A method and system for selecting strong earthquake records near the source considering slip and thrust effects

Abstract

The present invention relates to the field of earthquake recording technology, and in particular to a method and system for selecting near-source strong earthquake records considering slip effects. The method comprises the following steps: introducing permanent displacement as a conditional parameter into seismic motion selection based on a generalized conditional intensity index, and using an extended probabilistic fault displacement hazard analysis method to determine a target permanent displacement; replacing the commonly used spectral acceleration with spectral displacement in a target seismic intensity index set to characterize the spectral components of seismic motion; further calculating and determining an empirical correlation coefficient applicable to permanent displacement-type near-source strong earthquake records, constructing a corresponding conditional intensity index target distribution, and finally performing optimal selection from a near-source strong earthquake database to obtain a permanent displacement-type near-source strong earthquake dataset that best matches the target conditional distribution. The present invention can provide a reasonable input seismic motion selection method for seismic response analysis of near-source engineering structures under the coupling of strong earthquake and fault motion.

Description

Technical Field

[0001] The present invention relates to the field of earthquake recording technology, and in particular to a method and system for selecting near-source strong earthquake records taking into account slip and thrust effects. Background Art

[0002] Numerous earthquake disasters have demonstrated that stick-slip dislocations on active faults can extend to the surface, causing significant permanent deformation and consequently severe damage to engineering structures. Surface fault motion manifests as a slip effect in near-source strong motion records. This effect induces a unidirectional velocity pulse in the velocity history parallel to the fault slip direction. Simultaneously, the displacement time history curve exhibits a step-like characteristic, with a permanent displacement at the end (the slip amplitude) included. Therefore, permanent displacement near-source strong motion records are of great value for studying strong earthquake fault rupture processes and coseismic surface deformation.

[0003] Current research on using permanent displacement seismic motion (i.e., permanent displacement resulting from the slip effect) as input for seismic response analysis focuses on: 1. From the perspective of strong earthquake record data processing, a baseline correction method based on the target permanent displacement is used to adjust the slip effect amplitude (permanent displacement) corresponding to the seismic motion record. A series of permanent displacement seismic motions processed by this baseline correction method are then used as input loads to explore the impact of different fault slip levels on the seismic response of cross-fault bridges. 2. Seismic motion simulation methods can be further divided into: (I) permanent displacement seismic motion simulation with a superimposed slip effect velocity pulse function, and (II) broadband hybrid seismic motion simulation methods that directly reflect the slip effect in the simulation results. However, no seismic motion selection method that considers the slip effect amplitude has yet to be proposed.

[0004] The seismic motion selection method is mainly based on "spectrum matching", and the target spectra involved include: design spectrum, consistent hazard spectrum, conditional mean spectrum, etc. However, the potential destructive power of seismic motion is difficult to fully describe by a single seismic motion intensity index, so relying solely on matching target spectra for seismic motion selection has certain limitations. The standard seismic motion data processing process (baseline correction combined with bandpass filtering) is difficult to retain permanent displacement information. The excessive manual intervention involved in the determination criteria of the segmented time points in the early multi-segment baseline correction method may cause errors in the recovery results of permanent displacement. In addition, while high-pass filtering eliminates long-period noise in the acceleration record, it also filters out the low-frequency information corresponding to the permanent displacement in the original record. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for selecting near-source strong earthquake records taking into account the slip effect, which can also incorporate other seismic motion characteristics (such as duration and cumulative energy) into the seismic response analysis of the structure. Under the theoretical framework of the generalized seismic motion intensity index, seismic motion selection is completed by matching the multivariate conditional distribution constructed for any target seismic motion intensity index set.

[0006] The present application provides a method for selecting strong earthquake records at a near-source earthquake taking into account the slip effect, including the following steps:

[0007] S1. Based on the basic information of the target fault and site, the extended probabilistic fault displacement hazard analysis is used to determine the permanent displacement target value corresponding to the specified exceedance probability level. Combined with the permanent displacement mean value given by the ground motion prediction model of the slip effect under the set earthquake rupture scenario, the standard deviation coefficient corresponding to the permanent displacement is determined as a conditional parameter.

