Regional seismic activity monitoring and analyzing method

Abstract

The invention relates to the technical field of earthquake monitoring, in particular to a regional earthquake activity monitoring and analyzing method, which comprises the following steps: acquiring earthquake waveform data corresponding to each station in a corresponding station network according to the corresponding preset station network in a to-be-monitored region; the station net comprises a plurality of stations; according to the acquired seismic waveform data corresponding to each station, based on a preset seismic automatic positioning detection strategy, automatically detecting and positioning each corresponding seismic event from the seismic waveform data, and forming a corresponding micro-seismic directory; according to the acquired seismic waveform data corresponding to each station, a three-dimensional speed structure corresponding to the to-be-measured area is inverted based on a preset three-dimensional speed result inversion strategy; and according to the corresponding micro-seismic directory and the corresponding three-dimensional velocity structure, analyzing and predicting seismic activity corresponding to the to-be-detected area, and forming seismic activity analysis result data corresponding to the to-be-detected area.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake monitoring, and in particular to a method for monitoring and analyzing regional earthquake activity. Background Art

[0002] With the development of earthquake science and geophysics, earthquake monitoring technology has become an important tool for disaster prevention, scientific research and engineering safety assessment. Earthquake monitoring can not only provide real-time information on earthquake activity, but also help scientists understand the mechanism of earthquake occurrence, predict future earthquake activity, and provide key data support for earthquake early warning systems through the analysis of historical data.

[0003] Most of the existing earthquake activity monitoring systems predict earthquake activity through the existing historical earthquake data and the geological data corresponding to the area to be tested through a preset earthquake monitoring model, thereby realizing the monitoring and analysis of earthquake activity in the area to be tested. Although the analysis and monitoring of earthquake activity in the area to be tested are realized, since most of the corresponding data are historically collected data, the corresponding accuracy and reliability are not high during the corresponding earthquake activity monitoring and analysis process, and it is impossible to truly perform real-time and accurate analysis of earthquake activity in the area to be tested. Summary of the invention

[0004] One of the purposes of the present invention is to provide a regional seismic activity monitoring and analysis method, which can monitor and analyze the seismic activity in the area to be measured in real time, and improve the accuracy and reliability of the seismic activity monitoring and analysis.

[0005] In order to achieve the above object, a regional seismic activity monitoring and analysis method is provided, comprising the following steps:

[0006] S1. According to a pre-set station network corresponding to the area to be tested, obtaining seismic waveform data corresponding to each station in the corresponding station network; the station network includes a plurality of stations;

[0007] S2. According to the seismic waveform data corresponding to each station obtained, based on a preset earthquake automatic location detection strategy, automatically detect and locate the corresponding seismic events from the seismic waveform data and form a corresponding microseismic catalog;

[0008] S3, according to the seismic waveform data corresponding to each station obtained, based on the preset three-dimensional velocity result inversion strategy, invert the three-dimensional velocity structure corresponding to the area to be tested;

[0009] S4. Analyze and predict the seismic activity corresponding to the area to be measured according to the corresponding microseismic catalog and the corresponding three-dimensional velocity structure, and form seismic activity analysis result data corresponding to the area to be measured.

[0010] Technical principles and effects of this scheme: In this scheme, firstly, stations are set up in the area to be tested to form a corresponding station network, and then the seismic waveform data of the area to be tested is monitored through the station network, and the seismic waveform data corresponding to each station is collected. Through the arrangement of the station network, all-round monitoring of seismic activities in the area to be tested is achieved.

[0011] Afterwards, according to the seismic waveform data corresponding to each station obtained, the respective data processing is carried out through the preset earthquake automatic positioning detection strategy and the preset three-dimensional velocity result inversion strategy, that is, the corresponding seismic events are automatically detected and located from the seismic waveform data, and the corresponding microseismic catalog is formed, and the three-dimensional velocity structure corresponding to the test area is inverted.

