A method, device, medium and equipment for identifying repeated earthquakes

Abstract

The present invention relates to the field of data processing, and in particular to a method, device, medium and equipment for identifying repeated earthquakes, comprising: obtaining a first waveform similarity set to group the candidate earthquake waveforms according to a candidate earthquake waveform set and a standard waveform template set, obtaining W1 first clustering sets, clustering the waveform analysis data set corresponding to the candidate earthquake waveform set according to a preset weight set, obtaining W2 second clustering sets, obtaining a third clustering set corresponding to each first clustering set according to the W1 first clustering sets and the W2 second clustering sets, and determining the repeated earthquake category corresponding to the third clustering set, comprehensively considering the similarity between the candidate earthquake waveform and the standard waveform template in the overall waveform, and the local waveform characteristics of the candidate earthquake waveform, improving the accuracy of the third clustering set, and thereby improving the recognition accuracy of repeated earthquakes.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a repeated earthquake identification method, device, medium and equipment. Background Art

[0002] When conducting fault friction experiments in the laboratory to simulate the seismogenic process of earthquakes, an acoustic emission monitoring system is required to accurately identify and effectively capture the microfractures and stick-slip phenomena generated during the fault friction process. In earthquake research, when velocity-weakening materials and velocity-enhancing materials coexist, there are often significant differences in mechanical properties in different areas of the fault zone, and repeated earthquakes often occur. These repeated earthquakes refer to earthquakes that occur at the same fault location with similar magnitudes and recurrence intervals, and highly similar waveforms and focal mechanisms. Laboratory earthquake physics simulations of this repetitive earthquake phenomenon require accurate acoustic emission signal recognition technology and repeated earthquake identification and classification technology, but the high-frequency signal of this microfracture has the characteristics of low energy, fast attenuation and dissipation, and a high level of mixed noise. In actual operation, it is difficult to accurately capture and identify repeated earthquakes in laboratory earthquake research.

[0003] In order to accurately identify repeated earthquakes, at present, based on the collected seismic waveforms, the band clustering method, clustering parameters, starting layer, layer offset and the size of the sliding time window are usually determined, and the seismic attribute data of the layer to be studied are clustered using the sliding time window to determine the category to which the sample points of each layer in the layer to be studied belong, and then the clustering results of adjacent layers are unified according to the spatial similarity, and the identification of repeated earthquakes is completed based on the clustering results. However, when clustering seismic waveforms, the above method focuses on the number of sample points between adjacent peaks in the seismic waveform, and performs local waveform analysis on the seismic waveform based on the sliding time window, which cannot achieve the overall waveform analysis of the seismic waveform, resulting in poor accuracy of the clustering results of the seismic waveform, and thus low accuracy in the identification of repeated earthquakes.

[0004] Therefore, how to improve the accuracy of identifying repeated earthquakes has become an urgent problem to be solved. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is a repeated earthquake identification method, which comprises the following steps:

[0006] S100, obtaining a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P} and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q}, where C urefers to the candidate earthquake waveform corresponding to the u-th target time point, D v It refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates.

[0007] S200, according to C and D, obtain the first waveform similarity set E corresponding to C = {E1, E2, ..., E u ,……,E P}, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ}, E uv C u and D v The waveform similarity between them.

[0008] S300, grouping P candidate seismic waveforms according to E, obtaining W1 first clustering sets, wherein each first clustering set includes several candidate seismic waveforms, and the repeated earthquake category corresponding to each first clustering set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set.

[0009] S400, according to C, obtain the waveform analysis data set H corresponding to C = {H1, H2, ..., H u , ..., H P}, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u}, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u = {G u1 , G u2 , ..., G ur , ..., G uR}, C uThe rth inflection point data G ur = {G ur 1 , G ur 2}, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point.

[0010] S500, obtain a preset weight set Z = {Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second cluster sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data, and each second cluster set includes several candidate earthquake waveforms.

[0011] S600, based on W1 first cluster sets and W2 second cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0012] The present invention also provides a repeated earthquake identification device, the repeated earthquake identification device comprising:

[0013] The data acquisition module is used to obtain the candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P} and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q}, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D v It refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates.

[0014] The similarity calculation module is used to obtain the first waveform similarity set E corresponding to C according to C and D = {E1, E2, ..., E u ,……,E P}, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,EuQ}, E uv C u and D v The waveform similarity between them.

[0015] The waveform grouping module is used to group P candidate seismic waveforms according to E to obtain W1 first cluster sets, wherein each first cluster set includes several candidate seismic waveforms, and the repeated earthquake category corresponding to each first cluster set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set.

[0016] The waveform analysis module is used to obtain the waveform analysis data set H corresponding to C according to C = {H1, H2, ..., H u , ..., H P}, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u}, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u = {G u1 , G u2 , ..., G ur , ..., G uR}, C u The rth inflection point data G ur = {G ur 1 , G ur 2}, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point.

[0017] The waveform clustering module is used to obtain a preset weight set Z = {Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second clustering sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data. Each second clustering set includes several candidate earthquake waveforms.

