Data Analysis Method, Device, System and Medium for Earthquake Early Warning Network

Abstract

The present invention is applicable to the field of data processing technology, and provides a data analysis method, device, system and medium for a seismic early warning network. The method includes: obtaining observation data of different observation instruments in the seismic early warning network, where the different observation instruments include at least two types of observation instruments; for any two types of observation instruments, performing observation point matching 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, and judging the observation differences of each group of observation point pairs according to the characteristic quantities; determining the observation differences between the two types of observation instruments according to the observation differences of each group of observation point pairs; and determining a calibration strategy for the observation data of different observation instruments in the seismic early warning network based on the observation differences of all different types of observation instruments. The present invention can analyze the differences in the observation data of different monitoring instruments in the seismic early warning network, thereby guiding data calibration and improving the accuracy of seismic early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly 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 earthquake monitoring network near the epicenter to monitor earthquakes in real time. After an earthquake occurs, based on the seismic wave information first obtained by the stations near the epicenter, the three elements of the earthquake and the earthquake motion influence field are quickly estimated, and using the principle that the propagation speed of electromagnetic waves is much greater than that of seismic waves, early warning information is issued before the destructive seismic waves reach the target area. Earthquake early warning can provide people with more time to take shelter and is an important means to reduce losses of life and property.

[0003] In the related art, an earthquake early warning network consists of three types of instruments: seismographs, accelerographs, and intensity meters with different deployment conditions. The inventors of the present application found that seismographs are usually deployed on bedrock, accelerographs and intensity meters are usually deployed on soil layers, and in actual engineering construction, intensity meters are usually deployed on the ground of the house beside the base station. Due to different deployment conditions of the three types of instruments, it may lead to differences in the observed data, resulting in inconsistent or even contradictory observation results. Therefore, accurately analyzing the differences between the three types of monitoring instruments is crucial for guiding data correction and improving the accuracy of earthquake early warning. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a data analysis method, device, system and medium for an earthquake early warning network to analyze the differences in the observed data of different monitoring instruments in the earthquake early warning network, so as to guide data correction and improve the accuracy of earthquake early warning.

[0005] The first aspect of the embodiments of the present invention provides a data analysis method for an earthquake early warning network, and the method includes:

[0006] Obtain the observed data of different observation instruments in the earthquake early warning network, where the different observation instruments include at least two types of observation instruments;

[0007] For any two types of observation instruments, perform observation point matching according to the positions of each observation instrument to obtain multiple pairs of observation points;

[0008] Extract feature quantities from the observed data of each observation point, and judge the observation differences of each pair of observation points according to the feature quantities; determine the observation differences between the two types of observation instruments according to the observation differences of each pair of observation points;

[0009] Based on the observation differences of all different types of observation instruments, determine the correction strategy for the observed data of different observation instruments in the earthquake early warning network.

[0010] In a possible implementation of the first aspect, 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:

[0011] For any one observation point of one type, select, from each observation point of the other type, the observation point with a distance less than a preset threshold and the smallest distance, and form a group of observation point pairs;

[0012] Wherein, the preset threshold is determined according to the distances between the two selected observation points and the earthquake epicenter.

[0013] In a possible implementation of the first aspect, extracting characteristic quantities from the observation data of each observation point, and judging the observation differences of each group of observation point pairs according to the characteristic quantities, including:

[0014] 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 group of observation point pairs, and obtain the observation differences of each group of observation point pairs;

[0015] Correspondingly, determining the observation differences between the two types of observation instruments according to the observation differences of each group of observation point pairs, including:

[0016] Calculate the average value of the differences in peak accelerations of each group of observation point pairs to obtain a statistic, and determine the observation differences between the two types of observation instruments according to the statistic.

[0017] In a possible implementation of the first aspect, determining the observation differences between the two types of observation instruments according to the statistic, including:

[0018] Determine the statistic distribution through a permutation experiment;

[0019] Determine the confidence level of the statistic according to the statistic and the statistic distribution;

[0020] If the confidence level is less than a preset confidence level threshold, it is determined that there are observation differences between the two types of observation instruments; otherwise, it is determined that there are no observation differences between the two types of observation instruments.

[0021] In a possible implementation of the first aspect, the preset threshold is determined according to the following method:

[0022] Calculate the average value of the distances between the two selected observation points and the earthquake epicenter;

[0023] Determine the preset threshold according to the average distance; wherein, the preset threshold is negatively correlated with the average distance.

[0024] In a possible implementation of the first aspect, after obtaining the observation data of different observation instruments in the earthquake early warning network, the method further includes:

[0025] Preprocess the observation data of different observation instruments and convert them into the same type of data.

[0026] In a possible implementation of the first aspect, the at least two types of observation instruments include: seismographs, strong motion seismographs, and intensity meters.

