CN111382908B - Earthquake random event set simulation method considering correlation of large earthquake time - Google Patents
Earthquake random event set simulation method considering correlation of large earthquake time Download PDFInfo
- Publication number
- CN111382908B CN111382908B CN202010166378.4A CN202010166378A CN111382908B CN 111382908 B CN111382908 B CN 111382908B CN 202010166378 A CN202010166378 A CN 202010166378A CN 111382908 B CN111382908 B CN 111382908B
- Authority
- CN
- China
- Prior art keywords
- earthquake
- seismic
- magnitude
- time
- potential
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 101
- 238000004088 simulation Methods 0.000 title claims abstract description 22
- 238000009826 distribution Methods 0.000 claims abstract description 67
- 230000000694 effects Effects 0.000 claims abstract description 24
- 230000036962 time dependent Effects 0.000 claims abstract description 14
- 238000005315 distribution function Methods 0.000 claims description 21
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 14
- 238000005070 sampling Methods 0.000 claims description 8
- 238000009827 uniform distribution Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 9
- 238000012502 risk assessment Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000011081 inoculation Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 208000035999 Recurrence Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000005293 physical law Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Computer Security & Cryptography (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application relates to the field of earthquake engineering and the field of disaster insurance business, and provides a method for simulating a seismic random event set by considering the correlation of large earthquake time, which comprises the following steps: determining seismic activity parameters on the seismic zone and the potential source area; setting the time length of a random event set seismic sequence; for a potential seismic source region, generating a seismic random event set by a seismic grading file; obtaining earthquake magnitude, magnitude and time of an earthquake by adopting a poisson process model for a low earthquake magnitude gear; obtaining the earthquake magnitude, magnitude and time of the earthquake by adopting a time-dependent process model for the high earthquake magnitude gear; and integrating the seismic catalogs of all the potential seismic source areas to obtain the seismic catalogs of the whole seismic band. The application creatively provides poisson distribution (time independent model) in the statistical sense of the whole seismic activity on the seismic zone, and simultaneously adopts a large-seismic-time related model for the local part of the potential seismic source zone, so that the whole and the local part are organically combined, the accuracy and the practicability of the simulation of the random event set of the earthquake are greatly improved, and the method has good application prospect.
Description
Technical Field
The application relates to the field of earthquake engineering and the field of disaster insurance business, in particular to a method for simulating a seismic random event set by considering the correlation of large earthquake time, which can be used for analyzing the earthquake risk.
Background
The Monte Carlo simulation method, also called random simulation method, is a method for performing computer simulation by using random numbers with various probability distributions, and the method randomly observes and samples along with a researched system, and obtains certain parameters of the researched system through observation statistics of a large number of sample values. Therefore, as long as the probability model of the occurrence of the earthquake can be reasonably represented, namely, the distribution rule of the occurrence of the earthquake event in a certain area in time and space is obtained, the Monte Carlo method can be directly utilized to simulate the earthquake sequence. For any one simulated seismic event, the ground movement caused by the seismic event can be simulated by using the attenuation relation. Thus, a sequence of ground movement in a period can be obtained, and the result which is the same as that of the traditional probability risk analysis method can be obtained through statistics of the sequence. This is the seismic risk analysis method based on Monte Carlo simulation.
The traditional probabilistic earthquake risk analysis method is to calculate the influence of the earthquake on the field singly and then calculate the influence of all the earthquake events within the influence range of the field point in an accumulated way, and the Monte Carlo method is to generate a series of earthquake random event sets, and the earthquake risk is calculated by using the random event sets. The random event set is the main input for calculating the earthquake vibration influence field, and the establishment of an earthquake disaster model in earthquake insurance is important. The generation of artificial seismic catalogues conforming to physical laws and seismological awareness is particularly important for seismic risk analysis.
The current method for generating the earthquake random event set by utilizing Monte Carlo mainly has the following problems:
(1) The earthquake activity generally adopts a poisson process model with independent time, and the poisson model is not capable of properly reflecting the process from inoculation to earthquake onset in the statistical sense, and particularly has obvious cycle recrudescence for high-earthquake-level earthquakes;
(2) It is not appropriate to apply the b value of the seismic band to the potential source region, the b value reflecting the proportional relationship of the different magnitude seismic frequencies within the seismic band. The method is a statistical relationship obtained after the seismic data of all the potential source areas in the seismic zone are integrated together, and does not represent the frequency distribution function relationship of the seismic coincidence magnitude in each potential source area in the zone, so that the potential source area used by the b value is inappropriate.
Disclosure of Invention
The application aims to overcome the defects of the prior art and provides a method for simulating a random event set of an earthquake by considering the correlation of time of a large earthquake.