[0018] The repeated earthquake identification module is used to obtain the third cluster set corresponding to each first cluster set based on W1 first cluster sets and W2 second cluster sets, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0019] The present invention also provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned repeated earthquake identification method.

[0020] The present invention also provides an electronic device, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0021] The present invention has at least the following beneficial effects: by obtaining a candidate seismic waveform set C, a preset standard waveform template set D and a preset weight set Z, according to C and D, a first waveform similarity set E corresponding to C is obtained, P candidate seismic waveforms are grouped according to E, and W1 first clustering sets are obtained, the similarity between the candidate seismic waveform and the standard waveform template in the overall waveform is considered, and the candidate seismic waveform and the standard waveform template are matched, thereby improving the accuracy of the first clustering set; according to C, a waveform analysis data set H corresponding to C is obtained, H is clustered according to Z, and W2 second clustering sets are obtained, and the similarity between the candidate seismic waveform and the standard waveform template in the overall waveform is considered. The candidate earthquake waveforms are clustered based on the local waveform characteristics, thereby improving the accuracy of the second cluster set; based on W1 first cluster sets and W2 second cluster sets, the third cluster set corresponding to each first cluster set is obtained, and the repeated earthquake category corresponding to each first cluster set is determined as the repeated earthquake category corresponding to the corresponding third cluster set, and the matching between the first cluster set and the second cluster set is comprehensively considered, the similarity between the candidate earthquake waveform and the standard waveform template in the overall waveform, and the local waveform characteristics of the candidate earthquake waveform are comprehensively considered, thereby improving the accuracy of the third cluster set, thereby improving the recognition accuracy of repeated earthquakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a repeated earthquake identification method provided in Embodiment 1 of the present invention;

[0024] Figure 2 A schematic structural diagram of a repeated earthquake identification device provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0025] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] This embodiment 1 provides a repeated earthquake identification method, which includes the following steps: Figure 1 As shown:

[0029] S100, obtaining a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P} and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q}, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D vIt refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates.

[0030] Among them, repetitive earthquakes refer to earthquakes that occur at the same fault location with similar magnitudes and recurrence intervals, and highly similar waveforms and focal mechanisms. Based on the differences in magnitude, recurrence interval, waveform, and focal mechanism, multiple repetitive earthquake categories can be set, and the standard waveform template corresponding to each repetitive earthquake category can be obtained to identify and distinguish different earthquake waveforms.

[0031] Specifically, the differences between different repetitive earthquake categories can be reflected in the corresponding seismic waveforms, and the repetitive earthquake categories can be set by the implementer according to the actual situation. The standard waveform template corresponding to each repetitive earthquake category can be determined by the average value of the seismic waveforms corresponding to each repetitive earthquake category, or by the seismic waveform with the largest number corresponding to each repetitive earthquake category.

[0032] The candidate earthquake waveforms can be earthquake waveforms collected by an acoustic emission monitoring system in a laboratory environment. Since there are consistency and differences in magnitude, recurrence interval, waveform and source mechanism among the candidate earthquake waveforms, the candidate earthquake waveforms are analyzed to identify a group of repeated earthquakes corresponding to each repeated earthquake category, which can serve as the data basis for subsequent experiments.

[0033] The target time point can be in milliseconds.

[0034] As described above, obtaining the candidate earthquake waveform set C and the preset standard waveform template set D provides a data basis for identifying repeated earthquakes.

[0035] In a specific implementation, S100 further includes the following steps:

[0036] S110, obtaining a seismic wave frequency domain data set A = {A1, A2, ..., A i , ..., A N}, where the seismic wave frequency data list A corresponding to the i-th preset time point i ={A i1 , A i2 , ..., A ij , ..., A iM(i)}, A ij It refers to the jth seismic wave frequency corresponding to the i-th preset time point, i=1, 2,..., N, N refers to the total number of preset time points, j=1, 2,..., M(i), M(i) refers to the total number of seismic wave frequencies corresponding to the i-th preset time point.

[0037] S120, according to A i, get A i Corresponding frequency interruption duration list B i = {B i1 , B i2 , ..., B ik , ..., B iL(i)}, where B ik A i The corresponding k-th frequency discontinuity duration, k = 1, 2, ..., L(i), L(i) refers to A i The total number of corresponding frequency interruption durations.

[0038] S130, according to A i , the preset first frequency threshold range, B i and the preset frequency discontinuity duration threshold, and select P target time points from N preset time points.

[0039] S140, extract the seismic waveforms corresponding to the P target time points respectively, and obtain a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P}.

[0040] The preset first frequency threshold range [a, b] refers to the range of frequencies that can be collected and acquired when a micro-earthquake occurs, a refers to the preset minimum frequency, and b refers to the preset maximum frequency. The specific value of the preset first frequency threshold range can be set by the implementer according to actual conditions.

[0041] For example, since microseismic signals have the characteristics of shallow epicenter, short propagation distance and shorter wavelength than seismic waves, the local medium inhomogeneity has a greater impact on microseismic signals than on seismic waves, and the propagation distance of microseismic signals is much smaller than that of seismic waves. They can be roughly regarded as propagating in rock masses with the same layered phase structure. Correspondingly, the frequency range of microseismic signals is between a few hertz and several hundred hertz. Therefore, when identifying microseisms, a preset first frequency threshold range can be set according to the frequency range of the microseismic signals.

