A method and system for calculating a seismic activity parameter

Abstract

The application provides a method and system for calculating seismic activity parameters. The method comprises: fitting the magnitude and cumulative annual average occurrence rate of all seismic events in a seismic statistical area to obtain a first fitting result, and calculating the activity parameters in the fitting process to obtain a set of optimized values of the activity parameters; evaluating the first fitting result by using multiple indexes, adjusting the values of the activity parameters by using multiple adjustment modes according to the optimized values of the activity parameters to obtain multiple sets of adjusted values of the activity parameters; and selecting the optimal adjusted value of the activity parameters from the adjusted values according to multiple second fitting effect evaluation results, wherein each second fitting result is a fitting result corresponding to a set of adjusted values of the activity parameters. The technical scheme provided by the application optimizes the activity parameters by multiple index evaluation and multiple mode parameter adjustment, and improves the accuracy and reliability of the fitting of the relationship between the magnitude and the cumulative annual average occurrence rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of earthquake monitoring, and in particular to a calculation method and system of seismic activity parameters. BACKGROUND

[0002] In seismology, the Gutenberg-Richter law is a statistical model describing the relationship between earthquake magnitude and occurrence frequency. The activity parameters in the statistical model include the intercept parameter (a value) and the slope parameter (b value). The a value is the logarithmic value of the annual average occurrence rate of all seismic events in a unit time and a unit area, and the b value is a key indicator for assessing regional seismic activity and risk. The larger the b value, the higher the proportion of small earthquake events, and the fewer the large earthquakes. Conversely, a decrease in the b value may indicate an increase in the probability of stress accumulation or large earthquakes in the region. The a value and the b value can determine the value of V4, which is the average annual occurrence rate of earthquakes with a magnitude of 4 or above in a seismic zone.

[0003] The existing scheme uses the maximum likelihood method to fit the magnitude and cumulative annual average occurrence rate, and uses a single indicator to evaluate the statistical model effect. The model parameters are adjusted according to the model effect to optimize the fitting effect of the relationship curve between the cumulative annual average occurrence rate and the magnitude.

[0004] Although the existing scheme can optimize the parameters, its evaluation system relies on a single indicator, which leads to a single adjustment direction of the parameters and makes it difficult to capture the nonlinear characteristics in complex seismic activity patterns. In addition, the statistical model is sensitive to initial parameters, and in areas with sparse or unevenly distributed data, it is easy to fall into a local optimal solution, which lacks robustness and affects the credibility of the fitting results. SUMMARY

[0005] The present application provides a calculation method and system of seismic activity parameters to solve the problem of poor accuracy of activity parameters caused by multiple factors in the prior art, which ultimately leads to low fitting accuracy.

[0006] In a first aspect, the present application provides a calculation method of seismic activity parameters, comprising:

[0007] obtaining a seismic statistical area;

[0008] fitting the magnitude and cumulative annual average occurrence rate of all seismic events in the seismic statistical area to obtain a first fitting result, and calculating the activity parameters in the fitting process to obtain a set of activity parameter optimization values, wherein the first fitting result is the relationship curve between the cumulative annual average occurrence rate and the magnitude of the initial fitting;

[0009] evaluating the first fitting result using multiple indicators to obtain a first fitting effect evaluation result, wherein the multiple indicators include linear correlation strength and mean square error dimension.

[0010] In a case where the linear correlation strength evaluation value in the first fitting effect evaluation result is less than a strength threshold value or the mean square error evaluation value is greater than an error threshold value, adjusting the value of the activity parameter according to the activity parameter optimization value by using multiple adjustment modes to obtain multiple sets of adjusted values of the activity parameter;

[0011] According to the second fitting effect evaluation results of the multiple second fitting results, each of which is a fitting result corresponding to a set of adjusted values of the activity parameter, selecting an optimal set of adjusted values of the activity parameter from the multiple sets of adjusted values of the activity parameter.

[0012] Optionally, fitting the magnitudes and the cumulative annual average occurrence rates of all seismic events in the seismic statistical region to obtain a first fitting result, and calculating the activity parameter during the fitting process to obtain a set of activity parameter optimization values, including:

[0013] According to the difference between the occurrence time of each seismic event in the seismic statistical region and the parameter calculation time, combining the historical seismic recurrence period, calculating the time weight of each seismic event by using an exponential decay function, and based on the kernel density estimation value of the region where the seismic event is located, combining the fault density and the crustal deformation rate parameter, calculating the spatial weight of each seismic event;

[0014] Fusing the time weight and the spatial weight of each seismic event according to a preset proportion to obtain a comprehensive weight of each seismic event;

[0015] According to the historical occurrence rate of the seismic events in each magnitude bin, calculating a historical statistical error, and adjusting the comprehensive weight of the data points corresponding to the seismic events in each magnitude bin according to the error to obtain an adjusted comprehensive weight;

[0016] Fitting all data points in the seismic statistical region by using a weighted least squares method, and in the fitting process, taking the adjusted comprehensive weight as a weighting coefficient, minimizing the weighted residual sum of squares of all data points, to obtain a set of activity parameter optimization values.

[0017] Optionally, fitting all data points in the seismic statistical region by using a weighted least squares method, and in the fitting process, taking the adjusted comprehensive weight as a weighting coefficient, minimizing the weighted residual sum of squares of all data points, to obtain a set of activity parameter optimization values, including:

[0018] Defining a linear relationship model between the magnitude and the cumulative annual average occurrence rate, the linear relationship model complying with the G-R law and including the activity parameter to be optimized;

[0019] Constructing a diagonal weight matrix, diagonal elements of the diagonal weight matrix being the adjusted comprehensive weights of the data points;

[0020] using the adjusted comprehensive weight as a weighting coefficient, using a weighted residual sum of squares calculation formula corresponding to the weighted least squares method to iteratively calculate the value of the activity parameter, and calculating the weighted residual sum of squares under the value of the activity parameter;

[0021] If the change amount of the weighted residual sum of squares is less than a preset change amount threshold or reaches a maximum number of iterations, the iteration is terminated, and the value of the activity parameter output in the last generation is taken as a set of activity parameter optimization values.

[0022] Optionally, the first fitting result is evaluated using multiple indicators to obtain a first fitting effect evaluation result, including:

[0023] calculating a Pearson correlation coefficient between the measured data of the seismic event and the first fitting result, and determining the Pearson correlation coefficient as a linear correlation strength evaluation value;

[0024] calculating the mean of the residual sum of squares of all data points, and taking the mean as a mean square error evaluation value;

[0025] combining the linear correlation strength evaluation value and the mean square error evaluation value to obtain the first fitting effect evaluation result.

[0026] Optionally, the multiple adjustment modes include a single-parameter adjustment mode and a double-parameter linkage adjustment mode.

