CN108376125B - Seismic intensity evaluation method and device - Google Patents
Seismic intensity evaluation method and device Download PDFInfo
- Publication number
- CN108376125B CN108376125B CN201810087847.6A CN201810087847A CN108376125B CN 108376125 B CN108376125 B CN 108376125B CN 201810087847 A CN201810087847 A CN 201810087847A CN 108376125 B CN108376125 B CN 108376125B
- Authority
- CN
- China
- Prior art keywords
- seismic
- intensity
- seismic intensity
- historical
- earthquake
- 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.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Abstract
The invention relates to a method and a device for evaluating seismic intensity, which evaluate the possibility of the seismic intensity under the premise of considering the uncertainty of the seismic intensity and avoid the deviation of a deterministic seismic intensity estimated value and an actual value caused by the dispersion between the seismic intensity and seismic motion parameters. The method comprises the following steps: constructing a seismic intensity evaluation model according to the historical seismic oscillation parameters of multiple places and the corresponding historical seismic intensity; acquiring a target earthquake motion parameter value acquired by a target station when an earthquake occurs; and obtaining the probability that the seismic intensity of the position of the target station is respectively the intensity grade according to the target seismic motion parameter value and the seismic intensity evaluation model.
Description
Technical Field
The disclosure relates to the field of seismic monitoring, in particular to a seismic intensity evaluation method and device.
Background
After an earthquake happens, the earthquake can damage earth surface buildings, and the loss of lives and properties of people is brought. The earthquake intensity is the most intuitive index for measuring the intensity of ground vibration caused by an earthquake and the degree of damage and casualties. The earthquake intensity has wide and irreplaceable effects in earthquake prevention and disaster reduction, and is the basis of research on aspects such as emergency disaster relief after earthquake, earthquake specification and design, earthquake damage prediction, earthquake motion parameters, earthquake motion attenuation relation and the like. The traditional intensity evaluation method is that earthquake workers arrive at an earthquake field after earthquake, and the intensity is evaluated after all areas are integrated, obviously, the method is slow, and is not beneficial to emergency disaster relief and loss evaluation after earthquake.
In order to quickly estimate the seismic intensity after an earthquake occurs, a large number of researchers research the corresponding relation between the seismic motion parameters and the seismic intensity, and after the empirical relation between the seismic motion parameters and the seismic intensity is researched, the intensity evaluation result can be obtained by quickly acquiring the strong earthquake table network data after the earthquake occurs.
However, due to the complexity of seismic damage, the rapidly-evaluated intensity is greatly deviated from the actually-surveyed intensity map. For example, in Wenchuan earthquake of grade 8.0, the evaluation result of the American intensity rapid evaluation chart ShakeMap has a larger deviation with the intensity distribution chart evaluated by Wenchuan earthquake field survey of grade 8.0. Comparing the corresponding relationship between seismic intensity and seismic motion parameters (peak acceleration PGA and peak velocity PGV) given by researchers in the united states, japan, europe, and china, the results show that the relationship between seismic intensity and seismic motion parameters (PGA and PGV) is greatly discrete. Under the same intensity, the maximum value of the ratio of the maximum value to the minimum value of PGA and PGV is 32 times of V degree and 10 times of VI degree respectively. The great dispersion of the relationship between the seismic intensity and the seismic motion parameters is the root cause of great deviation between the rapidly evaluated intensity and the actually investigated intensity map, which is the difficult point and key point to be solved at present.
Disclosure of Invention
The invention aims to provide a seismic intensity evaluation method and a device, which are used for evaluating the possibility of seismic intensity on the premise of considering the uncertainty of the seismic intensity and avoiding the deviation of a deterministic seismic intensity estimated value and an actual value caused by the dispersion between the seismic intensity and seismic motion parameters.