[0008] S2. Under the assumed earthquake rupture scenario, select a seismic motion prediction model applicable to permanent displacement type near-source strong earthquake records to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of each location in the target seismic intensity indicator set;

[0009] S3. Based on the permanent displacement type near-source strong earthquake database, calculate the standard deviation correlation coefficient matrix between any earthquake motion parameter indicators applicable to permanent displacement type near-source strong earthquake motions;

[0010] S4. Based on the basic concept of the generalized seismic intensity index, the conditional mean and conditional standard deviation of the seismic intensity index in each region are calculated, and the multivariate conditional distribution of the generalized seismic intensity index is further constructed.

[0011] S5. Approximately randomly extract multiple sets of target simulation vectors from the target multivariate conditional distribution using the Latin hypercube sampling method, and search the permanent displacement type near-source strong earthquake database one by one for the candidate ground motion dataset with the smallest error with each set of target simulation vectors;

[0012] S6. Use the R value obtained by weighted summation of the statistic D value in the KS test to measure the deviation between the distribution of each alternative sample and the target conditional distribution, and finally take the alternative data set with the smallest R value as the final selection result.

[0013] Furthermore, the basic information of the target fault and site in S1 includes the fault type, fault length, fault dip, minimum magnitude that affects the project site, maximum potential earthquake magnitude, average annual earthquake occurrence rate, b value in the Gutenberg-Richter relationship, and average shear wave velocity within 30 m below the surface of the site; the set earthquake rupture scenario includes the set magnitude, the upper boundary depth of the fault rupture surface, and the shortest distance from the site to the fault rupture surface; the standard deviation coefficient ε corresponding to the conditional parameter PD lnPD is defined as follows:

[0014]

[0015] Among them, under the earthquake rupture scenario Rup, the mean value of PD μ lnPD and standard deviation σ lnPD It can be given by the corresponding earthquake motion prediction model, while lnPD is the target value under the specified exceedance probability level given by the probabilistic fault displacement hazard analysis method extended in S1.

[0016] Furthermore, any two earthquake intensity indices IM in S3 i and IM j The correlation coefficient can be calculated by the standard deviation coefficient ε lnIMi and ε lnIMj The correlation coefficient between them is replaced by the specific definition as follows:

[0017]

[0018] The actual rupture scenario information corresponding to each strong motion record in the permanent displacement type near-source strong earthquake database is recorded as rup i The selected earthquake prediction model gives the earthquake intensity index IM of each place. i The predicted mean is denoted as μ lnIMi|(rupi) and standard deviation σ lnIMi , combined with the actual value lnIM i (Geometric mean of the two horizontal components) Determine the standard deviation coefficient ε lnIMi ; n is the total number of strong motion records contained in the permanent displacement type near-source strong motion database, and They are the standard deviation coefficient ε lnIMi and ε lnIMj The corresponding sample mean.

[0019] Furthermore, the conditional mean and conditional standard deviation of the vibration intensity index of each location in S4 are given by the following definitions:

[0020]

[0021] Among them, the earthquake motion prediction model corresponding to the vibration intensity index of each place can give the mean μ under the set earthquake rupture scenario Rup lnIMi and standard deviation σ lnIMi ρ lnIMi,lnPD|Rup for lnIM i Correlation coefficient with lnPD.

[0022] Furthermore, in S5, since the dimensions of the parameters in the target ground motion intensity index set IM are different, the errors corresponding to the parameters are normalized by standard deviation and then the error function is constructed in the form of weighted sum of squares. The specific form is as follows:

[0023]

[0024] Among them, N IMi is the number of parameters in the target ground motion intensity index set IM; lnIM i nism and lnIM i m,scaled Respectively represent the target simulation vector of the nsim item {IM i} and the mth record in the earthquake motion database after amplitude modulation {IM i};w i In order to assign error weight coefficients to the seismic intensity indicators of various places, differentiated adjustments can be made according to the different levels of importance of the seismic intensity indicators.

[0025] Furthermore, the R value obtained by weighted summation of the statistic D value in the KS test in S6 is defined as:

[0026]

[0027] Among them, F IMi|PD (im i |pd) is the target GCIM distribution; ECDF(im i ) represents the empirical cumulative distribution function corresponding to the i-th earthquake motion intensity index in the candidate earthquake motion dataset; w i The weight coefficient can be kept consistent with the error function or reassigned; finally, the candidate seismic data set with the smallest R value is regarded as the optimal result that meets the target condition distribution and is output.

[0028] A system for selecting strong earthquake records from near-seismic sources taking into account the slip effect is constructed based on the above-mentioned method for selecting strong earthquake records from near-seismic sources, including a calculation module for executing the above-mentioned method for selecting strong earthquake records from near-seismic sources taking into account the slip effect.