[0027] The value of the activity parameter is adjusted according to the activity parameter optimization value using multiple adjustment modes to obtain multiple sets of adjusted values of the activity parameter, including:

[0028] Without adjusting the slope parameter of the G-R law in the activity parameter, a parameter V4 adjustment value corresponding to the G-R law is received through an interactive interface, a first adjusted value of the parameter V4 is generated, and the optimization value of the slope parameter and the first adjusted value of the parameter V4 are spliced as a first set of adjusted values of the activity parameter.

[0029] Without adjusting the parameter V4 corresponding to the G-R law in the activity parameter, an adjustment value of the slope parameter of the G-R law is received through an interactive interface, a first adjusted value of the slope parameter is generated, and the optimization value of the parameter V4 and the first adjusted value of the slope parameter are spliced as a second set of adjusted values of the activity parameter.

[0030] According to a preset association between the parameter V4 and the slope parameter, a second adjusted value of the parameter V4 and a second adjusted value of the slope parameter are determined to obtain a third set of adjusted values of the activity parameter.

[0031] The first set of adjusted activity parameter values, the second set of adjusted activity parameter values and the third set of adjusted activity parameter values are taken as a plurality of sets of adjusted activity parameter values.

[0032] Optionally, the obtaining the seismic statistical area comprises:

[0033] Obtaining a time segmentation file and a seismic parameter file corresponding to the seismic area name;

[0034] Parsing a time segmentation result from the time segmentation file and parsing occurrence time, magnitude and cumulative annual average occurrence rate of each seismic event from the seismic parameter file;

[0035] Determining a time period to which each seismic event belongs according to the occurrence time of each seismic event and the time segmentation result, and determining an identifier corresponding to each seismic event according to a preset corresponding relationship between a time period and an identifier;

[0036] Determining a position of each seismic event in the seismic statistical area according to the magnitude and the cumulative annual average occurrence rate of each seismic event, displaying a corresponding seismic event according to the identifier, and generating the seismic statistical area.

[0037] Optionally, after obtaining a plurality of second fitting results, the method further comprises:

[0038] Superimposing and displaying the relationship curve corresponding to the first fitting result and the relationship curves corresponding to the plurality of second fitting results on the seismic statistical area, and distinguishing the relationship curves by different colors or line types;

[0039] Receiving a click operation of a user on any relationship curve, and displaying an activity parameter optimization value corresponding to the relationship curve within a preset time length;

[0040] Generating a comprehensive report containing the activity parameter optimization value corresponding to the final relationship curve selected by the user and a fitting effect evaluation result according to the final relationship curve.

[0041] In a second aspect, the application provides a system for calculating seismic activity parameters, comprising:

[0042] An obtaining module for obtaining a seismic statistical area;

[0043] A fitting module for fitting the magnitude and the cumulative annual average occurrence rate of all seismic events in the seismic statistical area to obtain a first fitting result, and calculating activity parameters during the fitting process to obtain a set of activity parameter optimization values, wherein the first fitting result is a relationship curve of the cumulative annual average occurrence rate and the magnitude of the initial fitting;

[0044] an evaluation module configured to evaluate the first fitting result by using multiple indexes to obtain a first fitting effect evaluation result, wherein the multiple indexes include a linear correlation intensity and a mean square error dimension;

[0045] an adjustment module configured to, in a case where a linear correlation intensity evaluation value in the first fitting effect evaluation result is less than an intensity threshold value or a mean square error evaluation value is greater than an error threshold value, adjust a value of the activity parameter according to the activity parameter optimization value by using multiple adjustment modes to obtain multiple sets of adjusted values of the activity parameter;

[0046] a selection module configured to select an optimal set of adjusted values of the activity parameter from the multiple sets of adjusted values of the activity parameter according to second fitting effect evaluation results of multiple second fitting results, wherein each second fitting result is a fitting result corresponding to a set of adjusted values of the activity parameter.

[0047] In a third aspect, the present application provides a computing device including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the computing method of the seismic activity parameter according to any one of the first aspect.

[0048] In a fourth aspect, the present application provides a computer storage medium storing a computer program, wherein the computer program is executed by a computer to implement the computing method of the seismic activity parameter according to any one of the first aspect.

[0049] In the present application, a computing method of a seismic activity parameter is provided, which includes: obtaining a seismic statistical area; fitting magnitudes and cumulative annual average occurrence rates of all seismic events in the seismic statistical area to obtain a first fitting result, and calculating the activity parameter in the fitting process to obtain a set of activity parameter optimization values, wherein the first fitting result is a relationship curve between the cumulative annual average occurrence rate and the magnitude of the initial fitting; evaluating the first fitting result by using multiple indexes to obtain a first fitting effect evaluation result, wherein the multiple indexes include a linear correlation intensity and a mean square error dimension; in a case where a linear correlation intensity evaluation value in the first fitting effect evaluation result is less than an intensity threshold value or a mean square error evaluation value is greater than an error threshold value, adjusting a value of the activity parameter according to the activity parameter optimization value by using multiple adjustment modes to obtain multiple sets of adjusted values of the activity parameter; and selecting an optimal set of adjusted values of the activity parameter from the multiple sets of adjusted values of the activity parameter according to second fitting effect evaluation results of multiple second fitting results, wherein each second fitting result is a fitting result corresponding to a set of adjusted values of the activity parameter.

[0050] The application evaluates the initial fitting result by combining the linear correlation strength and the mean square error, breaks through the limitation of a single index, effectively quantifies the linear correlation degree and error distribution characteristics of the fitting curve, avoids the statistical model deviation caused by one-dimensional evaluation, and improves the accuracy of the magnitude-frequency relationship fitting. Based on the parameter values of the initial fitting optimization, multiple adjustment modes are used to generate multiple candidate parameter combinations, combined with the comprehensive evaluation of the secondary fitting result, the initial parameter sensitivity problem can be dynamically avoided, the local optimal convergence risk in the data sparse or uneven distribution area can be reduced, and the adaptability of the statistical model to the complex seismic activity pattern is improved. Through the iterative adjustment and multi-index screening mechanism, the dynamic calibration of the activity parameter is realized, so that the statistical model can flexibly adapt to the nonlinear characteristics of the seismic activity in different regions, and provide more practical quantitative basis for seismic risk assessment and disaster prediction. Further, based on the time-space heterogeneity characteristics of the seismic events, the time weight is calculated by using the exponential decay function combined with the historical recurrence period, and the spatial weight is constructed by fusing the kernel density estimation, fault density and crustal deformation rate, and the comprehensive weight is generated by a preset proportion; further, the comprehensive weight is dynamically corrected according to the historical statistical error of each magnitude interval, and finally the weighted least square method is used to fit the seismic event distribution data, so as to minimize the weighted residual sum of squares and optimize the activity parameter. The method effectively improves the representativeness of the data points by fusing the time-space double weight and dynamically correcting the error, and weakens the interference of the sparsity, uneven distribution and statistical deviation of the historical data on the fitting result; combined with the weighted least square optimization, the ability of the magnitude-frequency relationship curve to describe the complex seismic activity pattern is enhanced, and the robustness and regional adaptability of the activity parameter are improved, which provides more accurate statistical model support for seismic risk analysis.

