A Multi-Information Source Mechanism Calculation Method Based on Earthquake Early Warning Network Observations

Abstract

This invention discloses a multi-information source mechanism calculation method based on earthquake early warning station network observations. The method includes selecting velocity and acceleration recorded waveforms to form an input database; obtaining the P-wave initial motion polarity through manual picking and template matching techniques; calculating the source mechanism using a grid scanning method, and using solutions with an inconsistency ratio below a threshold as preliminary solutions; manually screening P-wave and S-wave phase waveforms; and jointly using initial motion polarity, S / P amplitude ratio, and body wave waveform as constraints to calculate the probability value of each preliminary solution. The probability distribution of clustered solutions is obtained through the Akaike information criterion and cluster analysis to obtain the optimal source mechanism calculation result. This invention, based on joint inversion calculations using acceleration records from early warning stations and velocity records from seismic stations, lowers the lower limit of the magnitude of earthquakes with computable source mechanisms. The comprehensive use of three types of information constraints improves the reliability of the results, and the calculation of the probability distribution of clustered solutions reduces the adverse effects of multiple solutions, thus improving the stability of the results.

Description

Technical Field

[0001] This invention belongs to the field of seismology technology and relates to a method for calculating earthquake source parameters inversion, and in particular to a method for calculating multi-information source mechanisms based on earthquake early warning station network observations. Background Technology

[0002] The focal mechanism solution contains basic information about the rupture surface morphology and slip direction of an earthquake event. It can be used to determine the fault geometry, motion state, and rupture mode of an earthquake, providing crucial basic data for analyzing fault zone structure, estimating regional stress field changes, and assessing earthquake hazards. For earthquakes of larger magnitude, the focal mechanism solution can be calculated using waveform fitting methods, while for smaller earthquakes, the focal mechanism solution is often calculated using spatial distribution based on the initial polarity of P-waves combined with S / P amplitude ratio data. According to the dual-couple mechanical model, the focal mechanism can be represented by three variables: strike angle, dip angle, and slip angle. The strike angle represents the strike of the earthquake-generating fault, rotated clockwise with N as 0 degrees; the dip angle represents the angle between the earthquake-generating fault and the horizontal plane, ranging from 0 to 90 degrees; and the slip angle represents the relative sliding mode between the two sides of the earthquake-generating fault, which is the angle between the slip direction of the hanging wall relative to the footwall and the projection of the fault strike onto the footwall.

[0003] The main challenge in calculating the focal mechanism solution of small earthquakes lies in the limited number of stations with identifiable P-wave first motion polarities. This makes it difficult to impose sufficient constraints on the calculation process, resulting in multiple solutions and increasing the instability of the research results. To improve the stability and reliability of the calculated focal mechanism solutions for small earthquakes, seismologists have attempted two approaches. One is to increase the number of P-wave first motion polarities. Using manually identified first motion polarities as a training set, deep learning methods can automatically identify P-wave first motion polarities. Although the main goal of automatic identification is to quickly calculate the focal mechanism online, studies have shown that deep learning methods can identify more P-wave first motion polarities than manual methods, thereby increasing the number of earthquakes from which focal mechanisms can be calculated. For a group of earthquake pairs or swarms with close epicenters, waveform cross-correlation template matching technology can assist in the identification of P-wave first motion polarities. This method has also been applied to the calculation of focal mechanisms for multiple earthquake sequences. The other approach is to fully exploit the information in the earthquake waveform. The P-wave first motion is a simplified record from a station, represented by an upward or downward symbol, containing only 1 bit of information. The complete waveform carries far more information than the initial motion polarity, and incorporating waveforms into the calculation can better constrain the focal mechanism solution. Preliminary attempts have been made to apply methods such as using compressed sensing to reconstruct the seismic wavefield, introducing relative waveform changes into the objective function, and calculating the S / P amplitude ratio using the P / P and S / S amplitudes of similar waveform earthquake pairs to calculate the focal mechanism. For small earthquakes, using only one type of information—initial motion polarity, amplitude ratio, or seismic waveform—to calculate the focal mechanism still results in significant uncertainties. Therefore, it is necessary to design methods that combine multiple types of information to calculate the focal mechanism.