[0027] A second aspect of the embodiments of the present invention provides a data analysis device for an earthquake early warning network, and the device includes:

[0028] An acquisition module, configured to acquire the observation data of different observation instruments in the earthquake early warning network, and the different observation instruments include at least two types of observation instruments;

[0029] A matching module, configured to match the observation points for any two types of observation instruments according to the positions of the respective observation instruments, and obtain multiple groups of observation point pairs;

[0030] A calculation module, configured to extract characteristic quantities from the observation data of each observation point, and judge the observation differences of each group of observation point pairs according to the characteristic quantities; determine the observation differences between the two types of observation instruments according to the observation differences of each group of observation point pairs;

[0031] A processing module, configured to determine a calibration strategy for 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.

[0032] A third aspect of the embodiments of the present invention provides an earthquake early warning network system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method in the above-mentioned first aspect or any implementation manner of the first aspect are implemented.

[0033] A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method in the above-mentioned first aspect or any implementation manner of the first aspect are implemented.

[0034] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0035] In view of the problems that the observation instruments of the earthquake early warning network have diverse compositions and deployment conditions, and the observation data of different observation instruments may vary, in the embodiments of the present invention, first, for any two types of observation instruments, observation points are matched according to the positions of each observation instrument to obtain multiple pairs of observation points, ensuring the effectiveness of comparison; then, characteristic quantities are extracted from the observation data of each observation point, and based on the characteristic quantities, the observation differences of each pair of observation points are judged; according to the observation differences of each pair of observation points, the observation differences between these two types of observation instruments are determined; finally, based on the observation differences of all different types of observation instruments, a calibration strategy for the observation data of different observation instruments in the earthquake early warning network is determined. 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 calibration of observation data, and thus ensure the accuracy of earthquake early warning. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 It is a schematic flowchart of the data analysis method for the earthquake early warning network provided by the embodiments of the present invention;

[0038] Figure 2 It is a schematic diagram showing the variation of PGA of the observation point pairs of the seismograph and the accelerometer with the epicentral distance in the embodiments of the present invention, where: Figure 2 (a) is the north-south direction diagram, Figure 2 (b) is the east-west direction diagram, Figure 2 (c) is the vertical direction diagram;

[0039] Figure 3 It is the distribution of the permutation experiment results of the mean of the matching data differences between the seismograph and the accelerometer in the embodiments of the present invention, where: Figure 3 (a) is the north-south direction diagram, Figure 3 (b) is the east-west direction diagram, Figure 3 (c) is the vertical direction diagram;

[0040] Figure 4 It is a schematic diagram showing the variation of PGA of the observation point pairs of the seismograph and the strong motion seismograph with the epicentral distance in the embodiments of the present invention, where: Figure 4 (a) is the north-south direction diagram, Figure 4 (b) is the east-west direction diagram, Figure 4 (c) is the vertical direction diagram;

[0041] Figure 5It is the distribution of the results of the permutation experiment of the mean difference in matching data between the seismograph and the accelerograph provided by the embodiments of the present invention, where: Figure 5 (a) is the north-south direction diagram, Figure 5 (b) is the east-west direction diagram, Figure 5 (c) is the vertical direction diagram;

[0042] Figure 6 It is a schematic diagram showing the variation of PGA with the epicentral distance for the observation points of the accelerometer and the accelerograph provided by the embodiments of the present invention, where: Figure 6 (a) is the north-south direction diagram, Figure 6 (b) is the east-west direction diagram, Figure 6 (c) is the vertical direction diagram;

[0043] Figure 7 It is the distribution of the results of the permutation experiment of the mean difference in matching data between the accelerometer and the accelerograph provided by the embodiments of the present invention, where: Figure 7 (a) is the north-south direction diagram, Figure 7 (b) is the east-west direction diagram, Figure 7 (c) is the vertical direction diagram;

[0044] Figure 8 It is a schematic structural diagram of the data analysis device of the earthquake early warning network provided by the embodiments of the present invention;

[0045] Figure 9 It is a schematic structural diagram of the earthquake early warning network system provided by the embodiments of the present invention. Detailed implementation manners

[0046] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also 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 avoid unnecessary details from interfering with the description of the present invention.

[0047] In order to illustrate the technical solutions described in the present invention, the following will be described through specific embodiments.

[0048] In the related art, the earthquake early warning network consists of a large number of reference stations, basic stations, and general stations. Among them, the general stations composed of accelerometers account for 70%. More rapid response tasks need to be completed by the accelerometer data. However, due to the completely different deployment conditions of the accelerometers and the traditional seismographs, the extensive use of accelerometers not only improves the early warning response speed but also brings new problems to the use of early warning data.

[0049] The observed physical quantities corresponding to various instruments and the deployment site conditions are shown in Table 1. The broadband seismographs of the reference station are generally deployed on the bedrock (or underground) of the free field to reduce the noise impact; to prevent the amplitude limiting problem of the seismograph near the epicenter of a large earthquake, a strong motion seismograph is deployed at the seismograph station at the same time, and the deployment conditions of the two types of instruments are the same; the deployment conditions of the other strong motion seismograph observation points are for free field soil layer observation. To simplify the installation work of the accelerograph, the accelerographs are uniformly installed in the small room beside the communication base station, generally installed on the ground in the room, and if there is no horizontal ground, they are installed on the wall, but the height does not exceed 30 cm.