[0029] The present invention has the following beneficial effects: It uses permanent displacement, which characterizes the degree of fault dislocation, as a conditional parameter to construct a target conditional distribution, achieving a good match between the sample distribution of a selected dataset of permanent displacement-type near-source strong earthquake records and the target conditional distribution. Furthermore, the present invention fully considers the influence of permanent displacement (slip effect amplitude) on the low-frequency components of ground motion, and calculates a correlation coefficient matrix more suitable for permanent displacement-type near-source strong earthquake records. This ensures the reliability of the seismic response of near-source engineering structures under the coupling of strong earthquakes and fault dislocation when the ground motion data selected by this method are used as input. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Flowchart of the method for selecting near-source strong earthquake records considering the slip-thrust effect.

[0031] Figure 2 Schematic diagram of the permanent displacement hazard curve corresponding to the project site, the target permanent displacement, and the magnitude of the assumed rupture scenario in the embodiment given by the probabilistic fault displacement hazard analysis.

[0032] Figure 3 The figure shows the correlation heat map between 18 earthquake intensity indices applicable to permanent displacement type near-source strong motion records, as well as the correlation coefficient diagram of each period spectrum displacement.

[0033] Figure 4 Comparison diagram of the selected earthquake motion record dataset and the target conditional distribution.

[0034] Figure 5 The target conditional distribution corresponding to the earthquake intensity index, the acceptance domain of the KS test (confidence level is 0.1), and the sample cumulative distribution comparison chart of the selected earthquake dataset. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] The present invention is described in detail below with reference to specific embodiments.

[0037] Example 1

[0038] like Figure 1 A method for selecting strong earthquake records near the earthquake source considering the slip effect is shown, including the following steps:

[0039] Step S1: Based on the basic information of the target fault and site, the extended probabilistic fault displacement hazard analysis is used to give a permanent displacement target value corresponding to the specified exceedance probability level, and combined with the permanent displacement mean value given by the seismic motion prediction model of the slip effect under the set earthquake rupture scenario, the standard deviation coefficient corresponding to the permanent displacement as the conditional parameter is determined; the basic information of the target fault and site includes the fault type, fault length, fault dip, the minimum magnitude affecting the project site, the maximum potential earthquake magnitude, the average annual earthquake occurrence rate, the b value in the Gutenberg-Richter relationship and the average shear wave velocity within 30m below the site surface; the set earthquake rupture scenario includes the set magnitude, the upper boundary depth of the fault rupture surface and the shortest distance from the site to the fault rupture surface; the standard deviation coefficient ε corresponding to the conditional parameter PD lnPD is defined as follows:

[0040]

[0041] Among them, under the earthquake rupture scenario Rup, the mean value of PD μ lnPD and standard deviation σ lnPD It can be given by the corresponding earthquake motion prediction model, while lnPD is the target value under the specified exceedance probability level given by the probabilistic fault displacement hazard analysis method extended in S1;

[0042] Step S2: Under the assumed earthquake rupture scenario, a seismic motion prediction model applicable to permanent displacement-type near-source strong earthquake records is selected to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of each location in the target seismic intensity indicator set; the seismic intensity indicators involved in the target seismic intensity indicator set can be flexibly combined and matched according to specific needs, including but not limited to: peak acceleration, peak velocity, peak displacement, permanent displacement, Arias intensity, cumulative absolute velocity, significant duration, spectral intensity, and spectral displacement;

[0043] Step S3: Based on the permanent displacement type near-source strong earthquake database, calculate the standard deviation correlation coefficient matrix between any earthquake parameter indicators applicable to permanent displacement type near-source strong earthquakes; any two earthquake intensity indicators IM i and IM j The correlation coefficient can be calculated by the standard deviation coefficient ε lnIMi and ε lnIMj The correlation coefficient between them is replaced by the specific definition as follows:

[0044]

[0045] The actual rupture scenario information corresponding to each strong motion record in the permanent displacement type near-source strong earthquake database is recorded as rup iThe selected earthquake prediction model gives the earthquake intensity index IM of each place. i The predicted mean is denoted as μ lnIMi|(rupi) and standard deviation σ lnIMi , combined with the actual value lnIM i (Geometric mean of the two horizontal components) Determine the standard deviation coefficient ε lnIMi ; n is the total number of strong motion records contained in the permanent displacement type near-source strong motion database, and They are the standard deviation coefficient ε lnIMi and ε lnIMj The corresponding sample mean;