[0042] Due to the complex situation when an earthquake occurs, multiple different frequency data will be collected at the same preset time point. If the range of the seismic wave frequency data corresponding to the preset time point is close to the first frequency threshold range, and the interval between the seismic wave frequency data is less than the preset frequency interval duration threshold, then it can be considered that the seismic waveform corresponding to the preset time point meets the screening conditions and can be selected as a candidate seismic waveform for subsequent repeated earthquake identification.

[0043] Therefore, the seismic wave frequency data list corresponding to the i-th preset time point is judged according to the preset first frequency threshold range. If A iThe minimum frequency A in i1 Close to a, and A i The maximum frequency A iM(i) Close to b, then judge A i The filter condition meets the preset first frequency threshold range [a, b]. Specifically, if |A i1 -a| / a<σ1, and |A iM(i) -b| / b<σ2, then judge A i The screening condition corresponding to the preset first frequency threshold range is met. The specific values ​​of the preset first threshold σ1 and the preset second threshold σ2 can be set by the implementer according to actual conditions.

[0044] Further, according to the preset frequency interruption duration threshold, i The corresponding frequency interruption duration list is used for judgment. If B i The maximum value of is less than the preset frequency interruption duration threshold, then it is judged that A i The screening condition corresponding to the preset frequency interruption duration threshold is met, and the specific value of the preset frequency interruption duration threshold can be set by the implementer according to the actual situation.

[0045] When A i When the filtering conditions corresponding to the preset first frequency threshold range and the filtering conditions corresponding to the preset frequency interruption duration threshold are met at the same time, the i-th preset time point can be determined as the target time point, and the seismic waveform corresponding to the target time point can be extracted as a candidate seismic waveform for subsequent repeated earthquake identification.

[0046] In the above, according to the preset first frequency threshold range and the preset frequency interruption duration threshold, the seismic wave frequency data list corresponding to each preset time point is judged, the qualified target time points and the corresponding seismic waveforms are screened out, and a set of candidate seismic waveforms is obtained as the basis for identifying repeated earthquakes, thereby improving the screening accuracy of candidate seismic waveforms and further improving the identification accuracy of repeated earthquakes.

[0047] In a specific implementation, S110 further includes the following steps:

[0048] S111, acquiring time domain waveform data within a preset time period according to a preset acoustic emission monitoring system, wherein the preset time period includes N preset time points.

[0049] S112, convert the time domain waveform data into the frequency domain, and obtain the seismic wave frequency domain data set A = {A1, A2, ..., A i , ..., A N}.

[0050] Among them, in an earthquake environment, the acoustic emission monitoring system can use acoustic emission sensors to detect the transient elastic ultrasonic waves generated by materials under stress, and obtain the time domain waveform data within a preset time period by converting mechanical vibrations into electrical signals, which are then amplified, processed, recorded and analyzed. The time domain waveform data is further converted to the frequency domain to obtain the seismic wave frequency domain data set A, which is convenient for analyzing the seismic waveform data.

[0051] S200, according to C and D, obtain the first waveform similarity set E corresponding to C = {E1, E2, ..., E u ,……,E P}, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ}, E uv C u and D v The waveform similarity between them.

[0052] Among them, those skilled in the art know that any waveform similarity calculation method in the prior art falls within the protection scope of the present invention, and will not be described in detail here.

[0053] S300, grouping P candidate seismic waveforms according to E, obtaining W1 first clustering sets, wherein each first clustering set includes several candidate seismic waveforms, and the repeated earthquake category corresponding to each first clustering set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set.

[0054] In a specific implementation, S300 further includes the following steps:

[0055] S310, if max(E u ) The corresponding standard waveform template is D v , then C u Group to D v In the corresponding first cluster set, D v The corresponding repeated earthquake category is determined as D v The repeated earthquake categories corresponding to the corresponding first cluster set, where max() refers to the maximum value function.

[0056] S320, traverse E1, E2, ..., E u ,……,E P , and obtain W1 first clustering sets.

[0057] As described above, based on the first waveform similarity between the candidate earthquake waveform and the standard waveform template, the candidate earthquake waveform and the standard waveform template are matched, and then the candidate earthquake waveforms are grouped to obtain W1 first clustering sets, and each first clustering set corresponds to the same repeated earthquake category, providing a data basis for the identification of repeated earthquakes.

[0058] S400, according to C, obtain the waveform analysis data set H corresponding to C = {H1, H2, ..., H u , ..., H P}, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u}, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u = {G u1 , G u2 , ..., G ur , ..., G uR}, C u The rth inflection point data G ur = {G ur 1 , G ur 2}, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point.

[0059] Among them, the initial fluctuation time refers to the time when the waveform starts to fluctuate, and the fluctuation end time refers to the time when the waveform ends to fluctuate. There are multiple peaks in the waveform, which corresponds to multiple frequency peaks, and there are multiple inflection points in the waveform, each inflection point has a corresponding time and frequency.