[0050] Table 1 Observation point information of different types of instruments

[0051]

[0052] It can be seen that the deployment conditions of the three types of instruments are all different. Ignoring the differences in the performance of the instruments themselves (since the early warning data used is as close to the epicenter as possible and the ground motion signal is much larger than the noise background of the instrument, the data differences caused by the instrument performance can be ignored), the deployment site conditions will cause differences in the observed data. How to confirm the impact of this difference on the calculation of earthquake early warning parameters and perform consistency correction when using the ground motion data of instruments with different deployment methods is an urgent problem to be solved.

[0053] See Figure 1 As shown, the embodiment of the present invention provides a data analysis method for an earthquake early warning network, including:

[0054] Step S101, obtaining the observed data of different observation instruments in the earthquake early warning network, and the different observation instruments include at least two types of observation instruments.

[0055] In this embodiment, at least two types of observation instruments may include, but are not limited to, seismographs, strong motion seismographs, and accelerographs.

[0056] In the earthquake early warning network, multiple of each type of observation instrument are deployed. Each observation instrument serves as an observation point, and a series of observed data is obtained through observation.

[0057] Among them, the observed data of the seismograph is velocity, and the observed data of the strong motion seismograph and the accelerograph is acceleration. For the convenience of comparative analysis, the observed data of different observation instruments can be preprocessed and converted into the same type of data. For example, a differentiation operation is performed on the observed data of the seismograph.

[0058] Step S102, for any two types of observation instruments, perform observation point matching according to the positions of each observation instrument to obtain multiple pairs of observation points.

[0059] To confirm whether there are significant differences in the observed data between seismographs, accelerographs, and intensity meters due to deployment conditions, in this embodiment, the observation points are matched according to the positions of the respective observation instruments to eliminate the influence of other factors (such as epicentral distance, geology and topography near the observation point, seismic wave propagation path, propagation direction, etc.).

[0060] Optionally, this step can be implemented in the following manner:

[0061] For any observation point of one type, select the observation point with the smallest distance and less than the preset threshold from each observation point of the other type to form a pair of observation points; where the preset threshold is determined according to the distances of the two selected observation points from the epicenter.

[0062] In this embodiment, the matching method is that the observation points of the two types of observation instruments are as close as possible, and the threshold of their closeness is related to the distance of the observation point from the epicenter. For example, if the distance between two observation points is less than 1 / 6 of the epicentral distance, select the two points with the smallest distance among all pairs that meet the conditions as the matching points of the two types of instruments. Thus, search for all the observation points of the two types of instruments that can be matched to form pairs of observation points. Since the two observation points are close enough, the influence of the epicentral distance, propagation path, propagation direction, etc. on the data is basically the same, and only the deployment conditions will affect the data.

[0063] Among them, the preset threshold can be determined in the following way: calculate the average value of the distances of the two selected observation points from the epicenter; determine the preset threshold according to the average distance.

[0064] This embodiment does not limit the specific calculation formula of the preset threshold. However, it should be noted that: the preset threshold is negatively correlated with the average distance. That is, when the distance of the observation point from the epicenter is far, the accuracy of the data decreases, and the selected pairs of observation points need to be closer to improve the accuracy of the comparison.

[0065] Step S103, extract the characteristic quantities from the observed data of each observation point, and judge the observation differences of each pair of observation points according to the characteristic quantities; determine the observation differences of the two types of observation instruments according to the observation differences of each pair of observation points.

[0066] Since the waveform data of seismic events recorded by observation instruments is relatively complex, in order to compare two sets of data, it is necessary to extract characteristic quantities from the event waveforms and use these characteristic quantities to represent the observed data for subsequent data analysis work. Peak ground acceleration (PGA) is the most commonly used characteristic quantity representing the amplitude characteristics of ground motion. Here, PGA is used as the characteristic quantity of the event waveform to construct two sets of matching data. Of course, other characteristic quantities can also be selected, such as peak ground velocity (PGV), peak ground displacement (PGD), root mean square acceleration (RMS), etc. However, the calculation and extraction of these characteristic quantities are relatively complex, and only PGA is taken as an application example here.

[0067] Optionally, extract characteristic quantities from the observed data of each observation point, and based on the characteristic quantities, judge the observation differences between each pair of observation points, including:

[0068] Extract the peak ground acceleration from the observed data of each observation point, calculate the difference between the peak ground accelerations corresponding to the two observation points in each pair of observation points, and obtain the observation difference for each pair of observation points.

[0069] Correspondingly, based on the observation differences of each pair of observation points, determine the observation differences between these two types of observation instruments, including:

[0070] Calculate the average value of the differences in peak ground acceleration for each pair of observation points M Diff , obtain a statistic, and based on the statistic, determine the observation differences between these two types of observation instruments.