According to a first aspect of the embodiments of the present disclosure, there is provided a seismic intensity evaluation method, including:
constructing a seismic intensity evaluation model according to the historical seismic oscillation parameters of multiple places and the corresponding historical seismic intensity;
acquiring a target earthquake motion parameter value acquired by a target station when an earthquake occurs;
and obtaining the probability that the seismic intensity of the position of the target station is respectively the intensity grade according to the target seismic motion parameter value and the seismic intensity evaluation model.
Optionally, the constructing a seismic intensity evaluation model according to the historical seismic motion parameters of the multiple locations and the corresponding historical seismic intensities includes:
obtaining a frequency distribution histogram of the historical earthquake motion parameters corresponding to the earthquake intensity according to the historical earthquake motion parameters and the corresponding historical earthquake intensity;
determining a probability distribution mode of earthquake motion parameters according to the frequency distribution histogram;
determining probability density functions of the earthquake motion parameters under each intensity level according to the probability distribution mode;
and constructing the seismic intensity evaluation model according to the probability density function.
Optionally, the seismic motion parameters include a peak acceleration and a peak velocity, and the probability density functions of the seismic motion parameters respectively for each intensity level are determined, where the probability density functions include at least one of the following:
determining probability density functions of the peak acceleration aiming at all intensity levels respectively;
determining probability density functions of the peak speed respectively aiming at all intensity levels;
the joint probability density function of peak acceleration and peak velocity at each severity level is determined.
Optionally, constructing the seismic intensity evaluation model according to the probability density function includes:
and constructing the seismic intensity evaluation model according to a Bayesian formula and the probability density function.
Optionally, before constructing the seismic intensity evaluation model according to the historical seismic motion parameters of the multiple locations and the corresponding historical seismic intensities, the method further includes:
detecting outliers in the historical seismic oscillation parameters of multiple places;
deleting the outliers;
according to the historical seismic motion parameters of multiple places and the corresponding historical seismic intensity, a seismic intensity evaluation model is constructed, and the method comprises the following steps:
and constructing the seismic intensity evaluation model according to the historical seismic motion parameters with the outliers deleted and the corresponding historical seismic intensity.
Optionally, after obtaining the probabilities that the seismic intensity of the position where the target station is located is each intensity level, the method further includes:
and determining the seismic intensity expectation and the seismic intensity standard deviation of the position according to the probability that the seismic intensity of the position is each intensity grade.
Optionally, the method further includes:
after seismic intensity expectation and seismic intensity standard deviation of a plurality of positions where a plurality of stations are located are respectively determined, obtaining the seismic intensity expectation and the seismic intensity standard deviation of each position in the area where the plurality of stations are located according to an interpolation method;
and drawing a seismic intensity expected graph and a seismic intensity standard deviation graph of the area according to the seismic intensity expected graph and the seismic intensity standard deviation of each position.
According to a second aspect of embodiments of the present disclosure, there is provided a seismic intensity evaluation device, comprising:
the model construction module is used for constructing a seismic intensity evaluation model according to the historical seismic motion parameters of multiple places and the corresponding historical seismic intensity;
the data acquisition module is used for acquiring a target earthquake motion parameter value acquired by the target station when an earthquake occurs;
and the probability obtaining module is used for obtaining the probability that the seismic intensity of the position of the target station is respectively in each intensity level according to the target seismic motion parameter value and the seismic intensity evaluation model.
Optionally, the model building module includes:
the histogram obtaining module is used for obtaining a frequency distribution histogram of the historical earthquake motion parameters corresponding to the earthquake intensity according to the historical earthquake motion parameters and the corresponding historical earthquake intensity;
the first determining module is used for determining the probability distribution mode of the earthquake motion parameters according to the frequency distribution histogram;
the second determining module is used for determining probability density functions of the earthquake motion parameters under each intensity level according to the probability distribution mode;
and the model construction submodule is used for constructing the seismic intensity evaluation model according to the probability density function.