[0060] In the candidate earthquake waveforms, the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data have a more important characterization role in the waveform characteristics such as the waveform shape and the frequency distribution of the waveform. Therefore, when judging the similarity of two candidate earthquake waveforms, the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data set of each candidate earthquake waveform are first extracted as the basis for matching and clustering the candidate earthquake waveforms, thereby improving the accuracy of the second clustering set.

[0061] In the above, the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data set of each candidate earthquake waveform are extracted as the basis for matching and clustering the candidate earthquake waveforms, thereby improving the accuracy of the second clustering set.

[0062] S500, obtain a preset weight set Z = {Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second cluster sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data, and each second cluster set includes several candidate earthquake waveforms.

[0063] Among them, Z1, Z2, Z3, and Z4 respectively correspond to the initial fluctuation time, the fluctuation end time, the maximum frequency peak value, and the inflection point data in characterizing the shape of the candidate earthquake waveform, the frequency distribution of the waveform and other waveform characteristics. The specific values ​​of Z1, Z2, Z3, and Z4 can be set by the implementer according to the actual situation.

[0064] In a specific implementation, S500 further includes the following steps:

[0065] S510, normalize H to obtain a normalized waveform analysis data set H 1 ={H 1 1, H 1 2, ..., H 1 u , ..., H 1 P}, where C u The corresponding normalized waveform analysis data H 1 u ={T 11 u , T 12 u , F 1 u , G 1 u}, T 11 u C uThe corresponding normalized initial fluctuation time, T 12 u C u The corresponding normalized fluctuation end time, F 1 u C u The corresponding normalized maximum frequency peak, G 1 u C u The corresponding normalized inflection point data set, G 1 u = {G 1 u1 , G 1 u2 , ..., G 1 ur , ..., G 1 uR}, C u The normalized r-th inflection point data G 1 ur = {G 1 ur 1 , G 1 ur 2}, G 1 ur 1 C u The normalized time corresponding to the rth inflection point, G 1 ur 2 C u The normalized frequency corresponding to the r-th inflection point.

[0066] S520, according to Z, H 1 u and H 1 α , obtain the second waveform similarity β between the u-th candidate earthquake waveform and the α-th candidate earthquake waveform uα , where H 1 α refers to the αth candidate earthquake waveform C α The corresponding normalized waveform analysis data, α∈{1, 2, ..., P} and α≠u, β uα Meet the following conditions:

[0067] β uα =Z1×dis(T 11 u , T 11 α )+Z2×dis(T 12u , T 12 α )+Z3×dis(F 1 u , F 1 α )+Z4×dis(G 1 u , G 1 α ),in,

[0068] dis(T 11 u , T 11 α ) refers to T 11 u and T 11 α The first distance between 12 u , T 12 α ) refers to T 12 u and T 12 α The second distance between 1 u , F 1 α ) refers to F 1 u and F 1 α The third distance between 1 u , G 1 α ) refers to G 1 u and G 1 α The fourth distance between.

[0069] S530, if β uα >β0, the uth candidate seismic waveform and the αth candidate seismic waveform are clustered into the same cluster set, where β0 refers to the preset similarity threshold.

[0070] S540, if β uα ≤β0, the u-th candidate earthquake waveform and the α-th candidate earthquake waveform are clustered into different cluster sets.

[0071] S550, traverse H 1 1, H 1 2, ..., H 1 u , ..., H 1P , and obtain W2 second clustering sets.

[0072] In order to ensure the scale consistency between data of different dimensions, the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data are normalized respectively to obtain the normalized waveform analysis data set H 1 , replaces the waveform analysis data set H to calculate the second waveform similarity, and combines the preset similarity threshold β0 to cluster the candidate earthquake waveforms.

[0073] In one specific embodiment, Z1+Z2+Z3+Z4=1.

[0074] As described above, based on the normalized initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data, combined with the importance of the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data in characterizing the shape of the candidate earthquake waveform, the frequency distribution of the waveform and other waveform characteristics, the second waveform similarity between the two candidate earthquake waveforms is calculated, which improves the calculation accuracy of the second waveform similarity and thereby improves the accuracy of the second clustering set.

[0075] S600, based on W1 first cluster sets and W2 second cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0076] In a specific implementation, S600 further includes the following steps:

[0077] S610, for the γth first cluster set, obtain the first number of identical candidate seismic waveforms between the γth first cluster set and W2 second cluster sets, where γ=1, 2, ..., W1, W1 refers to the total number of first cluster sets.

[0078] S620: Determine the second cluster set corresponding to the largest first quantity as an intermediate cluster set matching the γth first cluster set.

[0079] S640, determine the third cluster set corresponding to the γth first cluster set by the intersection of the γth first cluster set and the matching intermediate cluster set, and determine the repeated earthquake category corresponding to the γth first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0080] S630, traverse W1 first cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0081] Among them, the first cluster set is obtained by grouping according to the first waveform similarity between the candidate earthquake waveform and the standard waveform template. Therefore, the first cluster set takes into account the similarity between the candidate earthquake waveform and the standard waveform template in the overall waveform. The second cluster set is obtained by calculating the second waveform similarity clustering between two candidate earthquake waveforms by combining the importance of the initial fluctuation time, fluctuation end time, maximum frequency peak and inflection point data in characterizing the shape of the candidate earthquake waveform, the frequency distribution of the waveform and other waveform features. Therefore, the second cluster set takes into account the local waveform features of the candidate earthquake waveform.