[0071] M Diff The calculation method is as follows:

[0072]

[0073] In the formula, n represents the number of matching points, k and j respectively represent two different deployment methods.

[0074] When comparing the observation differences between two types of observation instruments, M Diff is used as the statistic. When this statistic is large enough or small enough, it indicates that there are significant differences 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 statistic distribution. If the actual observed statistic deviates from the position where the vast majority of the statistic distributions are located, that is, the occurrence probability of the actual observed statistic is less than a certain confidence level (such as 5%), then it is considered that the occurrence probability of the actual observed statistic is extremely small and there are significant differences between the two sets of data; otherwise, it is considered that the difference between the two is not significant.

[0075] The permutation test is a means to obtain the distribution of statistics. Its basic idea is as follows: Assume that there is no difference between two groups of data. By randomly re-grouping the two groups of data, a possible value of the statistic can be obtained. By using a computer to repeatedly sample multiple times, the distribution of the statistic can be obtained. Then, observe the position of the actual observed statistic on the distribution of the statistic obtained from the permutation test. If the probability of the actual observed statistic appearing is extremely small (less than the pre-set confidence level), it can be confirmed that the hypothesis that there is no difference between the two groups of data does not hold, and there is actually a difference between them. Because if there is no difference between the two groups of data, the probability of the actual observed statistic repeating should be large.

[0076] Compare the actual observed statistic with the statistical distribution obtained from the permutation test, and use a 5% confidence level to test whether there is a significant difference between the two groups of data. If the probability of the actual observed statistic appearing is less than the set confidence level, there is a significant difference between the two groups of data; otherwise, there is no obvious difference between the two groups of data.

[0077] Step S104: Based on the observation differences of all different types of observation instruments, determine the correction strategy for the observation data of different observation instruments in the earthquake early warning network.

[0078] By analyzing the influence of the deployment conditions on the observation data, it can be judged whether the observation data of the earthquake early warning network needs to be corrected, and it can also guide the correction of the observation data. For the joint application of different types of observation instruments, the issue of how to connect has important guiding significance. Since the correction strategy is not the focus of this embodiment, it will not be described in detail here.

[0079] It can be seen that for the problems that the composition and deployment conditions of the observation instruments in the earthquake early warning network are diverse and the observation data of different observation instruments may have differences, the embodiment of the present invention first matches the observation points according to the positions of each observation instrument for any two types of observation instruments to obtain multiple groups of observation point pairs to ensure the effectiveness of the comparison; then, extract the characteristic quantities from the observation data of each observation point, and judge the observation differences of each group of observation point pairs according to the characteristic quantities; determine the observation differences between the two types of observation instruments according to the observation differences of each group of observation point pairs; finally, based on the observation differences of all different types of observation instruments, determine the correction strategy for 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 the observation data, and thus ensure the accuracy of the earthquake early warning.

[0080] Hereinafter, the solution of the present application will be described through a specific embodiment.

[0081] There are 1,606 stations in the earthquake early warning network in a loess area. The observed physical quantities corresponding to various instruments and the deployment site conditions are shown in Table 1. Among them, there are 97 seismographs; 97 of the 399 strong motion instrument observation points are deployed in parallel with the seismographs, and the deployment conditions of the two types of instruments are the same. The other 302 strong motion instrument observation points are deployed under free-field soil layer observation conditions; there are 1,110 accelerometers.

[0082] (1) According to the conditions for searching for matching points in the above-mentioned embodiment, 93 groups of matching seismograph and accelerometer stations are searched from 97 seismograph observation points and 1,110 accelerometer observation points. Since the observed quantity of the seismograph is velocity, a differential operation needs to be performed first when comparing it with the acceleration of the accelerometer, and then the PGA value is extracted. In addition, there is a problem of amplitude limitation in the seismograph 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 amplitude-limited data. The scatter plot of the PGA varying with the epicentral distance obtained from the matching stations of the seismograph and the accelerometer is as Figure 2 shown (in the figure, N, E, and Z represent the north-south, east-west, and vertical directions respectively), Figure 2 (a), Figure 2 (b) and Figure 2 (c) The abscissa of all is the epicentral distance (unit: km), and the ordinate of all is the PGA (unit: gal).

[0083] From Figure 2 the scatter plot of the PGA varying with the epicentral distance, it can be clearly seen that there are two sets of variation laws of the PGA varying with the increase of the epicentral distance. For some observation points, the PGA obtained by the accelerometer is significantly greater than that of the seismograph, but for some other observation points, the difference between the two is not obvious. For most observation points, the PGA observed by the two instruments is mixed together, and it is difficult to distinguish whether the PGA of one instrument is clearly greater than that of the other instrument. According to the method introduced in the above-mentioned embodiment, the average difference is used as the statistic for 10,000 permutation experiments on the two sets of matching data, and the statistical distribution of the 3-component PGA permutation experiment is as Figure 3 shown (in the figure, N, E, and Z are the same as before, representing the north-south, east-west, and vertical directions; the two dotted lines in the figure represent the positions of the statistics corresponding to the 2.5% confidence level, and the solid line represents the position of the average difference of the statistics calculated from the actual observed data), Figure 3 (a), Figure 3 (b) and Figure 3 (c) The abscissa of all is the average difference, and the ordinate of all is the number. The actual observed statistic and its occurrence probability level obtained from the permutation experiment results are shown in Table 2.