[0004] Currently, most data used for calculating focal mechanisms are velocity waveforms recorded by seismometers, derived from fixed seismic networks or mobile observation networks. With the implementation of the National Intensity Rapid Reporting and Early Warning Project, a nationwide earthquake early warning network has been established and is operational, providing new data for focal mechanism calculations. The station density of the early warning network is much greater than that of the seismic network. For example, in the Beijing-Tianjin-Hebei region, the seismic network has 134 continuously observed stations, while the early warning network has 1653. Using such dense observational data can effectively improve the reliability of focal mechanism calculation results. Unlike the velocity records used previously, the early warning network records acceleration waveforms. Since the early warning network is relatively new, methods for calculating focal mechanisms using the densely observed acceleration records from the early warning network are rarely reported. Developing suitable calculation methods for this new type of observational data is an effective way to improve the stability and reliability of focal mechanism calculation results. Summary of the Invention

[0005] The purpose of this invention is to propose a novel method for calculating the focal mechanism of small earthquakes. This method utilizes acceleration seismic waveform data recorded by earthquake early warning stations, and integrates three types of information—the spatial distribution of P-wave initial polarity, the consistency of the S / P amplitude ratio, and the similarity of the direct body wave phase waveforms—to constrain the calculation results. Furthermore, the Akaike information criterion is used to provide a probability distribution regarding the potential for multiple solutions to the focal mechanism. This invention effectively lowers the lower limit of the magnitude of small earthquakes with calculable focal mechanisms and overcomes the uncertainty caused by multiple solutions in the calculation results, thereby improving the stability and reliability of focal mechanism calculations.

[0006] This invention proposes a multi-information source mechanism calculation method based on earthquake early warning station network observations, for calculating the source mechanism of small earthquakes. The technical solution is as follows:

[0007] (1) Select stations within 300km of the earthquake epicenter, including acceleration waveforms recorded by strong motion instruments of the earthquake early warning network and velocity waveforms recorded by seismographs of the seismological network. Extract the seismic waveform of each station from 60s before the earthquake, with a length of 180s. The seismic waveform of each station includes three components: vertical (Z direction), north-south (N direction), and east-west (E direction), to form the calculation input database.

[0008] (2) Obtaining the initial polarity of the P-wave;

[0009] 2-1) First, the polarity of the first motion of the P-wave in the calculated earthquake is picked up manually. For stations with both velocity and acceleration observations at the same site, the velocity record waveform of the seismic network is used first to pick up the polarity of the first motion of the P-wave. If the velocity record is not clear, the acceleration record is used as a reference. For stations with only acceleration observations, the acceleration record waveform is used directly to pick up the polarity of the first motion of the P-wave.

[0010] 2-2) The earthquake whose focal mechanism is to be calculated is taken as the target earthquake. Then, an earthquake with a magnitude at least 0.5 greater than the target earthquake's epicenter is selected as a template. Waveform cross-correlation template matching technology is used to identify the initial motion polarity that cannot be clearly identified manually.

[0011] 2-3) Verify the initial polarity of the P-wave manually identified by the same station and identified by the waveform cross-correlation template matching technology. If the results are inconsistent, manually confirm the initial polarity of the P-wave again.

[0012] (3) Calculate the preliminary solution of the focal mechanism;

[0013] 3-1) Based on the P-wave initial motion polarity obtained in step (2), the focal mechanism solution is calculated using the grid scanning method. During the calculation process, the initial motion polarity of up to one station can be deleted or reversed.

[0014] 3-2) Set all solutions with an optimal inconsistency ratio increase of less than 0.1 as candidate preliminary solutions, arrange them in ascending order of inconsistency ratio, select the top 500 as preliminary solutions for focal mechanism calculation, and record the initial motion polarity inconsistency ratio of each preliminary solution;

[0015] (4) Body wave waveform screening;

[0016] 4-1) Select stations within 100km of the earthquake epicenter. If the station records velocity waveforms from surface broadband seismometers, directly pass them through a 1-5Hz bandpass filter. If the station records acceleration waveforms from strong motion meters, first remove the mean and trend from the acceleration waveforms, then integrate them to the velocity waveforms, and then pass them through a 1-5Hz bandpass filter.

[0017] 4-2) Manually screen stations with high signal-to-noise ratios and clear recorded waveforms for direct P-wave and S-wave phases. Rotate the three-component waveforms to the ZRT coordinate system and extract the waveforms from 0.2s to 0.5s before the arrival of the Z-component P-wave and from 0.2s to 0.5s after the arrival of the RT-component S-wave for use in the next waveform fitting calculation.