[0012] Finally, according to the corresponding microseismic catalog and the corresponding three-dimensional velocity structure, the seismic activity corresponding to the tested area is analyzed and predicted to form the seismic activity analysis result data corresponding to the tested area.

[0013] In this scheme, the seismic waveform data of each station is collected, and the corresponding microseismic catalog and three-dimensional velocity structure are obtained through subsequent data processing through corresponding strategies. Therefore, the seismic activity corresponding to the measured area can be reliably and accurately analyzed and monitored in real time through the microseismic catalog and the corresponding three-dimensional velocity structure, which greatly improves the reliability and accuracy of seismic activity monitoring and analysis.

[0014] Furthermore, the preset earthquake automatic location detection strategy includes the following steps:

[0015] According to the seismic waveform data corresponding to each station obtained, based on the STA / LTA long-short window energy ratio, the P-wave peak value of the Z component corresponding to the seismic waveform data is detected, and the P-wave capacity is suppressed on the HP component through a polarization filter, and the S-wave peak value is identified again through STA / LTA, and the P-wave and S-wave phase arrival times corresponding to each station are determined, and the phase arrival times are the arrival times of the corresponding P-wave and S-wave at the corresponding station;

[0016] According to the arrival time of the seismic phase of the P wave and the S wave corresponding to each station, similar waveform data appearing in the same time period are searched and the waveform data corresponding to the consistent arrival time of the P wave and the S wave at different stations are determined, and the corresponding waveform data are associated;

[0017] According to the arrival time of the P-wave and S-wave phases corresponding to each station, the difference between the waveform arrival time and the theoretical travel time corresponding to each station in the test area is calculated, and the position corresponding to the minimum difference value is matched, and this position is used as the corresponding optimal epicenter position;

[0018] According to the arrival time of the P-wave and S-wave phases corresponding to each station and the determined optimal epicenter position, the absolute positioning of the earthquake event is performed by the HypoInverse positioning method to form the corresponding event templates and the corresponding PAL template event catalog;

[0019] According to the PAL template event catalog, the cross-correlation function between the event template corresponding to each station in the PAL template event catalog and the seismic waveform data corresponding to each station is calculated;

[0020] According to the arrival time of the seismic phases of the P wave and the S wave corresponding to each station, the cross-correlation functions corresponding to each station are aligned in travel time to form the aligned cross-correlation functions corresponding to each station;

[0021] Perform peak mask expansion on the cross-correlation functions corresponding to the aligned stations, and linearly superimpose the cross-correlation functions after mask expansion;

[0022] According to the relative travel time between each event template and the detected event, the relative position of the event self-detection is determined based on the double-difference relocation algorithm, and the corresponding microseismic catalog is formed.

[0023] Beneficial effects: In this scheme, the arrival time of P waves and S waves is automatically detected by the STA / LTA algorithm, which improves the accuracy and automation of detection. At the same time, the application of polarization filters reduces the interference of P waves on S wave detection. Through the association of waveform data, it is confirmed that different stations observe the same earthquake event, which enhances the consistency and reliability of event detection. The difference between the waveform arrival time and the theoretical travel time of each station is calculated, and the position with the smallest difference value is selected as the best epicenter position. The epicenter position is accurately determined using the station observation data and theoretical model, which improves the positioning accuracy. After that, an event template is established, which provides a reference for subsequent event identification and classification, and also forms a standardized event catalog. Through the calculation of the cross-correlation function, a mathematical tool for event identification is provided, which is convenient for subsequent waveform matching. The cross-correlation function is aligned in travel time to form an aligned cross-correlation function. Through alignment, the time difference caused by different propagation paths is eliminated, making waveform comparison more intuitive and effective.

[0024] Based on the double-difference relocation algorithm, the relative positions between events are determined and a micro-earthquake catalog is formed. The double-difference relocation technology is used to further improve the accuracy of earthquake event positioning, especially for the detection and positioning of tiny earthquake events. It significantly improves the accuracy and automation level of automatic detection and positioning of earthquake events, allowing the earthquake monitoring system to work efficiently without human intervention.