[0051] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0053] Figure 1 A flowchart of a method for calculating a seismic activity parameter provided by an embodiment of the application;

[0054] Figure 2 A structural schematic diagram of a seismic activity parameter calculation system provided by an embodiment of the application;

[0055] Figure 3A structural schematic diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order for those skilled in the technical field to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order in which they appear in this text, and the serial numbers of the operations such as 11, 12, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. described herein are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Also, "first" and "second" are not of different types.

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0059] To solve the problem of poor calculation accuracy of activity parameters caused by multiple factors such as single evaluation dimension, high parameter sensitivity and local optimal convergence tendency in the prior art, which ultimately leads to low fitting accuracy, an embodiment of the present application provides a method for calculating seismic activity parameters. The method adopts the following concept: combining multiple evaluation indexes, the final value of the activity parameter can be quickly and accurately calculated under multiple adjustment modes, thereby improving the accuracy and reliability of the fitting of the relationship between magnitude and cumulative annual average occurrence rate.

[0060] Figure 1 A flowchart of a method for calculating seismic activity parameters provided by an embodiment of the present application is shown in FIG. 1, which comprises the following steps. Figure 1

[0061] S11, obtaining a seismic statistical area.

[0062] ​The seismic statistical region can be a spatiotemporal data set containing geological parameters such as spatial positions, magnitudes, occurrence times, fault densities, crustal deformation rates, cumulative annual average occurrence rates, and the like of all historical earthquake events in a target region, and is used to represent the spatiotemporal distribution characteristics of seismic activity through time period identification, position identification, or joint identification of time period and position. For example, the abscissa of the seismic statistical region can be the magnitude, and the ordinate can be the cumulative annual average occurrence rate. The earthquake events of the first spatial position in the first time period are represented by a red circle in the seismic statistical region, and the earthquake events of the first spatial position in the second time period are represented by a red rectangle in the seismic statistical region. The earthquake events of the second spatial position in the first time period are represented by a green circle in the seismic statistical region, and the earthquake events of the second spatial position in the second time period are represented by a green rectangle in the seismic statistical region. In the embodiments of the present application, the historical earthquake events can be referred to as earthquake events.

[0063] S12, fitting the magnitudes and cumulative annual average occurrence rates of all earthquake events in the seismic statistical region to obtain a first fitting result, and calculating activity parameters in the fitting process to obtain a set of optimized values of the activity parameters. The first fitting result is a relationship curve of the cumulative annual average occurrence rate and the magnitude of the initial fitting.

[0064] The initial fitting refers to preliminary modeling of the statistical relationship between the magnitude and the cumulative annual average occurrence rate in the seismic statistical region, calculating the activity parameters by minimizing the sum of squared residuals, and generating an initial fitting curve and parameter optimization values. The preset fitting algorithm used in step S12 in the embodiments of the present application includes least squares method, maximum likelihood estimation, Bayesian inference, and the like. The least squares method can be weighted least squares method. The cumulative annual average occurrence rate refers to the average number of earthquake events occurring per unit time (year) within a certain magnitude range. The first fitting result refers to the preliminary model output obtained by initially fitting the magnitude and the cumulative annual average occurrence rate data, including the relationship curve of the cumulative annual average occurrence rate and the magnitude of the initial fitting (or referred to as the initial fitting curve) and the corresponding optimized values of the activity parameters. The relationship curve described above is a curve describing the mathematical relationship between the magnitude and the cumulative annual average occurrence rate, which is usually a linear model.

[0065] For example, in the initial fitting process, the parameter optimization values include a b value of 0.93 and a V4 value of 2.5.

[0066] S13, evaluating the first fitting result by using multiple indexes to obtain a first fitting effect evaluation result, and the multiple indexes include linear correlation strength and mean square error dimension.

[0067] The multi-index evaluation is quantified by the linear correlation strength and the mean square error, which are used to evaluate the fitting degree of the initial fitting curve and the original data in step S13. The mean square error dimension is one of the error indicators used to evaluate the fitting result, which quantifies the average square deviation between the predicted value of the fitting curve and the actual observed value.

[0068] S14, in the case that the linear correlation strength evaluation value in the first fitting effect evaluation result is less than the strength threshold value or the mean square error evaluation value is greater than the error threshold value, adjusting the value of the activity parameter according to the activity parameter optimization value, using multiple adjustment modes to adjust the value of the activity parameter, to obtain multiple sets of adjusted values of the activity parameter.

[0069] The multi-mode parameter adjustment is based on the activity parameter optimization value corresponding to the initial fitting curve, and uses a perturbation method, a genetic algorithm or a gradient descent strategy to generate multiple sets of candidate parameter combinations to explore a more optimal solution space. The present application embodiment can provide the following three adjustment modes, which can be selected by the user or combined, for example: adjusting the b value alone to dynamically update the fitting curve and the evaluation result; adjusting the V4 value alone to dynamically update the fitting curve and the evaluation result; adjusting the b value and the V4 value simultaneously to dynamically update the fitting curve and the evaluation result.

[0070] S15, according to the second fitting effect evaluation results of the multiple second fitting results, selecting the optimal set of adjusted values of the activity parameter from the multiple sets of adjusted values of the activity parameter, each second fitting result being a fitting result corresponding to a set of adjusted values of the activity parameter.

[0071] The second fitting result is a new model output obtained by re-fitting the magnitude-cumulative annual average occurrence rate data using a set of adjusted values of the activity parameter (such as b=0.78, V4=2.5). The selection means that through the comprehensive evaluation of the second fitting result, the optimal solution is selected from the multiple sets of candidate parameter values, ensuring that the statistical model is balanced and optimal under multiple indexes.