[0084] Table 2 Actual observed statistics and occurrence probabilities of the matching points of seismographs and accelerometers

[0085]

[0086] Figure 3 The statistical quantities in the north-south, east-west, and vertical directions, that is, the average value of the difference in the matching PGA, show an obvious normal distribution in the distributions obtained through 10,000 permutation experiments under the assumption that there is no difference between the two groups of data. Figure 3 The actually observed statistical quantity marked in the figure, that is, the solid line in the figure, corresponds to each component direction in Table 2 M Diff The value has never appeared in 10,000 permutation experiments, and the p-value (probability of the actually observed statistical quantity) corresponding to each sub-item in Table 2 is less than one in ten thousand. This indicates that there are significant differences between the two groups of matching data, that is, the PGA of the seismograph and the accelerograph. Figure 3 The positions of the 2.5% confidence levels on both sides (the dashed lines in the figure, combined into a 5% confidence level) are given in the statistical quantity distribution diagram. Since only whether there are significant differences between the two groups of data is considered here, the actually observed statistical quantity appearing outside the dashed lines indicates significant differences. If it is necessary to consider the significant differences in which one group of data is greater or less than the other, a one-sided 5% confidence level can be used for the test. In fact, even using a one-sided 5% confidence level to test whether the PGA of the accelerograph is significantly greater than that of the seismograph, a conclusion of significant differences can still be obtained because the average value of the difference between the accelerograph and the seismograph appears with a probability less than one in ten thousand in 10,000 permutation experiments. Therefore, a more definite conclusion can be obtained: at the 5% confidence level, the PGA of the accelerograph is significantly greater than that of the seismograph.

[0087] (2)When comparing the seismograph data and the strong motion data, it is necessary to exclude the cases where the two have the same deployment conditions, that is, the case where strong motion instruments are deployed at the seismograph observation points at the same time. Here, only the data of seismographs deployed on bedrock and strong motion instruments deployed on soil layers are compared. Among 302 strong motion instruments and 97 seismographs, 89 groups of qualified matching seismograph stations and strong motion instrument stations are obtained by searching. The scatter plots of the PGA of the two groups of matching data changing with the epicentral distance are as Figure 4 shown, Figure 4 (a), Figure 4 (b), and Figure 4 (c), the abscissa of which is the epicentral distance (unit: km), and the ordinate is the PGA (unit: gal).

[0088] Similar to the matching data of the seismograph and the accelerograph, Figure 4 in the matching data of the seismograph and the strong motion instrument, some data show that the PGA of the strong motion instrument is greater than that of the seismograph, but in more cases, the two groups of matching data points are mixed together and it is difficult to distinguish. Therefore, a permutation experiment is carried out on the two groups of data.

[0089] Construct the matching data of the seismograph and the strong motion instrument, and conduct 10,000 permutation experiments to obtain the distribution of the average value of the difference between the PGA of the strong motion instrument and the seismograph.Figure 5 The distribution of the three - component statistics and the positions of the actual observed statistics are given. Figure 5 (a), Figure 5 (b) and Figure 5 (c) have the mean difference on the abscissa and the number on the ordinate. Table 3 gives the actual observed statistics and their occurrence probabilities p.

[0090] Table 3 Actual statistics and occurrence probabilities of the matching data of seismographs and accelerographs

[0091]

[0092] According to Figure 5 , the mean value of the difference between the PGA of the accelerograph and the seismograph, and its distribution in the three directions is still a normal distribution. The mean value of the difference between the actually 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 side, indicating that the probability of the actually observed statistic appearing is much smaller than the pre - set confidence level. According to the actual observed statistics given in Table 3 and the fact that the probability of this statistic appearing in 10,000 permutation experiments is less than five ten - thousandths, it shows that there are significant differences in the PGA observed by the accelerograph and the seismograph. Moreover, the actually observed statistics in the three directions are all positive. When using a one - sided 5% confidence level, the test that the PGA of the accelerograph is greater than that of the seismograph can be passed. Therefore, according to the test results of the method in this embodiment, it can be confirmed that: under the same observation background but different deployment conditions, the PGA of the accelerograph is greater than that of the seismograph data.

[0093] (3) For the 302 accelerograph stations deployed in the soil layer and the 1110 intensity meter search - matching sites deployed in the small rooms beside the base stations, 281 groups of matching data are obtained. The scatter plot of the matching PGA data varying with the epicentral distance is as Figure 6 shown, Figure 6 (a), Figure 6 (b) and Figure 6 (c) have the epicentral distance (unit: km) on the abscissa and the PGA (unit: gal) on the ordinate.