[0018] (5) Multi-information fitting calculation;

[0019] 5-1) Based on the preliminary solution of the focal mechanism obtained in step (3), calculate the theoretical seismogram of the station selected in step 4-2), and pass it through a 1-5Hz bandpass filter;

[0020] 5-2) Calculate the waveform cross-correlation coefficient of the theoretical seismogram of the phases selected in 4-2). For stations that simultaneously select P-waves and S-waves, calculate the S / P amplitude ratio using the maximum amplitude of the T-component SH-wave and the maximum amplitude of the Z-component P-wave. Then calculate the amplitude ratio similarity coefficient between the theoretical seismogram and the observed waveform.

[0021] 5-3) Calculate the multi-information fitting coefficients of the preliminary solution for each source mechanism using the initial polarity inconsistency ratio, waveform cross-correlation coefficient, and amplitude ratio similarity coefficient;

[0022] (6) Calculation of probability distribution of cluster solution for focal mechanism;

[0023] 6-1) Using the maximum value of the multi-information fitting coefficient minus 0.02 as the threshold, the preliminary solutions of the focal mechanism with fitting coefficients higher than the threshold are used to participate in the clustering calculation;

[0024] 6-2) The standard for clustering calculation is that the difference between the strike angle, dip angle, and slip angle is no greater than 30°. The Akaike Information Criterion is used to calculate the AIC value of the preliminary solution of the focal mechanism for each participating cluster, and then the probability value of each preliminary solution is calculated;

[0025] 6-3) The probabilities of each solution in each class are added together to obtain the probability of the whole class. The mean of the strike angle, dip angle and slip angle of each node is taken as the focal mechanism solution of the whole class, that is, the probability distribution of the cluster solution is obtained.

[0026] 6-4) If the highest probability in the cluster solution exceeds 0.5, then it is taken as the optimal solution for the focal mechanism of this earthquake. If no cluster has a probability exceeding 0.5, then this earthquake is considered to have multiple solutions, and the cluster solution with the highest probability is the calculation result of the focal mechanism of this earthquake.

[0027] The beneficial effects and advantages of the present invention are as follows:

[0028] (1) By using the acceleration waveform recorded by the dense earthquake early warning station network and the velocity waveform recorded by the seismic network, the focal mechanism solution of the earthquake can be jointly inverted and calculated, thereby lowering the lower limit of the magnitude of small earthquakes with computable focal mechanisms and obtaining more focal mechanism calculation results for small earthquakes.

[0029] (2) The calculation of the source mechanism is constrained by three types of information: the spatial distribution of the initial polarity of the P wave, the consistency of the S / P amplitude ratio, and the similarity of the phase waveform of the direct body wave, which improves the reliability of the calculation results.

[0030] (3) The probability value of the preliminary solution of each source mechanism is calculated using the Akaike Information Criterion. The cluster solution with the highest probability is used as the result of the source mechanism calculation through clustering calculation, which reduces the adverse effect of multiple solutions on the calculation results and improves the stability of the results. Attached Figure Description

[0031] Figure 1 This is a schematic diagram illustrating the principle of the calculation method used in the embodiments of the present invention;

[0032] Figure 2 This document presents a practical application of the invention, including earthquake examples and calculation results. Detailed Implementation

[0033] This invention addresses the problem of calculating the focal mechanism of small earthquakes. Based on waveform data recorded by earthquake early warning observation networks, it combines acceleration waveforms recorded by these networks with velocity waveforms recorded by seismograph networks. It utilizes waveform cross-correlation template matching technology to detect the P-wave first motion polarity, which is difficult to identify manually. The calculation results are constrained by three types of information: the spatial distribution of P-wave first motion polarity, the consistency of the S / P amplitude ratio, and the similarity of the direct body wave phase waveforms. Furthermore, the Akaike information criterion is used to provide the probability distribution of possible multiple solutions for the focal mechanism. Compared with existing methods, this calculation method lowers the lower limit of the magnitude of small earthquakes for focal mechanism calculation and improves the reliability and stability of the calculation results.

[0034] The specific calculation steps are as follows:

[0035] (1) Select stations within 300km of the epicenter and extract the seismic waveform of each station from 60s before the earthquake, with a length of 180s. The seismic waveform of each station includes three components: vertical, north-south and east-west, and form the calculation input database.

[0036] (2) Obtaining the initial polarity of the P-wave;

[0037] 2-1) First, the polarity of the first motion of the P-wave in the calculated earthquake is picked up manually. For stations with both velocity and acceleration observations at the same site, the velocity record waveform of the seismic network is used first to pick up the polarity of the first motion of the P-wave. If the velocity record is not clear, the acceleration record is used as a reference. For stations with only acceleration observations, the acceleration record waveform is used directly to pick up the polarity of the first motion of the P-wave.