The application is based on the following basic theory and law of seismology:
1. the magnitude of the earthquake in the earthquake zone satisfies the truncated magnitude-frequency relationship (G-R relationship): log n=a-bM, where N is the number of earthquakes with all the magnitude greater than or equal to M, and a and b are coefficients, which can be obtained according to actual seismic record statistics.
2. Seismic activity in the seismic zone satisfies poisson distribution: the occurrence of the earthquake has randomness in space and time, and if the occurrence is only considered without considering the size of the earthquake, the earthquake event in the earthquake zone meets poisson distribution.
3. The seismic activity is unevenly distributed between different potential source regions within the seismic band, while the seismic activity is evenly distributed within the potential source regions.
The application adopts the following technical scheme:
a method for simulating a set of seismic random events taking into account the correlation of time of a large earthquake, said method comprising the steps of:
s1, determining a seismic zone and seismic activity parameters of each potential seismic source zone in the seismic zone;
s2, setting the time length of the earthquake sequence to be simulated;
s3, obtaining the annual average incidence of each earthquake level on each potential earthquake source area according to the annual average incidence of the earthquake in the earthquake zone and the earthquake space distribution function; then, respectively randomly generating a seismic random event set according to different earthquake level files; obtaining the magnitude, time and position of earthquake by using a poisson process model for the low earthquake magnitude gear; obtaining the magnitude, time and position of the earthquake by adopting a time-dependent process model for the high-magnitude gear; wherein, the magnitude gear is higher than 7 and lower than or equal to 7;
and S4, integrating the seismic catalogs of the potential seismic source areas to obtain the seismic catalogs of the whole seismic zone.
Further, in step S1, the seismic activity parameter includes a magnitude upper limit, a magnitude lower limit, a b value in a magnitude frequency relationship, an annual average incidence rate in a seismic zone, and a seismic spatial distribution function; the seismic activity parameters are determined according to historical data, geological data, seismology, geology and other research data of the seismic zone.
Further, the specific steps of step S3 are as follows:
s3.1, according to a seismic space distribution function of a seismic zone, distributing the annual average incidence rate of the seismic zone to each earthquake level in each potential earthquake source zone to obtain the annual average incidence rate of each earthquake level in each potential earthquake source zone;
s3.2, according to the annual average incidence rate of all the earthquake level gears in the potential earthquake source area obtained in the step S3.1, respectively randomly generating earthquake random event sets according to different earthquake level gears; obtaining the number, magnitude, time and position of earthquakes by using a poisson process model for a low-magnitude gear; and obtaining the number, magnitude, time and position of the earthquakes by adopting a time-dependent process model for the high-magnitude gear.
Further, in step S3.2, the method for determining the number, magnitude and time of the earthquakes in a certain low-magnitude gear is as follows:
determining the number of earthquakes: obtaining the earthquake number of the low-earthquake-level gear by using a Poisson process model, and randomly generating a Poisson distribution random number n taking the annual average incidence of the earthquake-level gear as a parameter, wherein n is the earthquake number of the earthquake-level gear in unit time;
determining the occurrence time of earthquake: the time interval of poisson distribution events is in negative exponential distribution, and the time nodes at which the earthquakes occur are obtained according to the time interval among the earthquakes of the low-vibration level of the corresponding number generated randomly;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position.
Further, in step S3.2, the method for determining the earthquake occurrence level, the earthquake occurrence size and the earthquake occurrence time of a certain high-level gear is as follows:
determining whether an earthquake has occurred: judging whether high-magnitude earthquake occurs or not by adopting a time-dependent process model, if the judging condition is met, the high-magnitude earthquake occurs in the unit time cycle, otherwise, the high-magnitude earthquake does not occur;
determining the occurrence time of earthquake: randomly selecting a time point as earthquake occurrence time by adopting uniform distribution in unit time;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position.
Further, the time correlation model adopts a normal distribution model, a lognormal distribution model or a BPT model.
Further, the magnitude of the seismic magnitude is determined by one of three stochastic methods:
A. a random uniform sampling method is adopted on the interval of the magnitude gear, and the magnitude of the magnitude is randomly generated;
B. the magnitude of the magnitude range accords with the G-R relation (magnitude-frequency), the G-R relation is used as probability distribution of magnitude, and the magnitude of the magnitude is randomly generated by using a Monte Carlo method;
C. and carrying out statistical analysis on actual historical earthquakes on all the magnitude files in each potential earthquake source area in an earthquake zone to obtain a distribution rule of the historical earthquakes on all the magnitude files, taking the rule as a magnitude distribution probability model on the magnitude files, and then randomly generating magnitude by using a Monte Carlo method.
The application also provides a computer program for realizing the earthquake random event set simulation method considering the correlation of the large earthquake time.
An information data processing terminal for realizing the earthquake random event set simulation method considering the correlation of the large earthquake time.
A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the above-described seismic random event set simulation method that considers time dependence of a large earthquake.