[0046] Step S4: Based on the basic concept of the generalized seismic intensity index, the conditional mean and conditional standard deviation of the seismic intensity index of each region are calculated, and the multivariate conditional distribution of the generalized seismic intensity index is further constructed. The conditional mean and conditional standard deviation of the seismic intensity index of each region are given by the following definitions:

[0047]

[0048] Among them, the earthquake motion prediction model corresponding to the vibration intensity index of each place can give the mean μ under the set earthquake rupture scenario Rup lnIMi and standard deviation σ lnIMi ρ lnIMi,lnPD|Rup for lnIM i The correlation coefficient with lnPD, both of which can be obtained in step S3;

[0049] Step S5: Multiple groups of target simulation vectors are approximately randomly sampled from the target multivariate conditional distribution using the Latin hypercube sampling method. The permanent displacement type near-source strong earthquake database is searched one by one for the candidate ground motion dataset with the smallest error with each group of target simulation vectors. Because the dimensions of the parameters in the target ground motion intensity index set IM are different, the errors corresponding to the parameters are normalized by standard deviation and then the error function is constructed in the form of weighted sum of squares. The specific form is as follows:

[0050]

[0051] Among them, N IMi is the number of parameters in the target ground motion intensity index set IM; lnIM i nism and lnIM i m,scaled Respectively represent the target simulation vector of the nsim item {IM i} and the mth record in the earthquake motion database after amplitude modulation {IM i};w iIn order to assign error weight coefficients to the earthquake intensity indicators of various places, differential adjustments can be made according to the different levels of importance of the earthquake intensity indicators;

[0052] Step S6: The deviation between each candidate sample distribution and the target conditional distribution is measured by using the R value obtained by weighted summation of the statistic D value in the KS test, and the candidate data set with the smallest R value is finally selected as the final result; the R value obtained by weighted summation of the statistic D value in the KS test is defined as:

[0053]

[0054] Among them, F IMi|PD (im i |pd) is the target GCIM distribution; ECDF(im i ) represents the empirical cumulative distribution function corresponding to the i-th earthquake motion intensity index in the candidate earthquake motion dataset; w i The weight coefficient can be kept consistent with the error function or reassigned; finally, the candidate seismic data set with the smallest R value is regarded as the optimal result that meets the target condition distribution and is output.

[0055] Example 2

[0056] Based on Example 1, this example utilizes a system for selecting strong earthquake records near the earthquake source that considers the impact of landslide, including a calculation module, and executes a method for selecting strong earthquake records near the earthquake source that considers the impact of landslide. The method is applied to engineering sites near the earthquake source. The specific steps include:

[0057] (1) Determine the target permanent displacement value and the corresponding standard deviation coefficient

[0058] Assuming that the basic information of the target fault and the project site is listed in Table 1, the permanent displacement (PD = 30 cm) at the 50-year exceedance probability of 2% determined by the probabilistic fault displacement hazard analysis is used as the conditional parameter, and the moment magnitude (M) corresponding to the assumed earthquake rupture scenario is given. W =7), such as Figure 2 shown.

[0059] In addition to permanent displacement PD, the target ground motion intensity index set also includes peak acceleration PGA, peak velocity PGV, peak displacement PGD, spectrum displacement Sd (period 0.05s-10s), 5-95% significant duration D S5-95 , spectral intensity SI, cumulative absolute velocity CAV.

[0060] In this embodiment, the number of seismic motion records selected is set to 30, and the number of selected records can also be adjusted according to actual needs.

[0061] Table 1

[0062]

[0063]

[0064] (2) Determine the unconditional mean and standard deviation corresponding to the earthquake intensity indicators of each location in the target earthquake intensity indicator set.

[0065] Under the assumed earthquake rupture scenario, the unconditional means and unconditional standard deviations of the earthquake intensity indicators in various locations determined by the seismic motion prediction model applicable to permanent displacement type near-source strong earthquake records are listed in Table 2.

[0066] Table 2

[0067]

[0068] (3) Calculate the standard deviation correlation coefficient matrix between the vibration intensity indicators of various locations for permanent displacement type near-source strong earthquake records.