[0038] 2-2) The earthquake whose focal mechanism is to be calculated is taken as the target earthquake. Then, an earthquake with a magnitude at least 0.5 greater than the target earthquake's epicenter is selected as a template. Waveform cross-correlation template matching technology is used to identify the initial motion polarity that cannot be clearly identified manually. The specific calculation process of template matching is as follows: Take the waveforms of 61 sampling points from 0.3s before to 0.3s after the arrival of the P wave in the Z component records of the template earthquake and the target earthquake, respectively. After passing through a 1-10Hz bandpass filter, calculate the cross-correlation function of the two waveforms. When the maximum cross-correlation value is greater than 0.7 and the absolute difference between the maximum and minimum values ​​is greater than 0.1, it is considered that the initial motion polarity of the target earthquake and the template earthquake at this station is consistent, and the target earthquake is marked as having the same initial motion polarity as the template earthquake. Conversely, when the minimum cross-correlation value is less than -0.7 and the absolute difference between the minimum and maximum values ​​is greater than 0.1, it is considered that the polarities are opposite.

[0039] 2-3) Verify the initial polarity of the P-wave manually identified by the same station and identified by the waveform cross-correlation template matching technology. If the results are inconsistent, manually confirm the initial polarity of the P-wave again.

[0040] (3) Calculate the preliminary solution of the focal mechanism;

[0041] 3-1) Based on the P-wave initial motion polarity obtained in step (2), the focal mechanism solution is calculated using the grid scanning method. During the calculation process, the initial motion polarity of up to one station can be deleted or reversed.

[0042] 3-2) Set all solutions with an optimal inconsistency ratio increase of less than 0.1 as candidate preliminary solutions, arrange them in ascending order of inconsistency ratio, select the top 500 as preliminary solutions for focal mechanism calculation, and record the initial motion polarity inconsistency ratio of each preliminary solution;

[0043] (4) Body wave waveform screening;

[0044] 4-1) Select stations within 100km of the earthquake epicenter. If the station records velocity waveforms from surface broadband seismometers, directly pass them through a 1-5Hz bandpass filter. If the station records acceleration waveforms from strong motion meters, first remove the mean and trend from the acceleration waveforms, then integrate them to the velocity waveforms, and then pass them through a 1-5Hz bandpass filter.

[0045] 4-2) Manually screen stations with high signal-to-noise ratios and clear recorded waveforms for direct P-wave and S-wave phases. Rotate the three-component waveforms to the ZRT coordinate system and extract the waveforms from 0.2s to 0.5s before the arrival of the Z-component P-wave and from 0.2s to 0.5s after the arrival of the RT-component S-wave for use in the next waveform fitting calculation.

[0046] (5) Multi-information fitting calculation;

[0047] 5-1) Based on the preliminary solution of the focal mechanism obtained in step (3), calculate the theoretical seismogram of the station selected in step 4-2), and pass it through a 1-5Hz bandpass filter;

[0048] 5-2) Calculate the waveform cross-correlation coefficient of the theoretical seismogram of the phases selected in 4-2). For stations that simultaneously select P-waves and S-waves, calculate the S / P amplitude ratio using the maximum amplitude of the T-component SH-wave and the maximum amplitude of the Z-component P-wave. Then calculate the amplitude ratio similarity coefficient between the theoretical seismogram and the observed waveform.

[0049] 5-3) Using the initial polarity inconsistency ratio, waveform cross-correlation coefficient, and amplitude ratio similarity coefficient, calculate the multi-information fitting coefficients of the preliminary solutions for each source mechanism. The multi-information fitting coefficient MSFit of the i-th preliminary solution is... i The expression is MDB i CC represents the contradiction ratio of 1 minus the i-th preliminary solution. ij RA represents the cross-correlation coefficient of the i-th preliminary solution for the j-th phase waveform. ikLet M represent the amplitude ratio similarity coefficient of the i-th preliminary solution at the k-th station. All three values ​​are between 0 and 1. M represents the number of stations with P-wave initial motion polarity, N represents the number of selected seismic phases, and P represents the number of stations from which the amplitude ratio coefficient can be calculated. All three values ​​are positive integers. Thus, the multi-information fitting coefficient of each preliminary solution is obtained. Its value is between 0 and 1. The larger the value, the better the fit between the theoretical seismogram and the actual observation data.