[0072] The following is a specific example:

[0073] For the earthquake events with magnitude M≥4.0 in a volcanic zone from 2000 to 2020, in order to optimize the relationship curve (referred to as V-M curve) between the cumulative annual average occurrence rate and the magnitude, the following process is executed by the embodiments of the present application: obtaining a time segmentation file and a seismic parameter file, wherein the time segmentation file can be divided into four time periods: 2000-2005, 2006-2010, 2011-2015, and 2016-2020; and the seismic parameter file records the cumulative annual average occurrence rate of different magnitude grades in each time period. For the sake of simple description, the following is described by taking two time periods 2000-2005 and 2006-2010 as examples: the first sub-region Zone A of the volcanic zone is represented by red, the second sub-region Zone B is represented by green, the time period 2000-2005 corresponds to a circle, and the time period 2006-2020 corresponds to a rectangle. The seismic statistical area is visualized, the weighted least squares method is used for fitting, and after the initial fitting is completed, the b value in the corresponding set of activity parameter optimization values is 0.93, and V4 is 2.5. The formula corresponding to the initial V-M curve is In(v)=5.2-0.93M, but the linear correlation intensity evaluation value R=0.82 is less than the intensity threshold 0.85, and the mean square error MSE=0.48 does not exceed the error threshold 0.5; in the case of adjusting the b value alone, the embodiments of the present application can generate b=0.95 (R=0.87, MSE=0.49) through the genetic algorithm, the embodiments of the present application can generate two sets of candidate parameters for the case of adjusting the b value alone; in the case of adjusting V4 alone, V4=2.7 is generated, and in the case of double-parameter linkage adjustment, b=0.91 and V4=2.6 are obtained. The adjusted values of the multiple sets of activity parameters are [b=0.95, V4=2.5], [b=0.93, V4=2.7], and [b=0.91, V4=2.6]. The second fitting effect evaluation results corresponding to the first group are R=0.87 and MSE=0.49, the second fitting effect evaluation results corresponding to the second group are R=0.83 and MSE=0.45, and the second fitting effect evaluation results corresponding to the third group are R=0.89 and MSE=0.42. Finally, [b=0.91, V4=2.6] is selected as the adjusted value of the optimal set of activity parameters.

[0074] By executing S11-S15, the embodiments of the present application improve the robustness of the relationship curve between the cumulative annual average occurrence rate and the magnitude through multi-dimensional evaluation and multi-mode parameter dynamic adjustment, solve the one-sidedness of single index evaluation and the problem of local optimal convergence; combined with the weighted optimization mechanism, the activity parameters are more in line with the regional seismic activity characteristics, and high-credibility quantitative support is provided for seismic risk analysis.

[0075] In one possible embodiment, S12, the magnitudes and cumulative annual average occurrence rates of all seismic events in the seismic statistical region are fitted to obtain a first fitting result, and the calculation of the activity parameters is performed during the fitting process to obtain a set of activity parameter optimization values, including:

[0076] Step 121, according to the difference between the occurrence time of each seismic event in the seismic statistical region and the parameter calculation time, combining the recurrence period of historical earthquakes, using an exponential decay function to calculate the time weight of each seismic event, and based on the kernel density estimation value of the region where the seismic event is located, and combining the fault density and crustal deformation rate parameters, the spatial weight of each seismic event is calculated.

[0077] Wherein, the time weight is a quantitative index reflecting the influence of the time sequence of the seismic event on the current activity, which is calculated based on the decay relationship between the time difference from now and the historical recurrence period. The spatial weight is a comprehensive index representing the spatial aggregation and tectonic activity of the seismic event, which is generated by the fusion of the kernel density estimation value, the fault density and the crustal deformation rate. The historical earthquake recurrence period is the average time interval of the repeated occurrence of seismic events of the same magnitude range in a certain region or fault zone. The exponential decay function is a mathematical function that decays with time or distance in an exponential form, which is used to quantify the degree of weakening of the influence of the seismic event on the current activity over time. The formula of the exponential decay function is: the time weight W(t) of the seismic event = e -λ·(t / T) Wherein, t is the time of the seismic event from now, T is the recurrence period, and λ is the decay coefficient for controlling the decay rate. The kernel density estimation value is the spatial aggregation density of the seismic event calculated based on the kernel function (such as Gaussian kernel), which reflects the spatial concentration degree of seismic activity around a certain position. The fault density is the total length or area ratio of the fault per unit area, which represents the strength of regional tectonic activity. The crustal deformation rate parameter is the crustal horizontal / vertical movement rate obtained by global positioning system (GPS) or interferometric synthetic aperture radar (InSAR) observation, which reflects the tectonic stress accumulation speed.

[0078] Step 122, the time weight and the spatial weight of each seismic event are fused according to a preset proportion to obtain a comprehensive weight of each seismic event.

[0079] Wherein, the comprehensive weight is a composite index of the spatio-temporal weight fused by a preset proportion (such as 40% of the time weight and 60% of the space weight), which is used to represent the relative importance of the seismic event in the fitting.

[0080] Step 123, according to the historical occurrence rate of the earthquake events in each magnitude bin, calculate the historical statistical error, adjust the comprehensive weight of the data points corresponding to the earthquake events in each magnitude bin according to the error, and obtain the adjusted comprehensive weight.

[0081] wherein the adjusted comprehensive weight is the final weight fused with the spatio-temporal weight and corrected by the historical statistical error, and is used for the priority allocation of the data points in the weighted fitting. For example, the error is 1%, and the adjusted comprehensive weight is 0.99.

[0082] Step 124, use the weighted least squares method to fit all the data points in the seismic statistical area, and in the fitting process, take the adjusted comprehensive weight as the weighted coefficient to minimize the weighted residual sum of squares of all the data points, so as to obtain a set of optimized values of the activity parameters.

[0083] wherein the weighted least squares method is a regression algorithm considering the weight of the data points, and the model parameters are solved by minimizing the weighted residual sum of squares. The formula of the weighted least squares method is: wherein w i is the weight of the i th data point, a is the intercept parameter, used for representing the level of seismic activity, and b is the slope parameter. In the initial fitting process, a and b are used together to determine the value of V4, so in the embodiments of the present application, b and the value of V4 can be the parameters to be optimized, is the summation symbol, indicating the calculation results from the 1 st to the n th data points are accumulated, y i is the dependent variable observation value, indicating the actual observation result of the i th data point. x i is the independent variable observation value, indicating the input feature of the i th data point. The weighted residual sum of squares is the sum of the squares of the residuals between the predicted values of the model after weighting and the actual observation values, and is used for quantifying the fitting accuracy.

[0084] The following is a specific example:

[0085] For a certain island in a certain region, the magnitude file M≥4 earthquake event from 2000 to 2020, one of which occurred in 2010, if the current time is 2020, then 10 years ago, the time weight of the earthquake event is Wtime=0.916, the recurrence period of the historical earthquake in the region is T=50, the attenuation coefficient is a=0.44, and the recent event (such as 2015, At=5 years) has a higher weight Wtime=0.956, and the historical event (such as 2000, At=20 years) has a lower weight Wtime=0.827). The spatial weight of the above earthquake event is Wspace=0.6, then Wtotal=0.4x0.8+0.6x0.6=0.68; the historical error calculated by the historical occurrence rate of the earthquake event in the magnitude file is Ei=0.2, and the adjusted weight is Wadjusted=0.68x0.8=0.54. After weighted fitting, a set of activity parameter optimization values b=0.93, V4=2.5 are obtained.

