Near-source strong earthquake record selection method and system considering gliding effect

Abstract

The invention relates to the technical field of seismic records, in particular to a near-source strong seismic record selection method and system considering a sliding impact effect, and the method comprises the following steps: introducing permanent displacement as a condition parameter into seismic oscillation selection based on a generalized condition intensity index; a target permanent displacement amount is given by an extended probability fault displacement danger analysis method; in the target seismic intensity index set, spectrum displacement is used for replacing common spectrum acceleration to represent spectrum components of seismic oscillation; an empirical correlation coefficient suitable for permanent displacement type near-source strong earthquake records is further calculated and given, corresponding condition intensity index target distribution is constructed, and finally optimization is carried out from a near-source strong earthquake database to obtain a permanent displacement type near-source strong earthquake data set which is most consistent with the target condition distribution; according to the method, a reasonable input vibration selection method can be provided for seismic response analysis of a near-seismic-source engineering structure under the strong vibration-fault movement coupling effect.

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 strong earthquake records near the earthquake source taking into account the slip effect. Background Art

[0002] Many earthquake disasters have shown that the stick-slip dislocation of active faults may extend to the surface, causing significant permanent structural deformation on the surface, and then causing serious damage to engineering structures. The surface fault dislocation is manifested as a slip effect in the near-seismic strong earthquake record, which will cause a unidirectional velocity pulse in the velocity time history parallel to the fault slip direction. At the same time, the displacement time history curve shows the characteristics of an approximate step function, and the end of the curve contains a permanent displacement (slip effect amplitude). Therefore, permanent displacement near-seismic strong earthquake records are of great value in studying the fault rupture process of strong earthquakes and co-seismic surface deformation.

[0003] At present, the research on using permanent displacement seismic motion (i.e., permanent displacement caused by considering the sliding effect) as input for seismic response analysis mainly 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 sliding effect amplitude (permanent displacement) corresponding to the seismic motion record. A series of permanent displacement seismic motions processed by this baseline correction method are used as input loads to explore the impact of different fault slip levels on the seismic response of cross-fault bridges. 2. The seismic motion simulation method can be subdivided into: (I) permanent displacement seismic motion simulation with superimposed sliding effect velocity pulse function, (II) broadband hybrid seismic motion simulation method directly reflects the sliding effect in the simulation results. However, no seismic motion selection method considering the sliding effect amplitude has been 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 there are certain limitations in seismic motion selection that only relies on matching target spectra. 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 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 acceleration records, it also filters out low-frequency information corresponding to permanent displacement in the original records. Summary of the invention

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

[0006] The embodiment of the present application provides a method for selecting strong earthquake records of near-seismic sources considering the slip-thrust effect, comprising the following steps:

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

[0008] S2. Under the set earthquake rupture scenario, select the seismic motion prediction model applicable to the permanent displacement type near-seismic source strong earthquake record to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of various places 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 motion;

[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 of each region are calculated, and the multivariate conditional distribution of the generalized seismic intensity index is further constructed;

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

[0012] S6. The deviation between the distribution of each candidate sample 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 finally the candidate data set with the smallest R value is taken 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, the minimum magnitude that affects 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 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 indicators IM in S3 i and IM j The correlation coefficient between lnIMi and ε lnIMj The correlation coefficient between them is used as a substitute, and the specific definition is 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 seismic intensity index IM of each region is given by the selected seismic prediction model i The predicted mean is denoted by 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 region in S4 are given by the following definitions:

[0020]

[0021] Among them, the earthquake motion prediction model corresponding to the earthquake intensity index of each place can give the mean value 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 error corresponding to each parameter is normalized by the standard deviation and then the error function is constructed in the form of a 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 {IM i} and the mth record in the earthquake 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 degrees of importance of the seismic intensity indicators.

[0025] Further, 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 ) is represented by the empirical cumulative distribution function corresponding to the i-th earthquake intensity index in the candidate earthquake data set; w i The weight coefficient can be kept consistent with that in 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 conditional distribution and is output.

[0028] A system for selecting strong earthquake records of near-seismic sources taking into account the landslide effect and constructed based on the above-mentioned method for selecting strong earthquake records of near-seismic sources, comprises a calculation module for executing the above-mentioned method for selecting strong earthquake records of near-seismic sources taking into account the landslide effect.

[0029] The beneficial effects of the present invention are as follows: the present invention uses the permanent displacement that characterizes the degree of fault dislocation as a conditional parameter to construct a target conditional distribution, so as to achieve a good match between the sample distribution of the selected permanent displacement type near-seismic source strong earthquake record data set and the target conditional distribution. In addition, the present invention fully considers the influence of the permanent displacement (slip effect amplitude) on the low-frequency component of the earthquake motion, and calculates and gives a correlation coefficient matrix that is more suitable for the permanent displacement type near-seismic source strong earthquake record, thereby ensuring the reliability of the seismic response of the near-seismic source engineering structure under the coupling of strong vibration and fault dislocation when the earthquake motion data selected by this method is 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 It is a schematic diagram of the permanent displacement hazard curve corresponding to the project site, the target permanent displacement and the magnitude of the set rupture scenario in the embodiment given by the probabilistic fault displacement hazard analysis.

[0032] Figure 3 It is a heat map of the correlation between 18 types of earthquake intensity indicators applicable to permanent displacement type near-source strong motion records, as well as a correlation coefficient diagram of each period spectral displacement.

[0033] Figure 4 Comparison diagram of the selected earthquake record data set and the target condition distribution.