[0082] In the above, the first cluster set and the second cluster set are matched, and the intersection of the first cluster set and the matching intermediate cluster set is determined as the corresponding third cluster set. The third cluster set comprehensively considers the similarity between the candidate earthquake waveform and the standard waveform template in the overall waveform, as well as the local waveform characteristics of the candidate earthquake waveform, thereby improving the accuracy of the third cluster set and further improving the accuracy of identifying repeated earthquakes.

[0083] In the above, a candidate earthquake waveform set C, a preset standard waveform template set D and a preset weight set Z are obtained. According to C and D, a first waveform similarity set E corresponding to C is obtained. According to E, P candidate earthquake waveforms are grouped to obtain W1 first cluster sets. The similarity between the candidate earthquake waveforms and the standard waveform templates in the overall waveform is considered, and the candidate earthquake waveforms and the standard waveform templates are matched, thereby improving the accuracy of the first cluster set. According to C, a waveform analysis data set H corresponding to C is obtained. According to Z, H is clustered to obtain W2 second cluster sets, and the local waveform characteristics of the candidate earthquake waveforms are considered. , clustering the candidate earthquake waveforms is performed to improve the accuracy of the second clustering set; based on W1 first clustering sets and W2 second clustering sets, the third clustering set corresponding to each first clustering set is obtained, and the repeated earthquake category corresponding to each first clustering set is determined as the repeated earthquake category corresponding to the corresponding third clustering set, and the matching between the first clustering set and the second clustering set is comprehensively considered, the similarity between the candidate earthquake waveform and the standard waveform template in the overall waveform, and the local waveform characteristics of the candidate earthquake waveform are comprehensively considered, the accuracy of the third clustering set is improved, and then the recognition accuracy of repeated earthquakes is improved.

[0084] Embodiment 2

[0085] The second embodiment provides a repeated earthquake identification device, the repeated earthquake identification device comprising: Figure 2 As shown:

[0086] Data acquisition module 21, used to acquire candidate seismic waveform set C = {C1, C2, ..., C u, ..., C P} and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q}, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D v It refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates.

[0087] The similarity calculation module 22 is used to obtain the first waveform similarity set E corresponding to C according to C and D = {E1, E2, ..., E u ,……,E P}, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ}, E uv C u and D v The waveform similarity between them.

[0088] The waveform grouping module 23 is used to group P candidate seismic waveforms according to E to obtain W1 first cluster sets, wherein each first cluster set includes several candidate seismic waveforms, and the repeated earthquake category corresponding to each first cluster set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set.

[0089] The waveform analysis module 24 is used to obtain the waveform analysis data set H corresponding to C according to C = {H1, H2, ..., H u , ..., H P}, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u}, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C uThe corresponding inflection point data set, G u = {G u1 , G u2 , ..., G ur , ..., G uR}, C u The rth inflection point data G ur = {G ur 1 , G ur 2}, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point.

[0090] The waveform clustering module 25 is used to obtain a preset weight set Z = {Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second clustering sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data. Each second clustering set includes a number of candidate earthquake waveforms.

[0091] The repeated earthquake identification module 26 is used to obtain the third cluster set corresponding to each first cluster set based on W1 first cluster sets and W2 second cluster sets, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0092] In a specific implementation, the data acquisition module 21 further includes:

[0093] The data acquisition submodule is used to acquire the seismic wave frequency domain data set A = {A1, A2, ..., A i , ..., A N}, where the seismic wave frequency data list A corresponding to the i-th preset time point i ={A i1 , A i2 , ..., A ij , ..., A iM(i)}, A ij It refers to the jth seismic wave frequency corresponding to the i-th preset time point, A i1 >A i2 >……>A ij >……>A iM(i), i=1, 2, ..., N, N refers to the total number of preset time points, j=1, 2, ..., M(i), M(i) refers to the total number of seismic wave frequencies corresponding to the i-th preset time point.

[0094] The frequency discontinuity duration acquisition submodule is used to obtain the frequency discontinuity duration according to A i , get A i Corresponding frequency interruption duration list B i = {B i1 , B i2 , ..., B ik , ..., B iL(i)}, where B ik A i The corresponding k-th frequency discontinuity duration, k = 1, 2, ..., L(i), L(i) refers to A i The total number of corresponding frequency interruption durations.

[0095] The time point screening submodule is used to select i , the preset first frequency threshold range, B i and the preset frequency discontinuity duration threshold, and select P target time points from N preset time points.

[0096] The seismic waveform extraction submodule is used to extract the seismic waveforms corresponding to P target time points respectively, and obtain the candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P}.

[0097] In a specific implementation, the data acquisition submodule further includes:

[0098] The data acquisition unit is used to acquire time domain waveform data within a preset time period according to a preset acoustic emission monitoring system, wherein the preset time period includes N preset time points.