[0086] By performing steps 121-124, the embodiment of the application reduces the interference of the uneven distribution of historical data in space and time by spatio-temporal weight fusion, and combines the statistical error dynamic correction mechanism to suppress the influence of abnormal fluctuations within the magnitude file. Weighted least squares fitting is used to improve the accuracy of activity parameters in describing the characteristics of regional seismic activity, especially suitable for seismic risk assessment in complex tectonic regions such as plate boundaries.

[0087] In one possible embodiment, step 124 uses the weighted least squares method to fit all data points in the seismic statistical region, and in the fitting process, the adjusted comprehensive weight is used as the weighting coefficient to minimize the weighted residual sum of squares of all data points to obtain a set of activity parameter optimization values, including:

[0088] Step a1, define a linear relationship model between magnitude and cumulative annual average occurrence rate, and the linear relationship model conforms to the G-R law and includes activity parameters to be optimized.

[0089] Wherein, the G-R law is to express Gutenberg and Richter relationship as a linear equation: log 10 N=a-bM, where a is the intercept parameter, used to represent the seismic activity level, b is the slope parameter, used to reflect the proportion of large and small earthquakes, and a and b are activity parameters to be optimized, M is the coefficient, and a and b are used to determine V4 value in the initial fitting process. This method is the prior art, which will not be described here.

[0090] Step a2, construct a diagonal weight matrix, and the diagonal elements of the diagonal weight matrix are the adjusted comprehensive weights of the data points.

[0091] Wherein, the diagonal weight matrix is an n x n matrix, only the diagonal elements are non-zero, the value is the adjusted comprehensive weight of each data point, and the non-diagonal elements are 0, which is used for matrix operation of weighted least squares method.

[0092] Step a3, using the adjusted comprehensive weight as the weighting coefficient, the weighted residual sum of squares calculation formula of the weighted least squares method is used to iteratively calculate the value of the activity parameter, and the weighted residual sum of squares under the value of the activity parameter is calculated.

[0093] Wherein, the weighted residual sum of squares is the deviation sum of squares of the model prediction value and the actual observation value with the weight matrix as the coefficient, which is used as the objective function of parameter optimization.

[0094] Step a4, if the change amount of the weighted residual sum of squares is less than the preset change amount threshold or reaches the maximum iteration number, the iteration is terminated, and the value of the activity parameter output in the last generation is taken as a set of activity parameter optimization values.

[0095] Wherein, the change amount threshold is a preset upper limit of the residual sum of squares change amount, which is used to judge convergence.

[0096] The following is a specific example:

[0097] In order to realize the optimization of the activity parameter of a mountain seismic belt, the earthquake data of a mountain with magnitude M≥4.0 from 1990 to 2020 is selected, the initial parameters a=6.2, b=1.1, V4=2.5; the comprehensive weight W=[0.7, 0.5,..., 0.8], n=150 data points, a 150x150 diagonal matrix is constructed; the first iteration: the weighted residual sum of squares WRSS(1)=0.25, b=1.08, V4=2.4; the fifth iteration: WRSS(5)=0.12, b=1.03, V4=2.5; the eighth iteration: WRSS(8)=0.119, b=1.03, V4=2.0, the change amount ΔWRSS=0.0005, which meets the convergence condition; the final optimization value b=1.03, V4=2.0, which is more consistent with the active trend of recent small earthquakes than the traditional method.

[0098] By performing steps a1-a4, the embodiment of the application realizes efficient convergence of the activity parameter by constructing a diagonal weight matrix to accurately quantify the priority of data points, combined with the closed solution iteration optimization of the weighted least squares method; the termination condition is dynamically determined by the residual change amount, which avoids overfitting and invalid calculation. The application in a mountainous area with complex structure shows that the b value error is reduced by 18%, the weighted residual sum of squares is reduced by 42%, and the description accuracy and calculation efficiency of the seismic risk assessment model for regional activity characteristics are improved.

[0099] In one possible implementation, S13 evaluates the first fitting result using multiple indicators to obtain a first fitting effect evaluation result, including:

[0100] Step 131: Calculate the Pearson correlation coefficient between the measured data of the seismic event and the first fitting result, and determine the Pearson correlation coefficient as the linear correlation strength evaluation value.

[0101] The linear correlation strength evaluation value is the linear correlation degree between the measured data and the fitting curve quantified by the Pearson correlation coefficient, and the value range is [-1, 1], and the closer to 1 indicates the stronger linear relationship. For example, in the initial fitting process, the linear correlation strength evaluation value is the linear correlation degree between the measured data and the initial fitting curve, and R = 0.82.

[0102] Step 132: Calculate the mean of the residual sum of squares of all data points, and determine the mean as the mean square error evaluation value.

[0103] The mean square error evaluation value is the mean of the residual sum of squares of the predicted value and the measured value of all data points, which quantifies the overall prediction error of the model.

[0104] Step 133: Combine the linear correlation strength evaluation value and the mean square error evaluation value to obtain the first fitting effect evaluation result.

[0105] The first fitting effect evaluation result is a two-dimensional quantitative conclusion combining the linear correlation strength and the mean square error, which is used to determine whether the model needs to be further optimized.

[0106] The following is a specific example:

[0107] The fault zone model evaluation of a certain region is selected, and the fault zone M≥4.0 earthquake events in 1980-2020 in the region are selected, and the G-R relationship is obtained by initial fitting, b = 1.05. The linear correlation strength R = 0.82 (less than the threshold value 0.85) is calculated, indicating that the magnitude-occurrence rate linear trend is weak. The mean square error MSE = 0.12 is calculated, showing that the model prediction error is large. It is determined that the linear correlation strength and the mean square error are both substandard, triggering the parameter adjustment process, and the optimized parameter b = 0.98 is adjusted. The secondary evaluation R = 0.88 and MSE = 0.08 meet the requirements.

[0108] By performing steps 131-133, the embodiments of the present application evaluate the model through the Pearson correlation coefficient and the mean square error in two dimensions, overcome the one-sidedness of the traditional single indicator, accurately identify the insufficient magnitude interval of fitting, and in the practical application of the San Andreas fault zone, the model error is reduced by 33% after parameter optimization, the linear correlation is improved by 7%, and the credibility and disaster warning efficiency of the seismic activity model are enhanced.

[0109] In a possible embodiment, the plurality of adjustment modes includes a single-parameter adjustment mode and a double-parameter linkage adjustment mode.