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

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 sliding effect is shown, comprising the following steps:

[0039] Step S1: Based on the basic information of the target fault and site, the permanent displacement target value corresponding to the specified exceedance probability level is given by the extended probabilistic fault displacement hazard analysis, and combined with the permanent displacement mean value given by the seismic motion prediction model of the sliding 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 surface of the site; the set earthquake rupture scenario includes the set magnitude, the upper boundary burial 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 set earthquake rupture scenario, a seismic motion prediction model suitable for permanent displacement type near-seismic source strong earthquake records is selected to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of various places in the target seismic motion intensity indicator set; the seismic motion intensity indicators involved in the target seismic motion 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 between lnIMi and ε lnIMj The correlation coefficient between them is used as a substitute, and the specific definition is 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 seismic intensity index IM of each region is given by the selected seismic prediction model i The predicted mean is denoted by 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 definition:

[0047]

[0048] Among them, the earthquake motion prediction model corresponding to the earthquake intensity index of each place can give the mean value 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 extracted from the target multivariate conditional distribution by the Latin hypercube sampling method, and the candidate seismic data sets with the smallest error with each group of target simulation vectors are searched one by one in the permanent displacement type near-source strong earthquake database; because the dimensions of the parameters in the target seismic 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 square sum, and 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 {IM i} and the mth record in the earthquake database after amplitude modulation {IM i}; w iIn order to assign error weight coefficients to the seismic intensity indicators of various places, differential adjustments can be made according to the different degrees of importance of the seismic intensity indicators;

[0052] Step S6: 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, and the candidate data set with the smallest R value is finally selected as the final selection 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 ) is represented by the empirical cumulative distribution function corresponding to the i-th earthquake intensity index in the candidate earthquake data set; w i The weight coefficient can be kept consistent with that in 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 conditional distribution and is output.

[0055] Example 2

[0056] On the basis of Example 1, this embodiment uses a system for selecting strong earthquake records near the earthquake source considering the sliding effect, including a calculation module, executing a method for selecting strong earthquake records near the earthquake source considering the sliding effect, and applying the method to the engineering site near the earthquake source, and 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, spectral 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 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 vibration intensity indicators of each location in the target ground vibration intensity indicator set.

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

[0069] The earthquake motion database consisting of 597 sets of permanent displacement type near-seismic 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-seismic source strong earthquake motion, such as Figure 3 As shown in Figure 2, the correlation between PD and PGD is the highest, 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 in each region

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

[0072]

[0073] Among them, the earthquake motion prediction model corresponding to the earthquake intensity index of each place can give the mean value 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 ground motion 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 periodic control points. The best selection results from multiple sets of candidate ground motion data sets are as follows: Figure 4 shown.

[0079] (6) Using the KS test to select the best earthquake motion dataset from multiple candidate earthquake motion datasets

[0080] The R value obtained by weighted summation of the statistic D value in the KS test is used to measure the deviation between the candidate sample distribution and the target conditional distribution. The confidence interval is set to 0.1. The final selected best earthquake motion 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] In the seismic motion selection method based on the generalized seismic motion intensity and considering the amplitude of the sliding effect, the present invention still follows the seismic motion selection framework based on the generalized seismic motion intensity index as a whole, 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 commonly used in seismic motion selection for structural seismic response analysis and seismic performance evaluation, it is considered that the amplitude of the sliding effect has a higher degree of influence 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 invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for selecting strong earthquake records of near-seismic sources considering the sliding effect, characterized in that: The following steps are involved: S1. Based on the basic information of the target fault and site, the permanent displacement target value corresponding to the specified exceedance probability level is given by the extended probabilistic fault displacement hazard analysis, and the permanent displacement mean value given by the ground motion prediction model of the sliding effect under the set earthquake rupture scenario is combined to determine the standard deviation coefficient corresponding to the permanent displacement as a conditional parameter; S2. Under the set earthquake rupture scenario, select the seismic motion prediction model applicable to the permanent displacement type near-seismic source strong earthquake record to determine the unconditional mean and unconditional standard deviation corresponding to the seismic intensity indicators of various places 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 motion; 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; S5. Approximately randomly extract multiple groups of target simulation vectors from the target multivariate conditional distribution by using the Latin hypercube sampling method, and search one by one in the permanent displacement type near-source strong earthquake database for the candidate ground motion data set with the smallest error with each group of target simulation vectors; S6. The deviation between the distribution of each candidate sample 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 finally the candidate data set with the smallest R value is taken as the final selection result.

2. The method for selecting strong earthquake records of near-seismic sources considering the sliding 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, the minimum magnitude that affects 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 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: 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.

3. The method for selecting strong earthquake records of near-seismic sources considering the sliding effect according to claim 1 is characterized in that: Any two earthquake intensity indicators IM in S3 i and IM j The correlation coefficient between lnIMi and ε lnIMj The correlation coefficient between them is used as a substitute, and the specific definition is 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 seismic intensity index IM of each region is given by the selected seismic prediction model i The predicted mean is denoted by 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.

4. The method for selecting strong earthquake records of near-seismic sources considering the sliding 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 earthquake intensity index of each place can give the mean value 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 of near-seismic sources considering the sliding 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 error corresponding to each parameter is normalized by standard deviation and then the error function is constructed in the form of weighted square sum. 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 {IM i } and the mth record in the earthquake 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 degrees of importance of the seismic intensity indicators.

6. The method for selecting strong earthquake records of near-seismic sources considering the sliding effect according to claim 1 is 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 ) is represented by the empirical cumulative distribution function corresponding to the i-th earthquake intensity index in the candidate earthquake data set; w i The weight coefficient can be kept consistent with that in 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 conditional distribution and is output.

7. A system for selecting strong earthquake records from near-seismic sources taking into account the sliding effect and 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-seismic sources considering the sliding effect.

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