The beneficial effects of the application are as follows:
1. in the generation process of the random event set, a time-dependent process model is adopted for the activity of the large earthquake to better accord with the physical process from accumulation to release of the actual earthquake strain, but the existing method only can generate the random event set which accords with the statistical rule and can not reflect the actual earthquake inoculation process. Because the time independent model is adopted to calculate the earthquake risk, the earthquake risk is overestimated when the earthquake occurrence time is short, and the earthquake risk is underestimated when the earthquake occurrence time is long;
2. the method provided by the application provides and adopts poisson distribution (time independent model) in the statistical sense of the whole seismic activity on the seismic zone, and simultaneously adopts a large-seismic time correlation model for the local part of the potential seismic source zone, so that the whole and the local part are organically combined. In the random event set simulation, the occurrence of the seismic event on the seismic band is considered to be a Poisson process, and the occurrence of the high-magnitude seismic event on each potential seismic source area in the seismic band is considered to be a non-Poisson process;
3. and obtaining the annual average incidence rate of each earthquake level grade according to the space distribution function on each potential earthquake source area, and then generating an earthquake random event set by the earthquake level grade. The problem that the magnitude relation of the potential seismic source area does not necessarily accord with the G-R (magnitude-frequency) is effectively avoided. Because the existing method for generating the seismic random event set generally generates the number of the seismic conforming to poisson distribution randomly by taking the annual average incidence rate as a parameter on a potential seismic source area, and then generates the seismic magnitude randomly according to the G-R relation. All the scales are put together at the time they are generated and the b value taken on the potential source zone is the b value on the seismic band. The b value over the seismic band is statistically based on data over the entire seismic band, while the potential source region is part of the seismic band, so such statistically significant values are not necessarily satisfied.
4. The earthquake activity of the low earthquake level adopts a time independent model, and the earthquake activity of the high earthquake level adopts a time dependent model. The different magnitude gears adopt different model mixing modes, so that the time-space non-uniformity of the seismic activity is reflected more.
Drawings
Fig. 1 is a schematic overall flow chart of a seismic random event set simulation method considering correlation of large earthquake time according to an embodiment of the application.
FIG. 2 is a schematic flow chart of a low-magnitude seismic random event set generation in an embodiment.
FIG. 3 is a schematic diagram of a process for generating a high-magnitude seismic random event set in an embodiment of the application.
FIG. 4 shows a historical seismic MT diagram of a Hendel seismic band.
FIG. 5 is a diagram of a simulation result seismic MT using the method of the application.
FIG. 6a is a diagram of simulated seismic "frequency versus time" for potential source region number 1.
FIG. 6b is a diagram of simulated seismic "frequency versus time" for potential source region number 2.
Fig. 7a shows a simulated seismic time interval distribution plot for a potential source zone number 29.
FIG. 7b shows a simulated seismic time interval distribution plot for a potential source region 30.
Fig. 7c shows a simulated seismic time interval distribution plot for a potential source zone number 31.
FIG. 7d shows a simulated seismic time interval distribution plot for a potential source region number 32.
FIG. 7e shows a simulated seismic time interval distribution plot for a potential source region 33.
FIG. 7f shows a simulated seismic time interval distribution plot for a potential source region 34.
FIG. 7g shows a simulated seismic time interval distribution plot for a potential source region number 35.
FIG. 8 shows the distribution characteristics of the number of simulated seismic annual occurrences with v 4 Is the number of earthquakes with magnitude of 4 or moreAnd (2) sum.
FIG. 9 is a graph showing a magnitude-frequency (G-R) relationship for seismic modeling.
FIG. 10 is a plot of the spatial distribution function of a simulated earthquake (probability of each magnitude range of earthquake falling within each potential source zone);
wherein: (a) grades 4.0-5.0; (b) 5.1-5.5; (c) stages 5.6-6.0; (d) stages 6.1-6.5; (e) stages 6.6-7.0; (f) stages 7.1-7.5; (g) grade 7.6-8.0.
Detailed Description
Specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be regarded as being isolated, and they may be combined with each other to achieve a better technical effect.
The application creatively proposes and adopts poisson distribution (time independent model) in statistical sense of the whole seismic activity on the seismic zone, and simultaneously adopts a large-earthquake time related model for the local part of the potential seismic source zone to organically combine the whole and the local part. In the random event set simulation, the occurrence of the seismic event on the seismic band is adopted to meet the poisson process, and the occurrence of the high-magnitude seismic event on the potential seismic source area is a non-poisson process.