[0094] From Figure 6 it can be seen that the PGA observed by the accelerograph and the intensity meter are almost all mixed together, and intuitively there is almost no obvious difference between the two, and it is very easy to be considered that there is no difference between the two groups of data. Conduct 10,000 permutation experiments on the two groups of data, and the distribution of the mean value statistic of the difference between the two groups of PGA is as Figure 7 shown, Figure 7 (a), Figure 7 (b) and Figure 7 (c) have the mean difference on the abscissa and the number on the ordinate. The results of the actual observed statistics and their occurrence probabilities are given in Table 4.

[0095] Table 4 Actual statistics and occurrence probabilities of the matching data between accelerometers and strong motion seismographs

[0096]

[0097] According to Figure 7 , the average value of the difference in PGA between the accelerometer and the strong motion seismograph is still normally distributed in the three directions. Figure 7 In, the actual observed statistic (the vertical solid line in the figure) relative to the two-sided 2.5% confidence level (the two-sided vertical dashed line) indicates a significant difference between the two sets of data. According to the data given in Table 4, although the probability of the actual observed statistic of the average difference in PGA between the two groups of accelerometers and strong motion seismographs is slightly higher than that of the actual observed statistic of the average difference in PGA between accelerometers and seismographs (Table 2) and that of the actual observed statistic of the average difference in PGA between strong motion seismographs and seismographs (Table 3) in 10,000 permutation experiments, the occurrence probabilities of the actual observed statistics in the three directions in Table 4 are still much less than the 5% confidence level. This result shows that there is a significant difference between the PGAs of accelerometers and strong motion seismographs. Moreover, further using a one-sided test can confirm that the PGA observed by the accelerometer is greater than the PGA of the corresponding strong motion seismograph.

[0098] (4) Pairwise grouping of the three types of instruments / three deployment methods in the earthquake early warning network and obtaining the matching data. The results of the permutation experiments on the three groups of matching data show that:

[0099] There is a significant difference between the PGAs observed by the accelerometer and the seismograph, and the PGA of the accelerometer is significantly greater than that of the seismograph. There is a significant difference between the PGAs observed by the strong motion seismograph and the seismograph, and the PGA of the strong motion seismograph is significantly greater than that of the seismograph. There is a significant difference between the PGAs observed by the accelerometer and the strong motion seismograph, and the PGA of the accelerometer is significantly greater than that of the strong motion seismograph. That is, accelerometer PGA > strong motion seismograph PGA > seismograph PGA.

[0100] The different deployment situations of the three types of instruments in the earthquake early warning observation network actually reflect the amplification effects of different strata, especially the loess stratum, on seismic waves. According to the investigation results of the earthquake site structure and the outcropping strata after the earthquake, the surface strata in the earthquake area are modern loess accumulated since the Quaternary. From the general deployment conditions of the three types of instruments in Table 1, the seismograph is deployed on the bedrock, so it is less affected by the surface soil layer, and the PGA of the recorded ground motion is relatively small. Although both the accelerograph and the intensity meter are deployed in the soil layer, the depths at which they penetrate the soil layer are different; the base of the accelerograph needs to be fixed deeper underground through steel bars, so the accelerograph is relatively less affected by the shallow loess layer on the surface; in contrast, since the intensity meter is installed on the floor in the small room beside the base station, the foundation of the installation base is shallower than that of the accelerograph, and it will be more affected by the surface loess layer. From the analysis of the differences in the stratum conditions for the deployment of the accelerograph and the intensity meter, it can be seen that the ground motion recording data of the intensity meter will be more affected by the loess layer. The comparison results of the accelerograph PGA and the intensity meter PGA using this method can confirm that the PGA observation data of the intensity meter for ground motion is significantly greater than the PGA observation data of the accelerograph. Thus, it can be confirmed that the larger ground motion recording of the intensity meter is related to the amplification effect of the loess layer. Therefore, the PGA difference in the different deployment conditions of the above accelerograph and intensity meter 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 earthquake magnitude is not high this time. The comparison and analysis results of the observation data show that different deployment methods of the earthquake early warning network in the loess area will have a significant impact on the ground motion recording data. Since there are significant differences in the ground motion data recorded by the instruments deployed in different ways in the loess area, it will inevitably affect the calculation of earthquake early warning parameters (such as magnitude), and further affect the estimation of the distribution of the earthquake damage influence field. Therefore, it is an unavoidable task to perform consistency correction when using the ground motion data of instruments with different deployment methods. In addition, when using PGA to estimate the intensity influence field or estimate the surface earthquake damage, attention should also be paid to this difference in ground motion data to avoid inconsistent or even contradictory results caused by data differences.