Optionally, the seismic motion parameters include peak acceleration and peak velocity, and the second determination module includes at least one of the following sub-modules:
the first determining submodule is used for determining probability density functions of the peak acceleration aiming at all intensity levels respectively;
the second determining submodule is used for determining probability density functions of the peak speed respectively aiming at all intensity levels;
and the third determining submodule is used for determining a combined probability density function of the peak acceleration and the peak speed under each intensity level.
Optionally, the model construction sub-module is configured to:
and constructing the seismic intensity evaluation model according to a Bayesian formula and the probability density function.
Optionally, the apparatus further comprises:
the detection module is used for detecting outliers in the historical seismic oscillation parameters of multiple places;
a deletion module to delete the outliers;
the model building module is configured to:
and constructing the seismic intensity evaluation model according to the historical seismic motion parameters with the outliers deleted and the corresponding historical seismic intensity.
Optionally, the apparatus further comprises:
and the third determining module is used for determining the seismic intensity expectation and the seismic intensity standard deviation of the position according to the probability that the seismic intensity of the position is respectively the intensity grade.
Optionally, the apparatus further comprises:
the acquisition module is used for acquiring seismic intensity expectation and seismic intensity standard deviation of each position in the area where the plurality of stations are located according to an interpolation method after the seismic intensity expectation and the seismic intensity standard deviation of the plurality of positions where the plurality of stations are located are respectively determined;
and the drawing module is used for drawing the seismic intensity expectation graph and the seismic intensity standard deviation graph of the area according to the seismic intensity expectation and the seismic intensity standard deviation of each position.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a computer, enable the computer to perform the method of the first aspect described above.
By the technical scheme, the seismic intensity evaluation model can be constructed according to the historical seismic parameters of multiple places and the corresponding historical seismic intensity, so that when an earthquake occurs, the target seismic motion parameter values acquired by the target station can be acquired, and then the probability that the seismic intensity of the position of the target station is in each intensity level is obtained according to the constructed seismic intensity evaluation model. Through the mode, the uncertain seismic intensity rapid evaluation method can rapidly evaluate the possibility of the seismic intensity grade by directly using the target seismic motion parameter value issued by the target station after an earthquake, and solves the problem that the existing seismic intensity value directly estimated according to experience has deviation with an actual value.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow diagram illustrating a seismic intensity evaluation method in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a seismic intensity evaluation probability according to an exemplary embodiment;
FIG. 3 is a diagram illustrating seismic intensity expectations, according to an exemplary embodiment;
FIG. 4 is a seismic intensity standard deviation plot shown in accordance with an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating a seismic intensity estimation process according to an exemplary embodiment;
FIG. 6 is a block diagram illustrating a seismic intensity evaluation device, according to an exemplary embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, and are not intended to limit the present disclosure.
FIG. 1 is a flow diagram illustrating a seismic intensity evaluation method, which may be implemented in a computer, as shown in FIG. 1, according to an exemplary embodiment, including the following steps.
Step S11: and constructing a seismic intensity evaluation model according to the historical seismic motion parameters of the multiple places and the corresponding historical seismic intensity.
Step S12: and acquiring target earthquake motion parameter values acquired by the target station when an earthquake occurs.
Step S13: and obtaining the probability that the seismic intensity of the position of the target station is respectively the intensity grade according to the target seismic motion parameter value and the seismic intensity evaluation model.
In step S11, The earthquake Motion parameters may include PGA and PGV parameters, and The multiple historical earthquake Motion parameters and corresponding historical earthquake intensities may be obtained from domestic and foreign related databases, for example, Strong vibration data of countries such as china, usa, mexico, Iran, etc. may be collected through a central website of a china Strong vibration table Network, a natural disaster website of The national marine and atmospheric administration, a COSMOS (The Consortium of organisations for Strong-Motion Observation Systems, Strong vibration Observation and cooperation Organizations) Strong vibration database, a japanese NIED (national institute of disaster prevention science and technology) Strong vibration database, a european ESM Strong vibration database, a european PRSM Strong vibration database, a netiran Strong vibration data Network (Iran Strong vibration work), etc. to obtain domestic and foreign original Strong vibration data.