[0025] Further, the preset three-dimensional velocity result inversion strategy includes the following steps:

[0026] Preprocess the seismic waveform data corresponding to each station;

[0027] Perform spectrum whitening on the pre-processed seismic waveform data corresponding to each station in the frequency domain, and calculate the cross-correlation function between each station;

[0028] The time derivative of the calculated cross-correlation function is calculated to obtain the corresponding forward derivative result and reverse derivative result;

[0029] The forward derivative results and the reverse derivative results are averaged in reverse order to finally obtain the equivalent empirical Green's function;

[0030] According to the obtained empirical Green's function, the corresponding fundamental-order Rayleigh surface wave group velocity curve is extracted by image analysis, and the corresponding three-dimensional velocity structure is directly inverted from the extracted fundamental-order Rayleigh surface wave group velocity curve.

[0031] Beneficial effects: In this scheme, the seismic waveform data corresponding to each station is first preprocessed, thereby improving the data quality, reducing the error caused by signal interference, and making subsequent processing more reliable.

[0032] The preprocessed seismic waveform data is then subjected to spectral whitening in the frequency domain to make the data have the same energy distribution in the frequency domain, eliminate the correlation between different frequency components, enhance the signal characteristics, and make the subsequent calculated cross-correlation function more stable and reliable.

[0033] Then, the time delay relationship of the signals received at different stations is revealed, which provides a basis for subsequent analysis. By calculating the cross-correlation function between each station, the time difference information of signal propagation is obtained, which provides important clues to the signal propagation path and speed, and helps to understand the propagation characteristics of seismic waves. The time derivative of the cross-correlation function is calculated to obtain the forward derivative results and the reverse derivative results respectively. The details of the signal change are enhanced to help better identify the start and end time of the signal. The forward derivative results and the reverse derivative results are averaged in reverse order to finally obtain the equivalent empirical Green's function. The influence of noise is reduced and the reliability of the empirical Green's function is improved. The fundamental Rayleigh surface wave group velocity curve in the empirical Green's function is extracted by image analysis technology, which provides key information on the velocity structure of the underground medium. That is, through a series of data processing and analysis steps, useful information is gradually extracted from the original seismic waveform data, and finally the three-dimensional velocity structure of the underground medium is inverted. This method not only improves the accuracy and reliability of data processing, but also provides important tools and technical support for seismological research.

[0034] Furthermore, the setting strategy corresponding to the preset network in the area to be tested in S1 is:

[0035] Randomly generate N initial populations, where individuals of the initial population are a collection of several station locations set in the area to be measured;

[0036] Based on the preset constraints, the initial population is preliminarily screened and judged. If the constraints are met, the corresponding population is a feasible solution, otherwise it is an infeasible solution and the corresponding population is eliminated;

[0037] Calculating the first fitness and the second fitness of the selected population according to the preset first fitness calculation formula and the second fitness calculation formula;

[0038] The first fitness calculation formula is:

[0039]

[0040] In the formula, F1 is the first fitness, d ij is the distance between the i-th station location and the j-th station location in the population, (x i ,y i ,z i ) is the position coordinate corresponding to the i-th station in the population, ∝ is the corresponding preset value, h mix is the height threshold corresponding to the z coordinate, n is the total number of station locations corresponding to the population;

[0041] The second fitness calculation formula is:

[0042]

[0043] In the formula, F2 is the second fitness, D2 is the monitoring benefit value corresponding to the population, S k is the monitoring sensitivity corresponding to the kth station position in the population, W k is the monitoring coverage corresponding to the kth station location in the population, α and β are the corresponding weighting coefficients;

[0044] Within the first preset number of iterations, according to the first fitness corresponding to the population, a population whose first fitness is less than or equal to the first fitness threshold is selected; and among the populations eliminated at this time, a population whose second fitness is in the top P% is selected and saved in a spare library; the size of P is negatively correlated with the current number of iterations;

[0045] When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and the population corresponding to the current population whose second fitness is less than the second fitness threshold is selected;

[0046] The selected population is hybridized and mutated to obtain the corresponding offspring population;

[0047] After obtaining the offspring population, continue to calculate the fitness until the preset number of iterations is met;

[0048] The output offspring population is used as the optimal solution set for multi-objective optimization.