[0101] Combined with the above embodiments, in view of the complex data analysis application scenario where the observation instruments of the earthquake early warning network have diverse compositions and deployment conditions, and the observation data is affected by multiple factors simultaneously, the present invention proposes a data analysis method that uses computer simulation replacement experiments to test whether there are differences in the observation data under different deployment conditions. Through this method, it can be confirmed whether the deployment conditions will have an obvious 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 different deployment conditions. The purpose of this step of operation is to make the influencing factors of the two groups of data only differ in the deployment conditions, while other influencing factors are basically the same, so as to eliminate the influence of other factors on the data differences. As described in the above embodiments, the ground motion data will be affected by a series of factors such as the epicentral distance, geology of the recording point, terrain conditions, propagation path, and propagation direction. Due to the constraints of the matching conditions, the matching data can ensure that these conditions are basically the same. In this way, only the factor to be studied, the deployment condition, has different effects on the data. Furthermore, whether the deployment condition will really have an obvious impact on the data can be confirmed through the data differences. This step of operation places relatively high requirements on the data, requiring that there be enough data to meet the matching conditions so that the obtained matching data is representative and the conclusions obtained are credible. The dense distribution of observation points in the earthquake early warning observation network makes this step of operation possible. Therefore, the data analysis conclusions obtained therefrom are reliable and representative. The replacement experiment in the operation of this method is the key step to obtain the distribution of the statistic. In this step, the selection of the statistic needs to be determined according to the objective of the data analysis. For example, in this embodiment, the purpose is to confirm whether there are significant differences between the warning observation data of two 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 differences of all matching data is used as the statistic. After determining the statistic, the statistic of the actual observation data can be calculated. Then, assuming that there is no difference between the two groups of data, the computer is used to perform several random groupings to obtain various possible values of the statistic, thereby obtaining the distribution of the statistic. If the actual observed statistic appears in a region where the distribution is relatively concentrated (such as the position between 2.5% confidence levels on both sides as shown in this article Figure 3 , Figure 5 , Figure 7 ), it indicates that the probability of this value appearing is very high, which is consistent with the statistical distribution obtained by assuming that there is no difference between the two groups of data, indicating that the assumption holds. However, if the actual observed statistic appears in an extreme position, it means that the probability of this situation appearing is extremely low, which does not conform to the understanding that the probability of this statistic should be very high when assuming that there is no difference between the two groups of data. Therefore, the assumption premise does not hold, proving that there are significant differences between the two groups of data.

[0102] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not 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 to the implementation process of the embodiments of the present invention.

[0103] Figure 8 It is a schematic structural diagram of a data analysis device 80 of an earthquake early warning network provided by an embodiment of the present invention. The device includes:

[0104] An acquisition module 81, configured to acquire observation data of different observation instruments in the earthquake early warning network, and the different observation instruments include at least two types of observation instruments.

[0105] A matching module 82, configured to match observation points for any two types of observation instruments according to the positions of the respective observation instruments, to obtain multiple pairs of observation points.

[0106] A calculation module 83, configured to extract characteristic quantities from the observation data of each observation point, and judge the observation differences of each pair of observation points according to the characteristic quantities; determine the observation differences between the two types of observation instruments according to the observation differences of each pair of observation points.

[0107] A processing module 84, configured to determine a calibration strategy for 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.

[0108] As a possible implementation manner, the calculation module 83 is specifically configured to:

[0109] Extract peak accelerations 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 differences of each pair of observation points;

[0110] As a possible implementation manner, the calculation module 83 is specifically configured to:

[0111] Calculate the average value of the differences in peak accelerations of each pair of observation points to obtain a statistic, and determine the observation differences between the two types of observation instruments according to the statistic.

[0112] As a possible implementation manner, the calculation module 83 is specifically configured to:

[0113] Determine the statistic distribution through a permutation experiment;

[0114] Determine the confidence level of the statistic according to the statistic and the statistic distribution;

[0115] If the confidence level is less than a preset confidence level threshold, it is determined that there are observation differences between the two types of observation instruments; otherwise, it is determined that there are no observation differences between the two types of observation instruments.

[0116] As a possible implementation manner, the preset threshold is determined according to the following method:

[0117] Calculate the average value of the distances between the two selected observation points and the epicenter;

[0118] Determine a preset threshold according to the average distance; wherein, the preset threshold is negatively correlated with the average distance.

[0119] As a possible implementation manner, after obtaining the observation data of different observation instruments in the earthquake early warning network, the obtaining module 81 is further configured to:

[0120] Preprocess the observation data of different observation instruments and convert them into the same type of data.

[0121] As a possible implementation manner, there are at least two types of observation instruments, including: seismographs, strong motion seismographs and intensity meters.

[0122] It can be seen that for the problems that the composition and deployment conditions of the observation instruments in the earthquake early warning network are diverse and the observation data of different observation instruments may be different, in the embodiment of the present invention, first, for any two types of observation instruments, observation point matching is performed according to the positions of each observation instrument to obtain multiple groups of observation point pairs, ensuring the effectiveness of comparison; then, characteristic quantities are extracted from the observation data of each observation point, and according to the characteristic quantities, the observation differences of each group of observation point pairs are judged; according to the observation differences of each group of observation point pairs, the observation differences between the two types of observation instruments are determined; finally, 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. 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.