According to the random procedural theory, X Y is two random variables which are independent of each other and obey poisson distribution, and the variable z=x+y is the sum of the poisson distribution obeying poisson distribution and the parameter X and Y, i.e. the poisson distribution on the seismic band can be decomposed into the superposition of poisson processes with different parameters on each potential seismic source area. The poisson process on the seismic zone reflects the statistical law on the whole seismic zone, but is not necessarily well reflected on the local potential seismic source zone. In particular, earthquakes of high magnitude at potential source areas with large-scale faults tend to exhibit good characteristic periodic recurrence. Thus, in a random event set simulation based on potential source regions, a low-magnitude earthquake is adopted as a poisson process and a high-magnitude earthquake is adopted as a non-poisson process. Namely, the Poisson process on the seismic band is decomposed into a partial Poisson process and a partial non-Poisson process. The parameters controlling the poisson distribution in the poisson distribution are the occurrence times in unit time, and the annual average occurrence rate in the earthquake event. The annual average incidence of each level of the potential source region is given by a spatial distribution function, and the normalization of the spatial distribution function is that the annual average incidence of each level of the potential source region in the seismic band is added to be equal to the annual average incidence of each level of the seismic band. And obtaining the low-magnitude gear on the seismic band as a poisson process by adopting the poisson process on the low-magnitude gear on the potential seismic source area. For the high-magnitude gear on the potential seismic source area, a non-poisson process is adopted, the annual average incidence rate reflects the average value of the seismic recurrence period, and therefore the results obtained by a Gaussian model or a BPT model and the like are all met by the non-poisson process. Since the annual incidence of the high-magnitude range for each potential source zone is normalized, the superposition of the high-magnitude ranges for each potential source zone reflects a poisson process that is also controlled by the annual incidence over the seismic zone. In addition, the variance of the model selection used in the non-poisson process on each of the potential source regions is reflected in the seismic bands after stacking. The non-poisson process of the high-magnitude gear can be popularized to the middle-magnitude gear and even the low-magnitude gear, namely, the poisson process on the seismic band can be obtained as long as the annual incidence rate of each magnitude gear on each potential seismic source area meets normalization no matter whether the poisson process or the non-poisson process is adopted. The problem to be solved here is that a potential source zone in a seismic zone has historical data, the recurrence period of strong earthquake can be set in a time-dependent model according to the historical data, but the potential source zone lack of data cannot be determined according to the historical data, and the high-magnitude earthquake characteristic period of the potential source zone lack of data can be deduced according to the seismic zone and the potential source zone with the historical data.
As shown in fig. 1, the embodiment of the application provides a method for simulating a seismic random event set by considering correlation of large earthquake time, which adopts a monte carlo method to simulate a random event set with more practical physical meaning, and specifically comprises the following steps:
s1, determining earthquake activity parameters on a seismic zone and a potential seismic source area based on historical data, geological data and seismology research, wherein the parameters specifically comprise the following parameters:
a) Upper limit of magnitude
The upper limit of the earthquake zone magnitude represents the upper limit value of the possible occurrence of the earthquake in the earthquake zone, namely the probability of the occurrence of the upper limit earthquake is almost zero; the upper limit of the magnitude is also the upper limit of the earthquake in the G-R relation in the earthquake statistical region according to the geological structure condition and the historical earthquake.
b) Lower limit of magnitude
The lower limit of the earthquake magnitude is also called the starting earthquake magnitude, and is the minimum earthquake magnitude which can influence the field point in the belt; the lower magnitude limit is also the lower magnitude limit for the earthquake in the G-R relationship within the seismic statistics area.
c) B-value in magnitude frequency relationship
The value b represents the proportional relation of the seismic frequency of different magnitudes in the seismic band, and the function is used for determining the probability density function of the seismic magnitude of the statistical region and the annual average incidence rate of each magnitude. Derived from the seismic data actually owned within the seismic statistics area.
d) Annual average incidence rate in seismic zone
The annual average occurrence of earthquakes refers to the number of earthquakes equal to or greater than the starting magnitude that occur each year on average in a statistical area.
e) Spatial distribution function
In the analysis of the probability of seismic risk, a function of the probability of occurrence of each magnitude range earthquake in each potential source zone is represented.
The spatio-temporal non-uniformity of the occurrence of an earthquake is characterized by the potential source region division and the upper limit of the magnitude of the earthquake, and is also related to the danger degree predicted by the earthquake construction position where the potential source region is and the earthquake activity characteristic. To faithfully reflect the spatio-temporal non-uniformity of seismic activity, the annual average incidence within the seismic band needs to be reasonably distributed into each potential source zone according to the prediction results. The distribution method adopted at present is to introduce a annual average incidence distribution weight coefficient, and the annual average incidence of a certain earthquake focus area in an earthquake zone is multiplied by the distribution weight coefficient of the potential earthquake focus area in the zone relative to the earthquake grade, so that the annual average incidence of the potential earthquake focus area in the earthquake grade is obtained, and the annual average incidence is expressed by the following formula:
V ij ＝V j W ij
wherein V is j Is the j-th earthquake level step of a certain earthquake zone [ M ] j-1 ,M j ]Average incidence over time; v (V) ij Is the annual average incidence of the jth magnitude gear of the in-band ith potential source zone; w (W) ij The above-mentioned distribution weight coefficients for the annual average incidence of the seismic level classes j in the seismic band to the potential source region i are then obtained.