[0049] Beneficial effects: In this scheme, the randomly generated initial population is first preliminarily screened and then screened by restrictive conditions, thereby improving the reliability of the initial population.

[0050] After that, the first fitness and the second fitness are calculated for the selected population. In the process of calculating the first fitness, the calculation method of the distance value between the station positions is selected by comparing the height difference between the adjacent station positions on the z coordinate with the preset height threshold. In this way, the first fitness corresponding to the station positions with a larger height difference is larger, so as to realize the screening of the population and improve the globality of the algorithm. When calculating the second fitness, the second fitness is calculated by calculating the monitoring sensitivity and the monitoring coverage.

[0051] When the population is screened again later, after selecting the population that meets the first fitness threshold, the eliminated population is selected for the second time and saved in the spare library, wherein the selection method is to select the population whose second fitness is in the top P%; the size of P is negatively correlated with the current number of iterations, and the setting of P makes the population in the spare library more excellent, which facilitates the preservation of excellent genes in subsequent screening and improves the corresponding convergence speed of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the regional seismic activity monitoring and analysis method in Example 1 of the present invention.

[0053] Figure 2 This is a layout diagram of the Jiangkou station network in a certain area in the first embodiment of the present invention.

[0054] Figure 3 This is a flow chart of a setting strategy corresponding to a pre-set station network in the area to be tested in the first embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following is further described in detail through specific implementation methods:

[0056] Embodiment 1

[0057] The regional seismic activity monitoring and analysis method is basically as follows Figure 1 As shown, the following steps are included:

[0058] S1. According to a pre-set station network corresponding to the area to be tested, obtaining seismic waveform data corresponding to each station in the corresponding station network; the station network includes a plurality of stations;

[0059] like Figure 3 As shown, the setting strategy corresponding to the preset network in the area to be tested in S1 is:

[0060] Randomly generate N initial populations, where individuals of the initial population are a collection of several station locations set in the area to be measured;

[0061] Based on the preset restriction conditions, the initial population is preliminarily screened and judged. If the restriction conditions are met, the corresponding population is a feasible solution, otherwise it is an infeasible solution and the corresponding population is eliminated. In this embodiment, the restriction conditions are that the station position repeatability is less than the preset repeatability threshold.

[0062] Calculating the first fitness and the second fitness of the selected population according to the preset first fitness calculation formula and the second fitness calculation formula;

[0063] The first fitness calculation formula is:

[0064]

[0065] In the formula, F1 is the first fitness, d ij is the distance between the i-th station location and the j-th station location in the population, (x i ,y i ,z i ) is the position coordinate corresponding to the i-th station in the population, ∝ is the corresponding preset value, h mix is the height threshold corresponding to the z coordinate, n is the total number of station locations corresponding to the population;

[0066] The second fitness calculation formula is:

[0067]

[0068] In the formula, F2 is the second fitness, D2 is the monitoring benefit value corresponding to the population, S k is the monitoring sensitivity corresponding to the kth station position in the population, W k is the monitoring coverage corresponding to the kth station location in the population, α and β are the corresponding weighting coefficients;

[0069] Within the first preset number of iterations, according to the first fitness corresponding to the population, a population whose first fitness is less than or equal to the first fitness threshold is selected; and among the populations eliminated at this time, a population whose second fitness is in the top P% is selected and saved in a spare library; the size of P is negatively correlated with the current number of iterations;

[0070] When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and a population whose second fitness corresponding to the current population is less than the preset second fitness is selected;

[0071] The selected population is hybridized and mutated to obtain the corresponding offspring population;

[0072] After obtaining the offspring population, continue to calculate the fitness until the preset number of iterations is met;

[0073] The offspring population is output as the optimal solution set for multi-objective optimization. The stations in the area to be measured are subsequently arranged according to the population in the optimal solution set, for example Figure 2 As shown, this is the layout of the station network at the mouth of a river corresponding to a certain area.