[0110] According to the activity parameter optimization value, the numerical value of the activity parameter is adjusted by using the plurality of adjustment modes, and a plurality of sets of adjusted numerical values of the activity parameter are obtained, including:

[0111] In step 141, without adjusting the slope parameter of the G-R law in the activity parameter, the adjustment value of the parameter V4 corresponding to the G-R law is received through the interactive interface, the first adjusted numerical value of the parameter V4 is generated, and the optimization value of the slope parameter and the first adjusted numerical value of the parameter V4 are spliced into the first set of adjusted numerical values of the activity parameter.

[0112] The first adjusted numerical value of the parameter V4 refers to the numerical value generated by directly inputting the adjustment value of the parameter V4 through the interactive interface under the premise of keeping the slope parameter b of the G-R law unchanged. The first set of adjusted numerical values of the activity parameter is spliced from the first adjusted numerical value of the parameter V4 and the original value of the unadjusted slope parameter, that is, a single-variable combination of only adjusting the parameter V4.

[0113] In step 142, without adjusting the parameter V4 corresponding to the G-R law in the activity parameter, the adjustment value of the slope parameter of the G-R law is received through the interactive interface, the first adjusted numerical value of the slope parameter is generated, and the optimization value of the intercept parameter and the first adjusted numerical value of the slope parameter are spliced into the second set of adjusted numerical values of the activity parameter.

[0114] The first adjusted numerical value of the slope parameter refers to the numerical value generated by directly inputting the adjustment value of the slope parameter b through the interactive interface under the premise of keeping the parameter V4 of the G-R law unchanged. The second set of adjusted numerical values of the activity parameter is spliced from the first adjusted numerical value of the slope parameter and the original value of the unadjusted parameter V4, that is, a single-variable combination of only adjusting the slope parameter.

[0115] In step 143, according to the preset correlation between the parameter V4 and the slope parameter, the second adjusted numerical value of the parameter V4 and the second adjusted numerical value of the slope parameter are determined to obtain the third set of adjusted numerical values of the activity parameter.

[0116] The correlation is the correlation between the intercept a, the slope b and the parameter V4 based on historical data statistics or a physical model.

[0117] In step 144, the first set of adjusted numerical values of the activity parameter, the second set of adjusted numerical values of the activity parameter and the third set of adjusted numerical values of the activity parameter are taken as the plurality of sets of adjusted numerical values of the activity parameter.

[0118] Wherein, the multiple sets of adjustment values are all candidate parameter sets generated by integrating manual adjustment and correlation adjustment, and are used for subsequent optimization.

[0119] The following is a specific example: the initial fitting obtains a b value of 0.93 and a V4 value of 2.5, corresponding to R = 0.82, which is less than the intensity threshold 0.85, and the mean square error MSE = 0.48, which does not exceed the error threshold 0.5. Three adjustment modes are used to adjust the numerical value, the first group is b = 0.95, V4 = 2.5, the second fitting effect evaluation result corresponding to the first group is R = 0.87, MSE = 0.49, the second group is b = 0.93, V4 = 2.7, the second fitting effect evaluation result corresponding to the second group is R = 0.83, MSE = 0.45, and the third group is b = 0.91, V4 = 2.6, the second fitting effect evaluation result corresponding to the first group is R = 0.89, MSE = 0.42.

[0120] By performing steps 141-144, the embodiments of the present application generate multiple sets of differentiated parameter combinations by combining manual interaction adjustment and automatic correlation derivation, breaking through the limitations of single algorithm adjustment; in the application of sparse data area, the b value error is reduced by 22%, and the coverage rate of the model to the historical earthquake sequence is improved, providing a more flexible and reliable parameter optimization scheme for seismic hazard analysis in complex structural areas.

[0121] In one possible embodiment, S11, a seismic statistical area is acquired, comprising:

[0122] Step 111, acquiring a time segmentation file and a seismic parameter file corresponding to the name of the seismic area.

[0123] Wherein, the time segmentation file is a structured file recording the staging rules of historical seismic activity in the target area, including time period start and end time, segmentation basis and other metadata. The seismic parameter file is an attribute data table storing all seismic events in the region, including occurrence time, latitude and longitude, magnitude, cumulative annual average occurrence rate and other fields.

[0124] Step 112, parsing the time segmentation result from the time segmentation file, and parsing the occurrence time, magnitude and cumulative annual average occurrence rate of each seismic event from the seismic parameter file.

[0125] Wherein, the time segmentation result is to divide the historical seismic activity into multiple continuous time periods, which is used for subsequent spatio-temporal pattern analysis. The seismic event attribute is the structured data of a single seismic event parsed, including occurrence time, magnitude, cumulative annual average occurrence rate, etc.

[0126] Step 113, according to the occurrence time of each seismic event and the time segmentation result, determine the time period to which each seismic event belongs, and according to the preset correspondence between the time period and the identifier, determine the identifier corresponding to each seismic event.

[0127] Wherein, the time period identifier is a visual mark given to different time period seismic events based on a preset rule.

[0128] Step 114, according to the magnitude and cumulative annual occurrence rate of each seismic event, determine the position of each seismic event in the seismic statistical area, display the corresponding seismic event according to the identifier, and generate the seismic statistical area.

[0129] Wherein, the seismic event position is a multidimensional data point with latitude and longitude as coordinates, magnitude as point size, and cumulative annual occurrence rate as color depth.

[0130] The following is a specific example:

[0131] The distribution map of the eastern seismic belt of a certain region is generated, the region name is "the eastern seismic belt of a certain region", the time segmentation file is divided into 1900-2020 at an interval of 20 years, and the seismic parameter file contains 1200 M≥4.0 seismic events, including time, magnitude, occurrence rate and the like. Time segmentation result: 6 time periods; Analyze seismic events: such as the Hualien M6.2 earthquake on November 15, 1986, with an occurrence rate of 0.05 times / year. The 1986 event is classified into the 1981-2000 period, and the identifier is "orange square"; the 2018 event is classified into the 2001-2020 period, and the identifier is "red dot". The dense orange square (mid-strength earthquake in the 1980s-2000s) along the eastern coast spreads northward in recent years; through the distribution map, it can be known that the nuclear density value near Hualien is the highest, reflecting the long-term active characteristics.

[0132] By performing steps 111-114, the present application realizes the multiscale visualization of the spatio-temporal distribution of seismic events through time segmentation and multidimensional identifier mapping; in the application of the eastern seismic belt of a certain region, the spatio-temporal evolution pattern of the mid-strength earthquake aggregation along the coast in the late 20th century and the northward diffusion in the 21st century is clearly revealed, which assists in identifying potential risk faults and provides intuitive and efficient decision support for regional seismic risk assessment.

[0133] In one possible embodiment, S15, after obtaining the plurality of second fitting results, the method further comprises:

[0134] Step 151, superimpose and display the relationship curve corresponding to the first fitting result and the relationship curves respectively corresponding to the plurality of second fitting results on the seismic statistical area, and distinguish the relationship curves by different colors or line types.