[0069] The earthquake motion database, consisting of 597 sets of permanent displacement type near-source strong earthquake records of 65 earthquake events, is combined with the earthquake motion prediction model to calculate the standard deviation correlation coefficient matrix between any earthquake motion intensity indicators applicable to permanent displacement type near-source strong earthquake motion, as shown in the following example: Figure 3 As shown in Figure 2, PD and PGD have the highest correlation, with a correlation coefficient of ρ reaching 0.69. In contrast, the correlations between other earthquake intensity indicators and PD are relatively low.

[0070] (4) Calculate the conditional mean and conditional standard deviation of the vibration intensity index for each location

[0071] Combining the above three steps, when the target permanent displacement PD is 30cm and the standard deviation coefficient ε lnPD =0.878, the calculation formulas for the conditional mean and conditional standard deviation corresponding to any earthquake intensity index are as follows:

[0072]

[0073] Among them, the earthquake motion prediction model corresponding to the vibration intensity index of each place can give the mean μ under the set earthquake rupture scenario Rup lnIMi and standard deviation σ lnIMi ρ lnIMi,lnPD|Rup for lnIM i The final calculation results are listed in Table 3.

[0074] Table 3

[0075]

[0076]

[0077] (5) Search one by one for the alternative seismic data sets that have the smallest error with each set of target simulation vectors.

[0078] In this embodiment, Latin hypercube sampling is used to extract 50 sets of target simulation vectors, and the error weight coefficient assigned to the target earthquake intensity index set IM is w i ={0.1,0.1,0.1,0,0.1,0.1,0.1,0.4}. The weight coefficient 0.4 for the spectral displacement is evenly distributed to the 10 selected period control points. The best selection results from multiple sets of candidate ground motion data sets are as follows: Figure 4 shown.

[0079] (6) Using KS test to select the best seismic data set from multiple sets of candidate seismic data sets

[0080] The deviation between the candidate sample distribution and the target conditional distribution is measured by using the R value obtained by weighted summation of the statistic D value in the KS test. The confidence interval is set to 0.1. The final selected optimal seismic data set corresponds to the sample cumulative distribution and the acceptance domain of the KS test as follows: Figure 5 The cumulative distribution of vibration intensity indicators in various places has achieved a good match with the corresponding conditional distribution.

[0081] The seismic motion selection method based on the generalized seismic motion intensity and the consideration of the slip effect amplitude of the present invention generally still follows the seismic motion selection framework based on the generalized seismic motion intensity index, with the following differences: 1. It is considered that the danger faced by near-fault projects, especially cross-fault projects, comes from the coupling effect of strong seismic motion and active fault dislocation, and the large structural deformation caused by the active fault dislocation is undoubtedly a greater threat to the engineering structure. The conditional strength index is replaced by the permanent displacement instead of the spectral acceleration corresponding to the first-order natural vibration period of the target structure; 2. Although spectral acceleration and spectral displacement are both commonly used in seismic motion selection for structural seismic response analysis and seismic performance evaluation, it is considered that the slip effect amplitude has a greater impact on the long-period amplitude of the displacement spectrum. In the target seismic intensity index set, the spectral displacement that is more sensitive to the long-period component is used instead of the commonly used spectral acceleration to characterize the spectral characteristics of the seismic motion, providing services for displacement-based seismic design.

[0082] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for selecting strong earthquake records near the earthquake source considering the slip effect, characterized in that: The following steps are involved: S1. Based on the basic information of the target fault and site, the extended probabilistic fault displacement hazard analysis is used to determine the permanent displacement target value corresponding to the specified exceedance probability level. Combined with the permanent displacement mean value given by the ground motion prediction model of the slip effect under the set earthquake rupture scenario, the standard deviation coefficient corresponding to the permanent displacement is determined as a conditional parameter. S2. Under the assumed earthquake rupture scenario, select a seismic motion prediction model applicable to permanent displacement type near-source strong earthquake records to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of each location in the target seismic intensity indicator set; S3. Based on the permanent displacement type near-source strong earthquake database, calculate the standard deviation correlation coefficient matrix between any earthquake motion parameter indicators applicable to permanent displacement type near-source strong earthquake motions; S4. Based on the basic concept of the generalized seismic intensity index, the conditional mean and conditional standard deviation of the seismic intensity index in each region are calculated, and the multivariate conditional distribution of the generalized seismic intensity index is further constructed. S5. Approximately randomly extract multiple sets of target simulation vectors from the target multivariate conditional distribution using the Latin hypercube sampling method, and search the permanent displacement type near-source strong earthquake database one by one for the candidate ground motion dataset with the smallest error with each set of target simulation vectors; S6. Use the R value obtained by weighted summation of the statistic D value in the KS test to measure the deviation between the distribution of each alternative sample and the target conditional distribution, and finally take the alternative data set with the smallest R value as the final selection result.