[0074] S2. According to the seismic waveform data corresponding to each station obtained, based on a preset earthquake automatic location detection strategy, automatically detect and locate the corresponding seismic events from the seismic waveform data and form a corresponding microseismic catalog;

[0075] The preset earthquake automatic location detection strategy includes the following steps:

[0076] According to the seismic waveform data corresponding to each station obtained, based on the STA / LTA long-short window energy ratio, the P-wave peak value of the Z component corresponding to the seismic waveform data is detected, and the P-wave capacity is suppressed on the HP component through a polarization filter, and the S-wave peak value is identified again through STA / LTA, and the P-wave and S-wave phase arrival times corresponding to each station are determined, and the phase arrival times are the arrival times of the corresponding P-wave and S-wave at the corresponding station;

[0077] According to the arrival times of the seismic phases of the P wave and the S wave corresponding to each station, similar waveform data appearing in the same time period are searched, and the waveform data corresponding to the consistent arrival times of the P wave and the S wave at different stations are determined, and the corresponding waveform data are associated; in this embodiment, by searching for similar waveform data appearing in the same time period, the search for clusters of earthquake occurrence times is completed, and association in the time domain is achieved.

[0078] According to the arrival time of the seismic phase of the P wave and the S wave corresponding to each station, the difference between the waveform arrival time and the theoretical travel time corresponding to each station in the measured area is calculated, and the position corresponding to the smallest difference value is matched, and this position is used as the corresponding optimal epicenter position; in this embodiment, the association in the spatial domain is achieved by grid searching the epicenter position with the smallest travel time residual.

[0079] According to the arrival time of the P-wave and S-wave phases corresponding to each station and the determined optimal epicenter position, the absolute positioning of the earthquake event is performed by the HypoInverse positioning method to form the corresponding event templates and the corresponding PAL template event catalog;

[0080] According to the PAL template event catalog, the cross-correlation function between the event template corresponding to each station in the PAL template event catalog and the seismic waveform data corresponding to each station is calculated;

[0081] According to the arrival time of the seismic phases of the P wave and the S wave corresponding to each station, the cross-correlation functions corresponding to each station are aligned in travel time to form the aligned cross-correlation functions corresponding to each station;

[0082] The peak values ​​of the cross-correlation functions corresponding to the aligned stations are masked and expanded, and the cross-correlation functions after mask expansion are linearly superimposed; in this embodiment, due to the difference in the epicenter position between the template event and the detected event, the error in the seismic phase picking of the template event, and the change in the wave velocity of the medium, the cross-correlation peaks cannot be completely aligned only by travel time offset, resulting in phase cancellation during subsequent superposition. Such deviations can be reduced by applying the peak mask, thereby improving the detection capability of the template. Secondly, the cross-correlation functions of each station that have been expanded with masks are linearly superimposed. The superposition of multiple stations can effectively suppress the cross-correlation value of the noise, thereby reducing the false recognition rate.

[0083] According to the relative travel time between each event template and the detected event, the relative position of the event self-detection is determined based on the double-difference relocation algorithm, and the corresponding microseismic catalog is formed.

[0084] S3, according to the seismic waveform data corresponding to each station obtained, based on the preset three-dimensional velocity result inversion strategy, invert the three-dimensional velocity structure corresponding to the area to be tested;

[0085] The preset three-dimensional velocity result inversion strategy includes the following steps:

[0086] The seismic waveform data corresponding to each station obtained are preprocessed; in this embodiment, the preprocessing operations include pre-screening, de-averaging, de-trending, resampling, band-pass filtering and normalization.