[0123] Figure 9 It is a schematic diagram of an earthquake early warning network system 90 provided by an embodiment of the present invention. As Figure 9 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 the earthquake early warning network. When the processor 91 executes the computer program 93, the steps in the above-mentioned embodiments of the data analysis method for each earthquake early warning network are implemented, such as Figure 1 the steps S101 to S104 shown. Alternatively, when the processor 91 executes the computer program 93, the functions of each module in the above-mentioned device embodiments are implemented, such as Figure 8 the functions of the modules 81 to 84 shown.

[0124] Exemplarily, the computer program 93 can be divided into one or more modules / units, which are stored in the memory 92 and executed by the processor 91 to implement the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 93 in the earthquake early warning network system 90.

[0125] 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 can understand that Figure 9 merely being examples of the earthquake early warning network system 90, they do not constitute a limitation on the earthquake early warning network system 90. It may include more or fewer components than those shown in the figure, or combine certain components, or have different components. For example, the earthquake early warning network system 90 may further include input / output devices, network access devices, a bus, etc.

[0126] The so-called processor 91 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0127] The memory 92 may be an internal storage unit of the earthquake early warning network system 90, such as the 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 media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the earthquake early warning network system 90. Further, the memory 92 may also include both the internal storage unit and the 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 will be output.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, 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. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0129] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0131] In the embodiments provided by the present invention, it should be understood that the disclosed device / system and method can be implemented in other ways. For example, the device / earthquake early warning network system embodiments described above are only illustrative. For example, the division of the above-mentioned modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0134] If the above-mentioned 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 such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, 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.

[0135] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various 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, Including: Obtain the 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; For any two types of observation instruments, perform observation point matching according to the positions of the respective observation instruments to obtain multiple pairs of observation points; among them, in the earthquake early warning network, multiple observation instruments of each type are deployed, and each observation instrument serves as an observation point; Extract feature quantities from the observation data of each observation point, and based on the feature quantities, judge the observation differences of each pair of observation points; based on the observation differences of each pair of observation points, determine the observation differences between the two types of observation instruments; Based on the observation differences of all different types of observation instruments, determine the calibration strategy for the observation data of different observation instruments in the earthquake early warning network; The step of, for any two types of observation instruments, performing observation point matching according to the positions of the observation instruments to obtain multiple pairs of observation points includes: For any one observation point of one type, select, from each observation point of the other type, the observation point with a distance less than a preset threshold and the smallest distance, and form a pair of observation points; Among them, the preset threshold is determined according to the following method: Calculate the average distance between the two selected observation points and the epicenter; Determine the preset threshold according to the average distance; among them, the preset threshold is negatively correlated with the average distance.

2. The data analysis method of the earthquake early warning network according to claim 1, characterized in that The step of extracting feature quantities from the observation data of each observation point and, based on the feature quantities, judging the observation differences of each pair of observation points 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 differences of each pair of observation points; Correspondingly, the step of, based on the observation differences of each pair of observation points, determining the observation differences between the two types of observation instruments includes: Calculate the average value of the differences in peak accelerations of each pair of observation points to obtain a statistic, and determine the observation differences between the two types of observation instruments according to the statistic.

3. The data analysis method of the earthquake early warning network according to claim 2, wherein The step of determining the observation differences between the two types of observation instruments according to the statistic includes: Determine the statistic distribution through a permutation experiment; Determine 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, determine that there are observation differences between the two types of observation instruments; otherwise, determine that there are no observation differences between the two types of observation instruments.

4. The data analysis method of the earthquake early warning network according to any one of claims 1 to 3, characterized in that After obtaining the observation data of different observation instruments in the earthquake early warning network, the method further includes: Preprocess the observation data of different observation instruments and convert them into the same type of data.

5. The data analysis method of the earthquake early warning network according to any one of claims 1 to 3, characterized in that, The at least two types of observation instruments include: seismographs, strong motion instruments, and intensity meters.

6. A data analysis device for an earthquake early warning network, characterized in that, Including: An acquisition module, configured to obtain the 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; A matching module, configured to, for any two types of observation instruments, perform observation point matching according to the positions of the respective observation instruments to obtain multiple pairs of observation points; among them, in the earthquake early warning network, multiple observation instruments of each type are deployed, and each observation instrument serves as an observation point; A calculation module, configured to extract feature quantities from the observation data of each observation point, and determine the observation differences between each pair of observation points according to the feature quantities; and determine the observation differences between the two types of observation instruments according to the observation differences between each pair of observation points. A processing module, configured to determine a calibration strategy for 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. For any two types of observation instruments, observation points are matched according to the positions of the observation instruments to obtain multiple pairs of observation points, including: For any one observation point of one type, an observation point with a distance less than a preset threshold and the smallest distance is selected from each observation point of the other type to form a pair of observation points. Wherein, the preset threshold is determined according to the following method: Calculate the average distance between the two selected observation points and the earthquake epicenter. Determine the preset threshold according to the average distance; wherein, the preset threshold is negatively correlated with the average distance.

7. 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 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images