[0050] (6) Calculation of probability distribution of cluster solution for focal mechanism;

[0051] 6-1) Using the maximum value of the multi-information fitting coefficient minus 0.02 as the threshold, the preliminary solutions of the focal mechanism with fitting coefficients higher than the threshold are used to participate in the clustering calculation;

[0052] 6-2) The standard for clustering calculation is that the difference in strike angle, dip angle, and slip angle should not exceed 30°. The Akaike Information Criterion is used to calculate the AIC value of the preliminary solution of the focal mechanism for each participating cluster, and then the probability value of each preliminary solution is calculated. The AIC difference ΔAIC is then calculated. i The expression is

[0053] ΔAIC i =N·[log(MSFit) max )-log(MSFit i )], where MSFit max This represents the maximum value of the multi-information fitting coefficients in the initial solutions used for clustering.

[0054] 6-3) The probabilities of each solution in each cluster are summed to obtain the probability of the entire cluster. The mean of the strike angle, dip angle, and slip angle of each node is taken as the focal mechanism solution of the entire cluster, thus obtaining the probability distribution of the cluster solution, the mathematical expression of which is: Where L represents the number of preliminary solutions for the focal mechanism participating in the clustering;

[0055] 6-4) If the highest probability in the cluster solution exceeds 0.5, then it is taken as the optimal solution for the focal mechanism of this earthquake. If no cluster has a probability exceeding 0.5, then this earthquake is considered to have multiple solutions, and the cluster solution with the highest probability is the calculation result of the focal mechanism of this earthquake.

[0056] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart illustrating the calculation method proposed in this invention.

[0058] Figure 2 The incident occurred at 17:20:39.7 on September 21, 2022, in Lulong, Hebei Province. L2.6 Calculation results of earthquake focal mechanism. According to step (2), the P-wave initial motion polarity of 16 stations was identified through manual identification and waveform cross-correlation template matching technology. According to step (3), 500 preliminary focal mechanism solutions were calculated (left figure). According to step (4), 31 body wave phase waveforms of 13 stations were selected. The P-wave initial motion polarity, the selected phase waveforms and amplitude ratio were substituted into the body wave fitting calculation in step (5) to obtain the probability value of each preliminary solution. Then, step (6) was used to calculate the cluster solution and its probability distribution. As shown in the right figure, the final three cluster solutions were obtained, namely cluster solution 1: strike 38°, dip 81°, slip angle 146°, probability 0.18; cluster solution 2: strike 139°, dip 49°, slip angle -11°, probability 0.66; and cluster solution 3: strike 159°, dip 71°, slip angle -36°, probability 0.16. Since the probability of cluster solution 2 exceeds 0.5, it is taken as the optimal solution for the focal mechanism of this earthquake (the black part in the right figure represents its focal mechanism interface).