Distribution weight coefficient W ij And may be further understood from another perspective. The above description is rewritten as:
W ij ＝v ij /v j
then W is ij Indicating that a certain earthquake zone has a primary magnitude of M (M j-1 ≤M≤M j ) The probability of the earthquake falling within the source region i, therefore, W ij Or may be a spatial distribution probability function, or spatial distribution function for short. The spatial distribution function is usually determined by adopting a factor equal weight method, a Bayesian criterion, fault tree analysis and other methods.
S2, setting the time length of the random event set seismic sequence. The length of time a seismic sequence needs to be simulated for a given simulation area may generally take different time periods of 5 years, 10 years, 20 years, or 50 years.
S3, obtaining the number of earthquake occurrences in unit time of each earthquake magnitude document on each potential earthquake source area according to the annual average occurrence rate and the earthquake space distribution function in the earthquake zone, and randomly generating an earthquake random event set by the earthquake magnitude document; obtaining the quantity, magnitude, time and position of earthquakes by using a poisson process model for the low-magnitude gear; obtaining the number, magnitude, time and position of earthquakes by adopting a time-dependent process model for the high-magnitude gear;
s3.1, according to a spatial distribution function of the seismic zone, distributing the annual average incidence rate of the seismic zone to each earthquake level in each potential earthquake source zone to obtain the annual average incidence rate of each earthquake level in each potential earthquake source zone;
s3.2, according to the annual average incidence rate of all the earthquake level gears in the potential earthquake source area obtained in the step S3.1, respectively randomly generating earthquake random event sets according to different earthquake level gears; obtaining the number, magnitude, time and position of earthquakes by using a poisson process model for a low-magnitude gear; and obtaining the number, magnitude, time and position of the earthquakes by adopting a time-dependent process model for the high-magnitude gear.
As shown in fig. 2, the method for determining the number, magnitude, size and time of the earthquakes in a certain low-magnitude gear is as follows:
determining the number of earthquakes: obtaining the earthquake number of the low-earthquake-level gear by using a Poisson process model, and randomly generating a Poisson distribution random number n taking the annual average incidence of the earthquake-level gear as a parameter, wherein n is the earthquake number of the earthquake-level gear in unit time;
determining the occurrence time of earthquake: the time interval of poisson distribution events is in negative exponential distribution, and the time nodes at which the earthquakes occur are obtained according to the time interval among the earthquakes of the low-vibration level of the corresponding number generated randomly;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position.
As shown in fig. 3, the method for determining the magnitude and time of the earthquake occurrence in a certain high-magnitude gear is as follows:
determining whether an earthquake has occurred: judging whether high-magnitude earthquake occurs or not by adopting a time-dependent process model, if the judging condition is met, the high-magnitude earthquake occurs in the unit time cycle, otherwise, the high-magnitude earthquake does not occur;
determining the occurrence time of earthquake: randomly selecting a time point as earthquake occurrence time by adopting uniform distribution in unit time;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position.
And S4, integrating the seismic catalogs of the potential seismic source areas to obtain the seismic catalogs of the whole seismic zone.
And (3) verifying results of the simulated seismic event set:
the process of seismic event simulation is based on the basic theory and setup and distribution described above. Whether the simulated random event set accords with theory and setting thereof in terms of time, space, G-R relation and earthquake incidence rate is checked to verify the reliability of the simulated earthquake event set. Selecting a Hendel river cover seismic zone as an example to simulate a seismic random event, and adopting the parameters of a fifth generation 'Chinese earthquake motion parameter zone map'; there are a total of 35 potential source regions in-band, with 7 potential source regions having an upper magnitude limit exceeding 7. The results of the simulation and verification of the simulation results are described below. The lower limit of the earthquake magnitude of the earthquake zone is 4.0, the upper limit of the earthquake magnitude is 8.0, the value b of the earthquake magnitude-frequency relation is 0.9, the annual average incidence rate of the earthquake zone is 4.5, and the earthquake magnitude gear is divided into earthquake magnitude gears which are divided into: 4.0 to 5.0, 5.1 to 5.5, 5.6 to 6.0, 6.1 to 6.5, 6.6 to 7.0, 7.1 to 7.5, 7.6 to 8.0, and in-band each potential source region parameter and spatial distribution function are detailed in the map parameters. And simulating an event set with the time sequence length of 10000 years, and selecting part of simulation results to explain.