[0135] The first fitting result refers to a magnitude-frequency relationship curve generated based on initial G-R law parameters.

[0136] Step 152, receiving a click operation of a user on any relationship curve, and displaying a corresponding activity parameter optimization value within a preset time length.

[0137] The user click operation refers to selecting a curve through a mouse or a touch screen; and the preset time length is a floating display window of 500 ms to 2 s, which is used to display the activity parameter corresponding to the curve.

[0138] Step 153, generating a comprehensive report containing the activity parameter optimization value corresponding to the final relationship curve and the corresponding fitting effect evaluation result according to the final relationship curve selected by the user.

[0139] The final relationship curve is a fitting curve corresponding to the optimization parameter selected by the user; and the comprehensive report contains the parameter optimization value, the fitting effect (such as error analysis), and the comparison result with historical earthquake data.

[0140] The following is a specific example:

[0141] To realize the optimization of the activity parameter of a seismic region, a certain seismic research institution needs to optimize the G-R law parameters of a certain fault zone to improve the prediction accuracy of aftershocks. The initial parameters are b = 0.93 and V4 = 2.5, and the fitting curve shows that the high-magnitude event fitting deviation is large. The user adjusts the intercept to V4 = 2.7 through the interactive interface, and keeps the slope at b = 0.93 to generate a second fitting curve. The superimposed display shows that the new curve is more consistent with the distribution density of M≥4.0 earthquakes. The user clicks the blue curve, and the floating window displays R = 0.83 and MSE = 0.45. A report is generated, and through the report, it can be known that the optimized parameters reduce the prediction error by 52%, and the new model is verified to have no difference with the observed data distribution.

[0142] By performing steps 111 to 114, the embodiment of the present application intuitively presents the fitting differences of different parameters on the seismic law through multi-fitting curve superposition, interactive parameter display, and automatic report generation, assists the user to quickly locate the optimal solution, and dynamically feedbacks and statistically evaluates the decision support to reduce the manual trial and error cost. The full-link automation from parameter adjustment to report output in the closed loop improves the iteration efficiency of the seismic prediction model.

[0143] Figure 2 A structural schematic diagram of a seismic activity parameter calculation system provided by the embodiment of the present application is shown in FIG. 1. Figure 2 The system includes the following modules:

[0144] The acquisition module 21 is configured to acquire a seismic statistical region.

[0145] The fitting module 22 is configured to fit magnitudes and cumulative annual average occurrence rates of all seismic events in a seismic statistical region to obtain a first fitting result, and to calculate activity parameters during the fitting to obtain a set of optimized values of the activity parameters, the first fitting result being a first fitting curve of the cumulative annual average occurrence rates and the magnitudes.

[0146] The evaluation module 23 is configured to evaluate the first fitting result by using multiple indexes to obtain a first fitting effect evaluation result, the multiple indexes including a linear correlation strength and a mean square error dimension.

[0147] The adjustment module 24 is configured to, in a case where the linear correlation strength evaluation value in the first fitting effect evaluation result is less than a strength threshold value or the mean square error evaluation value is greater than an error threshold value, adjust values of the activity parameters according to the optimized values of the activity parameters by using multiple adjustment modes to obtain multiple sets of adjusted values of the activity parameters.

[0148] The selection module 25 is configured to select, according to second fitting effect evaluation results of multiple second fitting results, an optimal set of adjusted values of the activity parameters from the multiple sets of adjusted values of the activity parameters, each second fitting result being a fitting result corresponding to a set of adjusted values of the activity parameters.

[0149] Figure 2 The seismic activity parameter calculation system can perform Figure 1 The implementation principle and technical effects of the seismic activity parameter calculation method of the embodiments are not described again. The specific operation modes of each module and unit in the seismic activity parameter calculation system of the embodiments have been described in detail in the embodiments of the method, and will not be described again.

[0150] In a possible design, Figure 2 The seismic activity parameter calculation system of the embodiments can be implemented as a computing device, such as a server. Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32.

[0151] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0152] The processing component 32 is configured to: acquire a seismic statistical area; fit magnitudes and cumulative annual average occurrence rates of all seismic events in the seismic statistical area to obtain a first fitting result, and calculate activity parameters in the fitting process to obtain a set of optimized values of the activity parameters, the first fitting result being a first fitting curve of the cumulative annual average occurrence rate and the magnitude; evaluate the first fitting result by using multiple indexes to obtain a first fitting effect evaluation result, the multiple indexes including a linear correlation strength and a mean square error dimension; in a case where a linear correlation strength evaluation value in the first fitting effect evaluation result is less than a strength threshold value or a mean square error evaluation value is greater than an error threshold value, adjusting values of the activity parameters by using multiple adjustment modes according to the optimized values of the activity parameters to obtain multiple sets of adjusted values of the activity parameters; and selecting an optimal set of adjusted values of the activity parameters from the multiple sets of adjusted values of the activity parameters according to second fitting effect evaluation results of multiple second fitting results, each second fitting result being a fitting result corresponding to a set of adjusted values of the activity parameters.

[0153] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements for executing the above method.

[0154] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as a random access memory (RAM), a static random access memory (SRAM), an electrically erasable programmable read only memory (EEPROM), an erasable programmable read only memory (EPROM), a programmable read only memory (PROM), a read only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or a optical disk.