Claims

1. A method for calculating multi-information source mechanisms based on earthquake early warning station network observations, characterized in that, Includes the following steps: (1) Select stations within 300km of the earthquake epicenter, including acceleration waveforms recorded by strong motion instruments of the earthquake early warning network and velocity waveforms recorded by seismographs of the seismological network. Extract the seismic waveforms of each station from 60s before the earthquake, with a length of 180s. The seismic waveforms of each station include three components: vertical Z-direction, north-south N-direction, and east-west E-direction, to form the calculation input database. (2) Obtaining the initial polarity of the P-wave: 2-1) First, the polarity of the first motion of the P-wave in the calculated earthquake is picked up manually. For stations with both velocity and acceleration observations at the same site, the velocity record waveform of the seismic network is used first to pick up the polarity of the first motion of the P-wave. If the velocity record is not clear, the acceleration record is used as a reference. For stations with only acceleration observations, the acceleration record waveform is used directly to pick up the polarity of the first motion of the P-wave. 2-2) The earthquake whose focal mechanism is to be calculated is taken as the target earthquake. Then, an earthquake within 10km of the epicenter of the target earthquake and with a magnitude at least 0.5 greater is selected as the template. Waveform cross-correlation template matching technology is used to identify the initial motion polarity that cannot be clearly identified manually. 2-3) Verify the initial polarity of the P-wave manually identified by the same station and identified by the waveform cross-correlation template matching technology. If the results are inconsistent, manually confirm the initial polarity of the P-wave again. (3) Calculation of preliminary solution for focal mechanism: 3-1) Based on the P-wave initial motion polarity obtained in step (2), the focal mechanism solution is calculated using the grid scanning method. During the calculation process, the initial motion polarity of up to one station can be deleted or reversed. 3-2) Set all solutions with an optimal inconsistency ratio increase of less than 0.1 as candidate preliminary solutions, arrange them in ascending order of inconsistency ratio, select the top 500 as preliminary solutions for focal mechanism calculation, and record the initial motion polarity inconsistency ratio of each preliminary solution; (4) Body wave waveform screening: 4-1) Select stations within 100km of the earthquake epicenter. If the station records velocity waveforms from surface broadband seismometers, directly pass them through a 1-5Hz bandpass filter. If the station records acceleration waveforms from strong motion meters, first remove the mean and trend from the acceleration waveforms, then integrate them to the velocity waveforms, and then pass them through a 1-5Hz bandpass filter. 4-2) Manually screen stations with high signal-to-noise ratios and clear recorded waveforms for direct P-wave and S-wave phases. Rotate the three-component waveforms to the ZRT coordinate system and extract the waveforms from 0.2s to 0.5s before the arrival of the Z-component P-wave and from 0.2s to 0.5s after the arrival of the RT-component S-wave for use in the next waveform fitting calculation. (5) Multi-information fitting calculation: 5-1) Based on the preliminary solution of the focal mechanism obtained in step (3), calculate the theoretical seismogram of the station selected in step 4-2), and pass it through a 1-5Hz bandpass filter; 5-2) Calculate the waveform cross-correlation coefficient of the theoretical seismograms of the phases selected in 4-2). For stations that simultaneously select P-waves and S-waves, calculate the S / P amplitude ratio using the maximum amplitude of the T-component SH-wave and the maximum amplitude of the Z-component P-wave. Then calculate the amplitude ratio similarity coefficient between the theoretical seismograms and the observed waveforms. 5-3) Calculate the multi-information fitting coefficients of the preliminary solution for each source mechanism using the initial polarity inconsistency ratio, waveform cross-correlation coefficient, and amplitude ratio similarity coefficient; (6) Calculation of probability distribution of focal mechanism clustering solution: 6-1) Using the maximum value of the multi-information fitting coefficient minus 0.02 as the threshold, the preliminary solutions of the focal mechanism with fitting coefficients higher than the threshold are used to participate in the clustering calculation; 6-2) The standard for clustering calculation is that the difference between the strike angle, dip angle and slip angle is no greater than 30°. The Akaike Information Criterion is used to calculate the AIC value of the preliminary solution of the focal mechanism of each participating cluster, and then the probability value of each preliminary solution is calculated. 6-3) The probability of the entire class is obtained by summing the probabilities of each solution in each class. The mean of the strike angle, dip angle and slip angle of each solution is taken as the focal mechanism solution of the entire class, that is, the probability distribution of the cluster solution is obtained. 6-4) If the highest probability in the cluster solution exceeds 0.5, then it is taken as the optimal solution for the focal mechanism of this earthquake. If no cluster has a probability exceeding 0.5, then this earthquake is considered to have multiple solutions, and the cluster solution with the highest probability is the calculation result of the focal mechanism of this earthquake.

2. The multi-information source mechanism calculation method based on earthquake early warning station network observation as described in claim 1, characterized in that: Applying step 5-3), calculate the multi-information fitting coefficients of the preliminary solution for the focal mechanism. The multi-information fitting coefficient MSFit of the i-th preliminary solution... i The mathematical expression is MDB i CC represents the ratio of the initial polarity contradiction between 1 and the i-th preliminary solution. ij RA represents the cross-correlation coefficient of the i-th preliminary solution for the j-th phase waveform. ik Let M represent the amplitude ratio similarity coefficient of the i-th preliminary solution at the k-th station, M represent the number of stations that picked up the initial polarity of the P-wave, N represent the number of seismic phases selected in step 4-2), and P represent the number of stations for which the amplitude ratio coefficient can be calculated.

3. The multi-information source mechanism calculation method based on earthquake early warning station network observation as described in claim 1, characterized in that: The mathematical expression for calculating the AIC value of the preliminary solution of the focal mechanism using step 6-2 is as follows: MSFit max To find the maximum value of the multi-information fitting coefficients in the preliminary solutions for clustering, calculate the probability value ω of the i-th preliminary solution. i The mathematical expression is , where L is the number of preliminary solutions for the focal mechanism participating in the clustering.

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