[0087] Specifically, based on the seismic waveform data corresponding to each station, data segments with poor quality are eliminated, the average value and linear trend of each data segment are removed to eliminate the impact of long-term changes, ensure that all data have the same sampling rate, select a suitable frequency range for bandpass filtering to retain the frequency components of interest, and normalize the data to make the amplitudes of different records comparable.

[0088] The pre-processed seismic waveform data corresponding to each station is subjected to spectrum whitening in the frequency domain, and the cross-correlation function between each station is calculated; in this embodiment, the cross-correlation function is usually calculated on the vertical component.

[0089] The time derivative of the calculated cross-correlation function is calculated to obtain the corresponding forward derivative result and reverse derivative result;

[0090] The forward derivative results and the reverse derivative results are averaged in reverse order to finally obtain the equivalent empirical Green's function;

[0091] According to the obtained empirical Green's function, the corresponding fundamental Rayleigh surface wave group velocity curve is extracted by image analysis, and the corresponding three-dimensional velocity structure is directly inverted from the extracted fundamental Rayleigh surface wave group velocity curve. In this embodiment, the specific extracted curve is the 0.2s-5s fundamental Rayleigh surface wave group velocity curve.

[0092] In addition, although the shallow crust is most sensitive to S waves during the inversion process, it is also relatively sensitive to P waves, so it is necessary to calculate the sensitive kernel of Rayleigh surface wave phase velocity with depth at different periods. Furthermore, in order to determine the spatial resolution of the inversion, it is also necessary to test the recovery of the model at different depths using a test board.

[0093] S4. Analyze and predict the seismic activity corresponding to the area to be tested according to the corresponding microseismic catalog and the corresponding three-dimensional velocity structure, and form the seismic activity analysis result data corresponding to the area to be tested. In this embodiment, the temporal and spatial migration characteristics of seismic activity, the distribution and velocity structure characteristics of earthquakes at different depths, and the changes in velocity structure at different time periods can be analyzed through the microseismic catalog and the corresponding three-dimensional velocity structure, so as to analyze the corresponding earthquake-pregnancy mechanism and the corresponding deep structural environment.

[0094] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for monitoring and analyzing regional seismic activity, characterized in that: The following steps are involved: S1. According to a pre-set station network corresponding to the area to be tested, obtaining seismic waveform data corresponding to each station in the corresponding station network; the station network includes a plurality of stations; S2. According to the seismic waveform data corresponding to each station obtained, based on a preset earthquake automatic location detection strategy, automatically detect and locate the corresponding seismic events from the seismic waveform data and form a corresponding microseismic catalog; S3, according to the seismic waveform data corresponding to each station obtained, based on the preset three-dimensional velocity result inversion strategy, invert the three-dimensional velocity structure corresponding to the area to be tested; S4. Analyze and predict the seismic activity corresponding to the area to be measured according to the corresponding microseismic catalog and the corresponding three-dimensional velocity structure, and form seismic activity analysis result data corresponding to the area to be measured.