[0099] The data conversion unit is used to convert the time domain waveform data into the frequency domain, and obtain the seismic wave frequency domain data set A = {A1, A2, ..., A i , ..., A N}.

[0100] In a specific implementation, the waveform grouping module 23 further includes:

[0101] The first waveform grouping submodule is used if max(E u ) The corresponding standard waveform template is D v , then C u Group to D v In the corresponding first cluster set, D v The corresponding repeated earthquake category is determined as Dv The repeated earthquake categories corresponding to the corresponding first cluster set, where max() refers to the maximum value function.

[0102] The second waveform grouping submodule is used to traverse E1, E2, ..., E u ,……,E P , and obtain W1 first clustering sets.

[0103] In a specific implementation, the waveform clustering module 25 further includes:

[0104] The normalization submodule is used to normalize H and obtain the normalized waveform analysis data set H 1 ={H 1 1, H 1 2, ..., H 1 u , ..., H 1 P}, where C u The corresponding normalized waveform analysis data H 1 u ={T 11 u , T 12 u , F 1 u , G 1 u}, T 11 u C u The corresponding normalized initial fluctuation time, T 12 u C u The corresponding normalized fluctuation end time, F 1 u C u The corresponding normalized maximum frequency peak, G 1 u C u The corresponding normalized inflection point data set, G 1 u = {G 1 u1 , G 1 u2 , ..., G 1 ur , ..., G 1 uR}, C u The normalized r-th inflection point data G 1 ur = {G 1 ur1 , G 1 ur 2}, G 1 ur 1 C u The normalized time corresponding to the rth inflection point, G 1 ur 2 C u The normalized frequency corresponding to the r-th inflection point.

[0105] The second similarity calculation submodule is used to calculate the similarity according to Z and H 1 u and H 1 α , obtain the second waveform similarity β between the u-th candidate earthquake waveform and the α-th candidate earthquake waveform uα , where H 1 α refers to the αth candidate earthquake waveform C α The corresponding normalized waveform analysis data, α∈{1, 2, ..., P} and α≠u, β uα Meet the following conditions:

[0106] β uα =Z1×dis(T 11 u , T 11 α )+Z2×dis(T 12 u , T 12 α )+Z3×dis(F 1 u , F 1 α )+Z4×dis(G 1 u , G 1 α ),in,

[0107] dis(T 11 u , T 11 α ) refers to T 11 u and T 11 α The first distance between 12 u , T 12 α ) refers to T 12 u and T12 α The second distance between 1 u , F 1 α ) refers to F 1 u and F 1 α The third distance between 1 u , G 1 α ) refers to G 1 u and G 1 α The fourth distance between.

[0108] The first clustering submodule is used if β uα >β0, the uth candidate seismic waveform and the αth candidate seismic waveform are clustered into the same cluster set, where β0 refers to the preset similarity threshold.

[0109] The second clustering submodule is used if β uα ≤β0, the u-th candidate earthquake waveform and the α-th candidate earthquake waveform are clustered into different cluster sets.

[0110] The third clustering submodule is used to traverse H 1 1, H 1 2, ..., H 1 u , ..., H 1 P , and obtain W2 second clustering sets.

[0111] In one specific embodiment, Z1+Z2+Z3+Z4=1.

[0112] In a specific embodiment, the repeated earthquake identification module 26 further includes:

[0113] The first quantity acquisition submodule is used to obtain the first quantity of the same candidate seismic waveforms between the γth first cluster set and W2 second cluster sets for the γth first cluster set, where γ=1, 2,..., W1, W1 refers to the total quantity of the first cluster sets.

[0114] The waveform matching submodule is used to determine the second cluster set corresponding to the largest first quantity as an intermediate cluster set matching the γth first cluster set.

[0115] The third cluster set acquisition submodule is used to determine the intersection of the γth first cluster set and the matching intermediate cluster set as the third cluster set corresponding to the γth first cluster set, and to determine the repeated earthquake category corresponding to the γth first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0116] The repeated earthquake identification submodule is used to traverse W1 first cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0117] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0118] Embodiment 3

[0119] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the steps:

[0120] S100, obtaining a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P} and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q}, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D v It refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates.

[0121] S200, according to C and D, obtain the first waveform similarity set E corresponding to C = {E1, E2, ..., E u ,……,E P}, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ}, E uv C u and D v The waveform similarity between them.

[0122] S300, grouping P candidate seismic waveforms according to E, obtaining W1 first clustering sets, wherein each first clustering set includes several candidate seismic waveforms, and the repeated earthquake category corresponding to each first clustering set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set.

[0123] S400, according to C, obtain the waveform analysis data set H corresponding to C = {H1, H2, ..., H u , ..., H P}, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u}, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u = {G u1 , G u2 , ..., G ur , ..., G uR}, C u The rth inflection point data G ur = {G ur 1 , G ur 2}, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point.

[0124] S500, obtain a preset weight set Z = {Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second cluster sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data, and each second cluster set includes several candidate earthquake waveforms.