1. Simulation results on seismic zones
In order to represent the change relation of earthquake magnitude and frequency with time, the seismology is represented by adopting a mode of an earthquake MT diagram, the horizontal axis is time, the vertical axis is magnitude, each earthquake is represented by a vertical line, and the density and intensity change of the earthquake are represented by the density and height of an earthquake marking line in the MT diagram. Historical data on the seismic bands 1970-2000 are selected as MT images, as shown in FIG. 4. Considering the incomplete history of the seismic records, especially the lack of records of small shocks due to insufficient recording capacity of the instrument, the MT diagram (FIG. 5) of the simulation result is basically consistent with the history of the seismic diagram.
2. Time distribution verification
The low-vibration level adopts a poisson distribution model, then the time interval value should conform to negative exponential distribution, the high-vibration level adopts a time correlation model of normal distribution, and then the time interval should conform to normal distribution. Since the seismic zone has more potential seismic sources for low-magnitude earthquake, only the potential seismic source region 1 and the potential seismic source region 2 are selected for illustration, and as can be seen in fig. 6a and 6b, the time interval of the low-magnitude earthquake is consistent with the negative exponential distribution and is consistent with the time interval distribution corresponding to the poisson model. The probability of high-magnitude earthquake happens to only 29-35 potential earthquake focus areas in the earthquake zone is shown in fig. 7 a-7 g, and it can be seen from the figures that the high-magnitude earthquake time intervals on 29-35 potential earthquake focus areas accord with normal distribution.
3. Checking the number of annual occurrences, i.e. the rate of occurrence
v4 is the sum of the seismic quantities with magnitude of 4 or more. The seismic band v4 represents the expected value (probability distribution mean) of the annual incidence of poisson distribution events, which determines the probability distribution of the number of occurrences of the earthquake, and is a descriptive parameter of the overall seismic activity of the seismic band. According to the assumption of Poisson distribution, the distribution characteristic of annual earthquake occurrence times is v 4 The main determinant of size. Distribution characteristics of annual earthquake occurrence times and v 4 The relationship of (2) is shown in FIG. 8. In fig. 8, the horizontal axis represents the number of annual earthquakes, and the vertical axis represents the frequency of occurrence of annual earthquakes. The distribution form of the annual earthquake occurrence times counted by the actual data is consistent with the poisson distribution. Thus, in determining the seismic statistics area v 4 This distribution characteristic is needed to reflect the number of occurrences of the earthquake year. In fig. 8, the histogram is counted by a set of random events, where the dashed line is a fitted poisson distribution curve, and it can be seen that it is substantially coincident.
4. Magnitude-frequency (G-R) relationship test
Within the seismic band, the seismic magnitude distribution conforms to the truncated G-R relationship. The histogram in fig. 9 is formed from the generated random event set statistics, where the dashed lines are fitted G-R relationships, and the magnitude of the earthquake and magnitude of the earthquake relationship magnitude-frequency relationship can be seen.
5. Spatial distribution function inspection
The spatial distribution function represents the probability that each magnitude range of the seismic falls within each potential source zone. And comparing the number of all the earthquake magnitude files in all the potential earthquake source areas in the whole earthquake catalogue with the space function to judge whether the earthquake magnitude files are in line with each other. FIG. 10 is a graph of the comparison of the various seismic levels with the spatial distribution function, wherein the broken lines are used for facilitating the resolution, the spatial distribution function is enlarged by the same proportion, the value of the broken points is the incidence ratio of the various seismic levels in the potential seismic source region, and the good fit can be seen.
Through the verification, the method can be used for accurately matching the statistical characteristics of all the earthquake in the earthquake zone, is superior to all the earthquake simulation methods in the prior art, and can be applied to the disaster insurance service field and the earthquake engineering field, and has wide application value.
Although a few embodiments of the present application have been described herein, those skilled in the art will appreciate that changes can be made to the embodiments herein without departing from the spirit of the application. The above-described embodiments are exemplary only, and should not be taken as limiting the scope of the claims herein.