2. The method for selecting strong earthquake records near the earthquake source considering the slip effect according to claim 1 is characterized in that: The basic information of the target fault and site in S1 includes the fault type, fault length, fault dip, minimum magnitude that affects the project site, maximum potential earthquake magnitude, average annual earthquake occurrence rate, b value in the Gutenberg-Richter relationship, and average shear wave velocity within 30m below the surface of the site; the set earthquake rupture scenario includes the set magnitude, the upper limit depth of the fault rupture surface, and the shortest distance from the site to the fault rupture surface; the standard deviation coefficient ε corresponding to the conditional parameter PD lnPD is defined as follows: Among them, under the earthquake rupture scenario Rup, the mean value of PD μ lnPD and standard deviation σ lnPD It can be given by the corresponding earthquake motion prediction model, while lnPD is the target value under the specified exceedance probability level given by the probabilistic fault displacement hazard analysis method expanded in S1.

3. The method for selecting strong earthquake records near the earthquake source considering the slip effect according to claim 1 is characterized in that: Any two earthquake intensity indices IM in S3 i and IM j The correlation coefficient can be calculated by the standard deviation coefficient ε lnIMi and ε lnIMj The correlation coefficient between them is replaced by the specific definition as follows: The actual rupture scenario information corresponding to each strong motion record in the permanent displacement type near-source strong earthquake database is recorded as rup i The selected earthquake prediction model gives the earthquake intensity index IM of each place. i The predicted mean is denoted as μ lnIMi|(rupi) and standard deviation σ lnIMi , combined with the actual value lnIM i Determine the standard deviation coefficient ε lnIMi ; Actual value lnIM i is the geometric mean of the two horizontal components, n is the total number of strong motion records contained in the permanent displacement type near-source strong motion database, and They are the standard deviation coefficient ε lnIMi and ε lnIMj The corresponding sample mean.

4. The method for selecting strong earthquake records near the earthquake source considering the slip effect according to claim 1 is characterized in that: The conditional mean and conditional standard deviation of the vibration intensity index of each region in S4 are given by the following definitions: Among them, the earthquake motion prediction model corresponding to the vibration intensity index of each place can give the mean μ under the set earthquake rupture scenario Rup lnIMi and standard deviation σ lnIMi ρ lnIMi,lnPD|Rup for lnIM i Correlation coefficient with lnPD.

5. The method for selecting strong earthquake records near the earthquake source considering the slip effect according to claim 1 is characterized in that: In S5, since the dimensions of the parameters in the target ground motion intensity index set IM are different, the errors corresponding to the parameters are normalized by standard deviation and then the error function is constructed in the form of weighted sum of squares. The specific form is as follows: Among them, N IMi is the number of parameters in the target ground motion intensity index set IM; lnIM i nism and lnIM i m,scaled Respectively represent the target simulation vector of the nsim item {IM i } and the mth record in the earthquake motion database after amplitude modulation {IM i };w i In order to assign error weight coefficients to the seismic intensity indicators of various places, differentiated adjustments can be made according to the different levels of importance of the seismic intensity indicators.

6. The method for selecting strong earthquake records near the earthquake source considering the slip effect according to claim 1, characterized in that: The R value obtained by weighted summation of the statistic D value in the KS test in S6 is defined as: Among them, F IMi|PD (im i |pd) is the target GCIM distribution; ECDF(im i ) represents the empirical cumulative distribution function corresponding to the i-th earthquake motion intensity index in the candidate earthquake motion dataset; w i The weight coefficient can be kept consistent with the error function or reassigned; finally, the candidate seismic data set with the smallest R value is regarded as the optimal result that meets the target condition distribution and is output.

7. A system for selecting strong earthquake records from near-seismic sources taking into account the slip effect, constructed based on the method for selecting strong earthquake records from near-seismic sources as described in any one of claims 1 to 6, characterized in that: It includes a calculation module for executing the above-mentioned method for selecting strong earthquake records of near-source earthquakes considering the slip effect.

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