2. The regional seismic activity monitoring and analysis method according to claim 1, characterized in that: The preset earthquake automatic location detection strategy includes the following steps: According to the seismic waveform data corresponding to each station obtained, based on the STA / LTA long-short window energy ratio, the P-wave peak value of the Z component corresponding to the seismic waveform data is detected, and the P-wave capacity is suppressed on the HP component through a polarization filter, and the S-wave peak value is identified again through STA / LTA, and the P-wave and S-wave phase arrival times corresponding to each station are determined, and the phase arrival times are the arrival times of the corresponding P-wave and S-wave at the corresponding station; According to the arrival time of the seismic phase of the P wave and the S wave corresponding to each station, similar waveform data appearing in the same time period are searched and the waveform data corresponding to the consistent arrival time of the P wave and the S wave at different stations are determined, and the corresponding waveform data are associated; According to the arrival time of the P-wave and S-wave phases corresponding to each station, the difference between the waveform arrival time and the theoretical travel time corresponding to each station in the test area is calculated, and the position corresponding to the minimum difference value is matched, and this position is used as the corresponding optimal epicenter position; According to the arrival time of the P-wave and S-wave phases corresponding to each station and the determined optimal epicenter position, the absolute positioning of the earthquake event is performed by the HypoInverse positioning method to form the corresponding event templates and the corresponding PAL template event catalog; According to the PAL template event catalog, the cross-correlation function between the event template corresponding to each station in the PAL template event catalog and the seismic waveform data corresponding to each station is calculated; According to the arrival time of the seismic phases of the P wave and the S wave corresponding to each station, the cross-correlation functions corresponding to each station are aligned in travel time to form the aligned cross-correlation functions corresponding to each station; Perform peak mask expansion on the cross-correlation functions corresponding to the aligned stations, and linearly superimpose the cross-correlation functions after mask expansion; According to the relative travel time between each event template and the detected event, the relative position of the event self-detection is determined based on the double-difference relocation algorithm, and the corresponding microseismic catalog is formed.

3. The regional seismic activity monitoring and analysis method according to claim 2 is characterized in that: The preset three-dimensional velocity result inversion strategy includes the following steps: Preprocess the seismic waveform data corresponding to each station; Perform spectrum whitening on the pre-processed seismic waveform data corresponding to each station in the frequency domain, and calculate the cross-correlation function between each station; The time derivative of the calculated cross-correlation function is calculated to obtain the corresponding forward derivative result and reverse derivative result; The forward derivative results and the reverse derivative results are averaged in reverse order to finally obtain the equivalent empirical Green's function; According to the obtained empirical Green's function, the corresponding fundamental-order Rayleigh surface wave group velocity curve is extracted by image analysis, and the corresponding three-dimensional velocity structure is directly inverted from the extracted fundamental-order Rayleigh surface wave group velocity curve.

4. The regional seismic activity monitoring and analysis method according to claim 3 is characterized in that: The setting strategy corresponding to the preset network in the area to be tested in S1 is: Randomly generate N initial populations, where individuals of the initial population are a collection of several station locations set in the area to be measured; Based on the preset constraints, the initial population is preliminarily screened and judged. If the constraints are met, the corresponding population is a feasible solution, otherwise it is an infeasible solution and the corresponding population is eliminated; Calculating the first fitness and the second fitness of the selected population according to the preset first fitness calculation formula and the second fitness calculation formula; The first fitness calculation formula is: In the formula, F1 is the first fitness, d ij is the distance between the i-th station location and the j-th station location in the population, (x i ,y i ,z i ) is the position coordinate corresponding to the i-th station in the population, ∝ is the corresponding preset value, h mix is the height threshold corresponding to the z coordinate, n is the total number of station locations corresponding to the population; The second fitness calculation formula is: In the formula, F2 is the second fitness, D2 is the monitoring benefit value corresponding to the population, S k is the monitoring sensitivity corresponding to the kth station position in the population, W k is the monitoring coverage corresponding to the kth station location in the population, α and β are the corresponding weighting coefficients; Within the first preset number of iterations, according to the first fitness corresponding to the population, a population whose first fitness is less than or equal to the first fitness threshold is selected; and among the populations eliminated at this time, a population whose second fitness is in the top P% is selected and saved in a spare library; the size of P is negatively correlated with the current number of iterations; When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and a population whose second fitness corresponding to the current population is less than the preset second fitness is selected; The selected population is hybridized and mutated to obtain the corresponding offspring population; After obtaining the offspring population, continue to calculate the fitness until the preset number of iterations is met; The output offspring population is used as the optimal solution set for multi-objective optimization.

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