[0125] S600, based on W1 first cluster sets and W2 second cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0127] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0128] Embodiment 4

[0129] A fourth embodiment of the present invention provides an electronic device, which includes a processor and the non-transitory computer-readable storage medium in the third embodiment of the present invention.

[0130] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A repeated earthquake identification method, characterized in that: The repeated earthquake identification method comprises the following steps: S100, obtain a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P } and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q }, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D v refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates; S200, based on C and D, obtain the first waveform similarity set E corresponding to C = {E1, E2, ..., E u ,……,E P }, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ }, E uv C u and D v The waveform similarity between them; S300, grouping P candidate seismic waveforms according to E to obtain W1 first cluster sets, wherein each first cluster set includes a number of candidate seismic waveforms, and the repeated seismic category corresponding to each first cluster set is consistent with the repeated seismic category corresponding to the corresponding standard waveform template set; S400, according to C, obtain the waveform analysis data set H corresponding to C = {H1, H2, ..., H u , ..., H P }, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u }, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u ={G u1 , G u2 , ..., G ur , ..., G uR }, C u The rth inflection point data G ur ={G ur 1 , G ur 2 }, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point; S500, obtaining a preset weight set Z={Z1, Z2, Z3, Z4}, clustering H according to Z, and obtaining W2 second cluster sets, wherein Z1 refers to a first weight corresponding to an initial fluctuation time, Z2 refers to a second weight corresponding to a fluctuation end time, Z3 refers to a third weight corresponding to a maximum frequency peak, and Z4 refers to a fourth weight corresponding to an inflection point data, and each second cluster set includes a number of candidate earthquake waveforms; S600, according to the W1 first cluster sets and the W2 second cluster sets, a third cluster set corresponding to each first cluster set is obtained, and the repeated earthquake category corresponding to each first cluster set is determined as the repeated earthquake category corresponding to the corresponding third cluster set, wherein S600 also includes the following steps: S610, for the γth first cluster set, obtaining a first number of identical candidate seismic waveforms between the γth first cluster set and W2 second cluster sets, where γ=1, 2, ..., W1, W1 refers to the total number of the first cluster sets; S620, determining the second cluster set corresponding to the largest first quantity as an intermediate cluster set matching the γth first cluster set; S640, determining the intersection of the γth first cluster set and the matching intermediate cluster set as the third cluster set corresponding to the γth first cluster set, and determining the repeated earthquake category corresponding to the γth first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set; S630, traverse W1 first cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

2. The repeated earthquake identification method according to claim 1, characterized in that: S100 also includes the following steps: S110, obtaining a seismic wave frequency domain data set A={A1, A2, ..., A i , ..., A N }, where the seismic wave frequency data list A corresponding to the i-th preset time point i ={A i1 , A i2 , ..., A ij , ..., A iM(i) }, A ij It refers to the jth seismic wave frequency corresponding to the i-th preset time point, A i1 >A i2 >……>A ij >……>A iM(i) , i=1, 2, ..., N, N refers to the total number of preset time points, j=1, 2, ..., M(i), M(i) refers to the total number of seismic wave frequencies corresponding to the i-th preset time point; S120, according to A i , get A i Corresponding frequency interruption duration list B i ={B i1 , B i2 , ..., B ik , ..., B iL(i) }, where B ik A i The corresponding k-th frequency discontinuity duration, k = 1, 2, ..., L(i), L(i) refers to A i The total number of corresponding frequency discontinuity durations; S130, according to A i , the preset first frequency threshold range, B i and the preset frequency discontinuity duration threshold, and select P target time points from N preset time points; S140, extract the seismic waveforms corresponding to the P target time points respectively, and obtain a candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P }.

3. The repeated earthquake identification method according to claim 2, characterized in that: S110 also includes the following steps: S111, acquiring time domain waveform data within a preset time period according to a preset acoustic emission monitoring system, wherein the preset time period includes N preset time points; S112, converting the time domain waveform data into the frequency domain, obtaining a seismic wave frequency domain data set A={A1, A2, ..., A i , ..., A N }.

4. The repeated earthquake identification method according to claim 1, characterized in that: S300 also includes the following steps: S310, if max(E u ) The corresponding standard waveform template is D v , then C u Group to D v In the corresponding first cluster set, D v The corresponding repeated earthquake category is determined as D v The repeated earthquake categories corresponding to the first cluster set, where max() refers to the maximum value function; S320, traverse E1, E2, ..., E u ,……,E P , and obtain W1 first clustering sets.