Claims (6)
1. A method for simulating a set of seismic random events taking into account the correlation of time of a large earthquake, the method comprising the steps of:
s1, determining a seismic zone and seismic activity parameters of each potential seismic source zone in the seismic zone;
s2, setting the time length of the earthquake sequence to be simulated;
s3, obtaining the annual average incidence of each earthquake level on each potential earthquake source area according to the annual average incidence of the earthquake in the earthquake zone and the earthquake space distribution function; then, respectively randomly generating a seismic random event set according to different earthquake level files; a poisson process model is adopted for the low-vibration level, and a time-dependent process model is adopted for the high-vibration level; a poisson process is adopted for the low-magnitude gear on the potential seismic source area, and the low-magnitude gear on the seismic zone is obtained to be the poisson process; the annual incidence rate of the high-magnitude gear of each potential seismic source area is normalized, and the poisson process controlled by the annual incidence rate on the seismic band is reflected by overlapping the high-magnitude gears of each potential seismic source area; the annual incidence rate of each earthquake level on each potential earthquake source area is normalized, and the overall earthquake activity on the earthquake zone is statistically poisson distribution;
s4, integrating the seismic catalogs of each potential seismic source area to obtain a seismic catalog of the whole seismic zone;
the specific steps of the step S3 are as follows:
s3.1, according to a spatial distribution function of the seismic zone, distributing the annual average incidence rate of the seismic zone to each earthquake level in each potential earthquake source zone to obtain the annual average incidence rate of each earthquake level in each potential earthquake source zone;
s3.2, according to the annual average incidence rate of all the earthquake level gears in the potential earthquake source area obtained in the step S3.1, respectively randomly generating earthquake random event sets according to different earthquake level gears; obtaining the number, magnitude, time and position of earthquakes by using a poisson process model for a low-magnitude gear; obtaining the number, magnitude, time and position of earthquakes by adopting a time-dependent process model for the high-magnitude gear;
in step S3.2, the method for determining the number, magnitude, size and time of the earthquakes of a certain low-magnitude gear is as follows:
determining the number of earthquakes: obtaining the earthquake number of the low-earthquake-level gear by using a Poisson process model, and randomly generating a Poisson distribution random number n taking the annual average incidence of the earthquake-level gear as a parameter, wherein n is the earthquake number of the earthquake-level gear in unit time;
determining the occurrence time of earthquake: the time interval of poisson distribution events meets the negative index distribution, and the time nodes of the occurrence of the earthquake are obtained according to the time interval among the earthquake of the low-earthquake-level gear which is randomly generated by the corresponding number;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position;
the method for determining the occurrence, magnitude, size and time of the earthquake in a certain high-magnitude gear comprises the following steps:
determining whether an earthquake has occurred: judging whether high-magnitude earthquake occurs or not by adopting a time-dependent process model, if the judging condition is met, the high-magnitude earthquake occurs in the unit time cycle, otherwise, the high-magnitude earthquake does not occur;
determining the occurrence time of earthquake: randomly selecting a time point as earthquake occurrence time by adopting uniform distribution in unit time;
determining the magnitude of the earthquake: randomly sampling by using a Monte Carlo method by adopting a probability model to obtain the magnitude of the earthquake magnitude in the magnitude range;
determining the seismic position: randomly generating a random number x which is uniformly distributed between the upper longitude limit and the lower longitude limit of the potential seismic source area, then generating a random number y which is uniformly distributed between the upper latitude limit and the lower latitude limit of the potential seismic source area, judging whether the point (x, y) is in the potential seismic source area, and if the finally randomly generated earthquake center position falls in the potential seismic source area, obtaining the longitude and latitude of the earthquake center position.
2. The method for simulating a set of random events of an earthquake taking into account correlation of time of major earthquake as set forth in claim 1, wherein in step S1, the seismic activity parameters include upper limit of magnitude, lower limit of magnitude, b value in magnitude frequency relation, annual average incidence in the seismic zone, spatial distribution function; the seismic activity parameters are determined from historical data and geological data on the seismic zone.
3. The method for simulating a set of seismic random events taking into account time dependence of a large earthquake as claimed in claim 1, wherein the time dependent process model is a normal distribution model, a lognormal distribution model or a BPT model.
4. The method for simulating a set of seismic random events taking into account time dependence of a large earthquake as claimed in claim 1, wherein the magnitude of the seismic magnitude is determined by one of three random methods:
A. a random uniform sampling method is adopted on the interval of the magnitude gear, and the magnitude of the magnitude is randomly generated;
B. the magnitude of the upper magnitude of the magnitude gear section accords with the magnitude
C. And carrying out statistical analysis on actual historical earthquakes on all the magnitude files in each potential earthquake source area in an earthquake zone to obtain a distribution rule of the historical earthquakes on all the magnitude files, taking the rule as a magnitude distribution probability model on the magnitude files, and then randomly generating the magnitude of the earthquake by using a Monte Carlo method.
5. An information data processing terminal for implementing the seismic random event set simulation method considering correlation of large earthquake time according to any one of claims 1 to 4.
6. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the seismic random event set simulation method of any of claims 1 to 4 taking into account the correlation of time of major earthquakes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166378.4A CN111382908B (en) | 2020-03-11 | 2020-03-11 | Earthquake random event set simulation method considering correlation of large earthquake time |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166378.4A CN111382908B (en) | 2020-03-11 | 2020-03-11 | Earthquake random event set simulation method considering correlation of large earthquake time |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111382908A CN111382908A (en) | 2020-07-07 |
| CN111382908B true CN111382908B (en) | 2023-08-22 |
Family
ID=71218763
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010166378.4A Active CN111382908B (en) | 2020-03-11 | 2020-03-11 | Earthquake random event set simulation method considering correlation of large earthquake time |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111382908B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114114393B (en) * | 2021-10-11 | 2023-08-01 | 中国地震局地球物理研究所 | Method for evaluating historical earthquake intensity characteristics |
| CN115343751A (en) * | 2022-08-04 | 2022-11-15 | 中国地震局地震预测研究所 | Method and system for determining fault seismic level of boundary zone of movable land parcel |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004239901A (en) * | 2003-01-17 | 2004-08-26 | Takeda Engineering Consultant:Kk | Earthquake predicting method, earthquake prediction system, earthquake prediction program and recording medium |
| CN103955620A (en) * | 2014-05-13 | 2014-07-30 | 中国地质大学（北京） | Engineering site earthquake hazard analysis method considering effect of potential earthquake source orientations |
| CN104749632A (en) * | 2015-04-16 | 2015-07-01 | 徐州工程学院 | K-line analysis method of earthquake trend |
| CN108492236A (en) * | 2018-02-07 | 2018-09-04 | 国家海洋环境预报中心 | Multiple current Tsunami disaster appraisal procedure based on Monte Carlo stochastic simulation |
| CN110046454A (en) * | 2019-04-25 | 2019-07-23 | 中国地震局地质研究所 | Probabilistic Seismic economic loss calculation method and system |
-
2020
- 2020-03-11 CN CN202010166378.4A patent/CN111382908B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004239901A (en) * | 2003-01-17 | 2004-08-26 | Takeda Engineering Consultant:Kk | Earthquake predicting method, earthquake prediction system, earthquake prediction program and recording medium |
| CN103955620A (en) * | 2014-05-13 | 2014-07-30 | 中国地质大学（北京） | Engineering site earthquake hazard analysis method considering effect of potential earthquake source orientations |
| CN104749632A (en) * | 2015-04-16 | 2015-07-01 | 徐州工程学院 | K-line analysis method of earthquake trend |
| CN108492236A (en) * | 2018-02-07 | 2018-09-04 | 国家海洋环境预报中心 | Multiple current Tsunami disaster appraisal procedure based on Monte Carlo stochastic simulation |
| CN110046454A (en) * | 2019-04-25 | 2019-07-23 | 中国地震局地质研究所 | Probabilistic Seismic economic loss calculation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 地震区划原则和方法的研究――以华北地区为例;时振梁,鄢家全,高孟潭;地震学报(第02期);第179页-190页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111382908A (en) | 2020-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| McGuire et al. | An introduction to probabilistic seismic hazard analysis | |
| Erdik | Earthquake risk assessment | |
| Villani et al. | Repeatable site and path effects on the ground‐motion sigma based on empirical data from southern California and simulated waveforms from the CyberShake platform | |
| Rezaeian et al. | Simulation of synthetic ground motions for specified earthquake and site characteristics | |
| Abrahamson | Seismic hazard assessment: problems with current practice and future developments | |
| Ghasemi et al. | An empirical spectral ground-motion model for Iran | |
| Klügel et al. | A scenario-based procedure for seismic risk analysis | |
| Cao et al. | Seismic hazard estimate from background seismicity in southern California | |
| CN111382908B (en) | Earthquake random event set simulation method considering correlation of large earthquake time | |
| EP1999488B1 (en) | System and method for providing earthquake data | |
| US20160275044A1 (en) | Estimation of Hidden Variance Distribution Parameters | |
| Araya et al. | Seismic hazard analysis: improved models, uncertainties and sensitivities | |
| Zuccolo et al. | Comparing the performance of regional earthquake early warning algorithms in Europe | |
| Chen et al. | Nonstationary spatial correlation in New Zealand strong ground‐motion data | |
| Rekoske et al. | Basin and site effects in the US Pacific Northwest estimated from small‐magnitude earthquakes | |
| Erdik | Earthquake risk assessment from insurance perspective | |
| Damiani et al. | Impact of the uncertainty in the parameters of the earthquake occurrence model on loss estimates of urban building portfolios | |
| JP2003296396A (en) | Expected life cycle cost evaluation system of building and recording medium in which expected life cycle cost evaluation program is recorded | |
| Chandler et al. | Scenario predictions for potential near-field and far-field earthquakes affecting Hong Kong | |
| Boschi | New trends in active faulting studies for seismic hazard assessment | |
| Panzera et al. | Investigating worldwide strong motion databases to derive a collection of free-field records to select design-compatible waveforms for Switzerland | |
| Ulmer et al. | An Expanded Data Set of Overburden (K σ) and Initial Static Shear Stress (K α) Correction Factors from Published Cyclic Laboratory Tests for Liquefaction Triggering Analyses | |
| Hosseini et al. | Probabilistic fault displacement hazard analysis for the north Tabriz fault | |
| Felzer | Appendix I: calculating California seismicity rates | |
| Alavi et al. | Optimizing Interpolation Methods and Point Distances for Accurate Earthquake Hazard Mapping in Gas Pipeline Routing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |