] As a possible implementation manner, the preset threshold is determined according to the following manner: Calculate the average distance between the two selected observation points and the epicenter; A preset threshold is determined according to the distance average value; wherein the preset threshold is negatively correlated with the distance average value.

[0072] As a possible implementation, after obtaining the observation data of different observation instruments in the earthquake early warning network, the acquisition module 81 is further used to: The observation data from different observation instruments are preprocessed and converted into the same type of data.

[0073] As a possible implementation, at least two types of observation instruments include: a seismometer, a strong motion detector and an intensity meter.

[0074] It can be seen that in view of the fact that the observation instruments and deployment conditions of the earthquake early warning network are diverse, and the observation data of different observation instruments may be different, the embodiment of the present invention first matches the observation points of any two types of observation instruments according to the positions of each observation instrument to obtain multiple groups of observation point pairs to ensure the effectiveness of the comparison; then, extract the characteristic quantity from the observation data of each observation point, and judge the observation difference of each group of observation point pairs according to the characteristic quantity; determine the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs; finally, based on the observation difference of all different types of observation instruments, determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network. The present invention can accurately detect the data differences caused by different deployment conditions of different types of observation instruments, provide a basis for the correction of observation data, and thus ensure the accuracy of earthquake early warning.

[0075] Fig. 9 FIG. 9 is a schematic diagram of an earthquake early warning network system 90 provided by an embodiment of the present invention. Fig. 9 As shown, the earthquake early warning network system 90 of this embodiment includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91, such as a data analysis program for an earthquake early warning network. When the processor 91 executes the computer program 93, the steps in the above-mentioned data analysis method embodiments of the earthquake early warning network are implemented, such as Figure 1 Alternatively, when the processor 91 executes the computer program 93, the functions of each module in the above-mentioned device embodiments are realized, for example, Figure 8 The functions of modules 81 to 84 are shown.

[0076] Exemplarily, the computer program 93 may be divided into one or more modules / units, which are stored in the memory 92 and executed by the processor 91 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 93 in the earthquake early warning network system 90.

[0077] The earthquake early warning network system 90 may include, but is not limited to, a processor 91 and a memory 92. Those skilled in the art will appreciate that Fig. 9 It is only an example of the earthquake early warning network system 90 and does not constitute a limitation of the earthquake early warning network system 90. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the earthquake early warning network system 90 may also include input and output devices, network access devices, buses, etc.

[0078] The processor 91 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0079] The memory 92 may be an internal storage unit of the earthquake early warning network system 90, such as a hard disk or memory of the earthquake early warning network system 90. The memory 92 may also be an external storage device of the earthquake early warning network system 90, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the earthquake early warning network system 90. Further, the memory 92 may also include both an internal storage unit and an external storage device of the earthquake early warning network system 90. The memory 92 is used to store the computer program and other programs and data required by the earthquake early warning network system 90. The memory 92 may also be used to temporarily store data that has been output or is to be output.

[0080] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0081] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] In the embodiments provided by the present invention, it should be understood that the disclosed devices / systems and methods can be implemented in other ways. For example, the device / earthquake early warning network system embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0087] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A data analysis method for an earthquake early warning network, characterized in that: include: Acquiring observation data of different observation instruments in an earthquake early warning network, wherein the different observation instruments include at least two types of observation instruments; For any two types of observation instruments, the observation points are matched according to the positions of each observation instrument to obtain multiple groups of observation point pairs; Extracting characteristic quantities from the observation data of each observation point, judging the observation difference of each pair of observation points according to the characteristic quantities; determining the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points; Based on the observation differences of all different types of observation instruments, a correction strategy for the observation data of different observation instruments in the earthquake early warning network is determined.

2. The data analysis method of the earthquake early warning network according to claim 1, characterized in that: For any two types of observation instruments, observation point matching is performed according to the positions of the observation instruments to obtain multiple groups of observation point pairs, including: For any observation point of one type, select the observation point with the smallest distance less than a preset threshold from each observation point of another type to form a set of observation point pairs; The preset threshold is determined according to the distance between the two selected observation points and the epicenter.

3. The data analysis method of the earthquake early warning network according to claim 1, characterized in that: The step of extracting a feature quantity from the observation data of each observation point and judging the observation difference of each pair of observation points according to the feature quantity comprises: Extract the peak acceleration from the observation data of each observation point, calculate the difference between the peak accelerations corresponding to the two observation points in each pair of observation points, and obtain the observation difference of each pair of observation points; Accordingly, determining the observation difference of the two types of observation instruments according to the observation difference of each group of observation point pairs includes: The average value of the difference between the peak accelerations of each group of observation point pairs is calculated to obtain a statistic, and the observation difference between the two types of observation instruments is determined based on the statistic.

4. The data analysis method of the earthquake early warning network according to claim 3, characterized in that: Determining the observation difference between the two types of observation instruments according to the statistic includes: Determine the distribution of statistics through permutation experiments; Determining the confidence level of the statistic according to the statistic and the statistic distribution; If the confidence level is less than a preset confidence level threshold, it is determined that there is an observation difference between the two types of observation instruments; otherwise, it is determined that there is no observation difference between the two types of observation instruments.

5. The data analysis method of earthquake early warning network according to claim 2, characterized in that: The preset threshold is determined according to the following method: Calculate the average distance between the two selected observation points and the epicenter; The preset threshold is determined according to the distance average value; wherein the preset threshold is negatively correlated with the distance average value.

6. The data analysis method for an earthquake early warning network according to any one of claims 1 to 5, characterized in that: After obtaining the observation data of different observation instruments in the earthquake early warning network, the method further includes: The observation data from different observation instruments are preprocessed and converted into the same type of data.

7. The data analysis method for an earthquake early warning network according to any one of claims 1 to 5, characterized in that: The at least two types of observation instruments include: a seismometer, a strong motion detector and an intensity meter.

8. A data analysis device for an earthquake early warning network, characterized in that: include: An acquisition module, used to acquire observation data of different observation instruments in the earthquake early warning network, wherein the different observation instruments include at least two types of observation instruments; A matching module is used to match the observation points of any two types of observation instruments according to the positions of each observation instrument to obtain multiple groups of observation point pairs; A calculation module, used to extract characteristic quantities from the observation data of each observation point, and determine the observation difference of each pair of observation points according to the characteristic quantities; and determine the observation difference of the two types of observation instruments according to the observation difference of each pair of observation points; The processing module is used to determine the correction strategy of the observation data of different observation instruments in the earthquake early warning network based on the observation differences of all different types of observation instruments.

9. An earthquake early warning network system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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