[0155] Of course, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

[0156] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0157] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0158] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the processing component, the storage component, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0159] The embodiments of the present application also provide a computer storage medium storing a computer program, and the computer program can implement the above-mentioned Figure 1 The method for calculating a seismic activity parameter according to the embodiments shown.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0161] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0162] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0163] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calculating seismic activity parameters, characterized in that, include: Obtain earthquake statistical zones; The magnitude and cumulative annual average occurrence rate of all earthquake events in the seismic statistical area are fitted to obtain the first fitting result. During the fitting process, the activity parameters are calculated to obtain a set of optimized values ​​of the activity parameters. The first fitting result is the relationship curve between the cumulative annual average occurrence rate and the magnitude of the initial fitting. The first fitting result is evaluated using multiple indicators to obtain the first fitting effect evaluation result. The multiple indicators include linear correlation strength and mean square error dimension. If the linear correlation strength evaluation value in the first fitting effect evaluation result is less than the strength threshold or the mean square error evaluation value is greater than the error threshold, the value of the activity parameter is adjusted using multiple adjustment modes according to the optimized value of the activity parameter to obtain multiple sets of adjusted values ​​of the activity parameter. Based on the evaluation results of the second fitting effect of multiple second fitting results, the optimal set of activity parameter adjusted values ​​is selected from multiple sets of activity parameter adjusted values, and each second fitting result is the fitting result corresponding to a set of activity parameter adjusted values; The magnitude and cumulative annual average occurrence rate of all earthquake events in the seismic statistical area are fitted to obtain the first fitting result. During the fitting process, activity parameters are calculated to obtain a set of optimized activity parameter values, including: The time weight of each earthquake event is calculated by taking into account the difference in occurrence time and parameters of each earthquake event in the seismic statistical area, combined with the historical earthquake recurrence cycle, using the exponential decay function. Based on the kernel density estimate of the area where the earthquake event is located, and combined with the fault density and crustal deformation rate parameters, the spatial weight of each earthquake event is calculated. The temporal and spatial weights of each earthquake event are combined according to a preset ratio to obtain the comprehensive weight of each earthquake event; Based on the historical occurrence rate of earthquake events within each magnitude range, the historical statistical error is calculated, and the comprehensive weight of the data points corresponding to earthquake events within each magnitude range is adjusted according to the error to obtain the adjusted comprehensive weight. The weighted least squares method is used to fit all data points in the seismic statistical area. During the fitting process, the weighted sum of squared residuals of all data points is minimized using the adjusted comprehensive weights as weighting coefficients to obtain a set of optimized values ​​for activity parameters. The weighted least squares method is used to fit all data points in the seismic statistical zone. During the fitting process, the adjusted comprehensive weights are used as weighting coefficients to minimize the weighted sum of squared residuals of all data points, thereby obtaining a set of optimized values ​​for activity parameters, including: A linear relationship model is defined between earthquake magnitude and cumulative annual average occurrence rate. The linear relationship model conforms to the GR law and includes the activity parameters to be optimized. Construct a diagonal weight matrix, where the diagonal elements of the diagonal weight matrix are the adjusted comprehensive weights of each data point; Using the adjusted comprehensive weights as weighting coefficients, the weighted residual sum of squares corresponding to the weighted least squares method is used to iteratively calculate the value of the activity parameter and calculate the weighted residual sum of squares under the value of the activity parameter. If the change in the weighted sum of squared residuals is less than the preset change threshold or the maximum number of iterations is reached, the iteration is terminated, and the values ​​of the activity parameters output by the last generation are used as a set of optimized activity parameter values. The evaluation of the first fitting result using multiple indicators to obtain the first fitting effect evaluation result includes: Calculate the Pearson correlation coefficient between the measured data of the earthquake event and the first fitting result, and determine the Pearson correlation coefficient as the linear correlation strength assessment value; Calculate the mean of the sum of squared residuals for all data points, and use the mean as the mean squared error assessment value; The linear correlation strength assessment value and the mean square error assessment value are combined to obtain the first fitting effect assessment result.

2. The method according to claim 1, characterized in that, The multiple adjustment modes include single-parameter adjustment mode and dual-parameter linkage adjustment mode; The step of adjusting the activity parameter value using multiple adjustment modes based on the optimized activity parameter value to obtain multiple sets of adjusted activity parameter values ​​includes: Without adjusting the slope parameter of the GR law in the activity parameters, the adjustment value of parameter V4 corresponding to the GR law is received by the user through the interactive interface, the first value after adjusting parameter V4 is generated, and the optimized value of the slope parameter and the first value after adjusting parameter V4 are concatenated to form the first set of activity parameter adjustment values. Without adjusting parameter V4 corresponding to the GR law in the activity parameters, the user inputs the adjustment value of the slope parameter of the GR law through the interactive interface, generates the first value after the slope parameter is adjusted, and concatenates the optimized value of parameter V4 and the first value after the slope parameter is adjusted to form the second set of activity parameter adjusted values. Based on the preset relationship between parameter V4 and slope parameter, determine the second value of parameter V4 after adjustment and the second value of slope parameter after adjustment, so as to obtain the third set of values ​​of activity parameter after adjustment. The adjusted values ​​of the first group of activity parameters, the second group of activity parameters, and the third group of activity parameters are taken as the adjusted values ​​of multiple groups of activity parameters.

3. The method according to claim 1, characterized in that, The acquisition of seismic statistical zones includes: Obtain the time segment file and earthquake parameter file corresponding to the name of the earthquake statistical zone; The time segmentation results are parsed from the time segmentation file, and the occurrence time, magnitude, and cumulative annual average occurrence rate of each earthquake event are parsed from the earthquake parameter file. Based on the occurrence time of each earthquake event and the time segmentation results, the time period to which each earthquake event belongs is determined, and based on the preset correspondence between time periods and identifiers, the identifier corresponding to each earthquake event is determined; Based on the magnitude and cumulative annual average occurrence rate of each earthquake event, the location of each earthquake event in the seismic statistics zone is determined, and the corresponding earthquake events are displayed according to the identifiers to generate the seismic statistics zone.

4. The method according to claim 1, characterized in that, After obtaining multiple second-fit results, the method further includes: The relationship curves corresponding to the first fitting result and the relationship curves corresponding to the multiple second fitting results are superimposed and displayed on the seismic statistics area, and each relationship curve is distinguished by different colors or line types. Receive user clicks on any relationship curve and display the corresponding optimized activity parameter values ​​within a preset time period; Based on the final relationship curve selected by the user, a comprehensive report is generated that includes the optimized values ​​of the activity parameters corresponding to the final relationship curve and the corresponding fitting effect evaluation results.

5. A system for calculating seismic activity parameters, characterized in that, A method for performing a calculation of a seismic activity parameter as described in any one of claims 1 to 4, comprising: The acquisition module is used to obtain seismic statistical zones; The fitting module is used to fit the magnitude and cumulative annual average occurrence rate of all earthquake events in the seismic statistical area to obtain the first fitting result. During the fitting process, the activity parameters are calculated to obtain a set of optimized values ​​of the activity parameters. The first fitting result is the relationship curve between the cumulative annual average occurrence rate and the magnitude of the initial fitting. An evaluation module is used to evaluate the first fitting result using multiple indicators to obtain a first fitting effect evaluation result. The multiple indicators include linear correlation strength and mean square error dimension. The adjustment module is used to adjust the value of the activity parameter according to the optimized value of the activity parameter by using multiple adjustment modes when the linear correlation strength evaluation value in the first fitting effect evaluation result is less than the strength threshold or the mean square error evaluation value is greater than the error threshold, thereby obtaining multiple sets of adjusted values ​​of the activity parameter. The selection module is used to select the optimal set of activity parameter adjusted values ​​from multiple sets of activity parameter adjusted values ​​based on the second fitting effect evaluation results of multiple second fitting results. Each second fitting result is the fitting result corresponding to a set of activity parameter adjusted values.

6. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for calculating seismic activity parameters as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for calculating seismic activity parameters as described in any one of claims 1 to 4.

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