5. The repeated earthquake identification method according to claim 1, characterized in that: S500 also includes the following steps: S510, normalize H to obtain a normalized waveform analysis data set H 1 ={H 1 1, H 1 2, ..., H 1 u , ..., H 1 P }, where C u The corresponding normalized waveform analysis data H 1 u ={T 11 u , T 12 u , F 1 u , G 1 u }, T 11 u C u The corresponding normalized initial fluctuation time, T 12 u C u The corresponding normalized fluctuation end time, F 1 u C u The corresponding normalized maximum frequency peak, G 1 u C u The corresponding normalized inflection point data set, G 1 u ={G 1 u1 , G 1 u2 , ..., G 1 ur , ..., G 1 uR }, C u The normalized r-th inflection point data G 1 ur ={G 1 ur 1 , G 1 ur 2 }, G 1 ur 1 C u The normalized time corresponding to the rth inflection point, G 1 ur 2 C u The normalized frequency corresponding to the r-th inflection point; S520, according to Z, H 1 u and H 1 α , obtain the second waveform similarity β between the u-th candidate earthquake waveform and the α-th candidate earthquake waveform uα , where H 1 α refers to the αth candidate earthquake waveform C α The corresponding normalized waveform analysis data, α∈{1, 2, ..., P} and α≠u, β uα Meet the following conditions: β uα =Z1×dis(T 11 u , T 11 α )+Z2×dis(T 12 u , T 12 α )+Z3×dis(F 1 u , F 1 α )+Z4×dis(G 1 u , G 1 α ), where dis(T 11 u , T 11 α ) refers to T 11 u and T 11 α The first distance between 12 u , T 12 α ) refers to T 12 u and T 12 α The second distance between 1 u , F 1 α ) refers to F 1 u and F 1 α The third distance between 1 u , G 1 α ) refers to G 1 u and G 1 α The fourth distance between S530, if β uα >β0, the u-th candidate seismic waveform and the α-th candidate seismic waveform are clustered into the same cluster set, where β0 refers to a preset similarity threshold; S540, if β uα ≤β0, clustering the u-th candidate seismic waveform and the α-th candidate seismic waveform into different clustering sets; S550, traverse H 1 1, H 1 2, ..., H 1 u , ..., H 1 P , and obtain W2 second clustering sets.

6. The repeated earthquake identification method according to claim 5, characterized in that: Z1+Z2+Z3+Z4=1.

7. A repeated earthquake identification device, characterized in that: The repeated earthquake identification device comprises: The data acquisition module is used to obtain the candidate seismic waveform set C = {C1, C2, ..., C u , ..., C P } and the preset standard waveform template set D = {D1, D2, ..., D v , ..., D Q }, where C u refers to the candidate earthquake waveform corresponding to the u-th target time point, D v refers to the vth standard waveform template, u=1, 2, ..., P, P refers to the total number of target time points, v=1, 2, ..., Q, Q refers to the total number of standard waveform templates; The similarity calculation module is used to obtain the first waveform similarity set E={E1, E2, ..., E u ,……,E P }, where C u The corresponding first waveform similarity list E u ={E u1 , E u2 ,……,E uv ,……,E uQ }, E uv C u and D v The waveform similarity between them; A waveform grouping module is used to group P candidate seismic waveforms according to E to obtain W1 first cluster sets, wherein each first cluster set includes a number of candidate seismic waveforms, and the repeated earthquake category corresponding to each first cluster set is consistent with the repeated earthquake category corresponding to the corresponding standard waveform template set; The waveform analysis module is used to obtain the waveform analysis data set H={H1, H2, ..., H u , ..., H P }, where C u Corresponding waveform analysis data H u ={T 1 u , T 2 u , F u , G u }, T 1 u C u The corresponding initial fluctuation time, T 2 u C u The corresponding fluctuation end time, F u C u The corresponding maximum frequency peak, G u C u The corresponding inflection point data set, G u ={G u1 , G u2 , ..., G ur , ..., G uR }, C u The rth inflection point data G ur ={G ur 1 , G ur 2 }, G ur 1 C u The time corresponding to the rth inflection point, G ur 2 C u The frequency corresponding to the rth inflection point; A waveform clustering module is used to obtain a preset weight set Z={Z1, Z2, Z3, Z4}, cluster H according to Z, and obtain W2 second cluster sets, wherein Z1 refers to the first weight corresponding to the initial fluctuation time, Z2 refers to the second weight corresponding to the fluctuation end time, Z3 refers to the third weight corresponding to the maximum frequency peak, and Z4 refers to the fourth weight corresponding to the inflection point data, and each second cluster set includes a number of candidate earthquake waveforms; A repeated earthquake identification module is used to obtain a third cluster set corresponding to each first cluster set according to the W1 first cluster sets and the W2 second cluster sets, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set, wherein the repeated earthquake identification module also includes: A first quantity acquisition submodule is used to acquire, for a γth first cluster set, a first quantity of identical candidate seismic waveforms between the γth first cluster set and W2 second cluster sets, wherein γ=1, 2, ..., W1, W1 refers to the total quantity of the first cluster sets; A waveform matching submodule, used to determine the second cluster set corresponding to the largest first quantity as an intermediate cluster set matching the γth first cluster set; The third cluster set acquisition submodule is used to determine the intersection of the γth first cluster set and the matching intermediate cluster set as the third cluster set corresponding to the γth first cluster set, and determine the repeated earthquake category corresponding to the γth first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set; The repeated earthquake identification submodule is used to traverse W1 first cluster sets, obtain the third cluster set corresponding to each first cluster set, and determine the repeated earthquake category corresponding to each first cluster set as the repeated earthquake category corresponding to the corresponding third cluster set.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the repeated earthquake identification method as described in any one of claims 1-6.

9. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 8.

Images