After the historical seismic parameters and seismic intensity of multiple places are obtained, a seismic intensity evaluation model can be constructed, and the following description will explain the method for constructing the seismic intensity evaluation model.
In one embodiment, the acquired data of the multiple places of the historical earthquake may include some outliers, and for convenience of statistics and obtaining a more accurate statistical result, the outliers of the data may be detected, and the historical earthquake motion parameters and the historical earthquake intensity after the outliers are deleted may be used to construct the earthquake intensity evaluation model.
The method of detecting an outlier is not limited in the embodiments of the present disclosure, as long as the outlier can be detected. For example, the outlier in the data may be checked using a box plot detection method, and then, for example, if the distance of the observation value from the bottom (fourth quartile) Q1 or the top (third quartile) Q3 of the box plot exceeds the observation value of the box height H (H ═ Q3-Q1) by more than 1.5 times, it may be regarded as the outlier, that is, the data in the section where the observation data is not (Q1-1.5H, Q3+1.5H) is the outlier.
In one embodiment, a seismic intensity evaluation model is constructed according to historical seismic motion parameters of multiple places and corresponding historical seismic intensities, a frequency distribution histogram of the historical seismic motion parameters corresponding to the seismic intensities is obtained according to the historical seismic motion parameters and the corresponding historical seismic intensities, then a probability distribution mode of the seismic motion parameters is determined according to the frequency distribution histogram, probability density functions of the seismic motion parameters under each intensity level are determined according to the probability distribution mode, and finally the seismic intensity evaluation model is constructed according to the probability density functions.
That is, the obtained historical seismic motion parameters and the corresponding historical seismic intensity of the multiple places can be counted, so that a frequency distribution histogram of the historical seismic motion parameters can be obtained, and the frequency distribution histogram can reflect the distribution condition of the historical seismic motion parameters corresponding to the seismic intensity. In practical application, for convenience of calculation, frequency distribution histograms of logPGA and logPGV respectively corresponding to seismic intensity can be constructed, so that a probability distribution mode of seismic oscillation parameters PGA and PGV is reflected. By observing and analyzing the frequency distribution histogram, which probability distribution mode (such as normal distribution, exponential distribution, beta distribution, and the like) the earthquake motion parameters conform to can be known.
Further, after determining the probability distribution mode of the seismic motion parameters, determining the probability density function of the seismic motion parameters respectively for each intensity level may include at least one of: determining probability density functions of the peak acceleration PGA respectively aiming at all intensity levels; determining probability density functions of the peak speed PGV respectively aiming at all intensity levels; and determining a joint probability density function of the peak acceleration PGA and the peak speed PGV under each intensity level.
In the embodiment of the present disclosure, the logPGA and logPGV are described as examples that they are normally distributed at each intensity level.
The probability distribution formulas of PGA and PGV at each intensity can be written as follows:
I(logPGA)＝μ(logPGA)+σ(logPGA)·e (1)
I(logPGV)＝μ(logPGV)+σ(logPGV)·e (2)
wherein I is seismic intensity; mu is the mean value of the earthquake motion parameters; sigma is the standard deviation of earthquake motion parameters; e is the normalized residual, conforming to the standard normal distribution N (0, 1).
To determine the seismic motion parameter mean μ and the seismic motion parameter standard deviation σ in equations (1) and (2), the present disclosure uses a gaussian curve to fit μ and σ in each intensity level, the results of which are shown in tables 1 and 2, for example, and in order to avoid the deficiency of data points that tend to be high in weight when ordinary least squares regression is performed, a weighted least squares method may be used to fit the equations. Table 1 shows the values of μ and σ in equation (1) at each intensity level, and table 2 shows the values of μ and σ in equation (2) at each intensity level.
TABLE 1 mean and standard deviation of logPGA at each intensity (I ≦ XI)
TABLE 2 mean and standard deviation of logPGV at each intensity (I ≦ XI)
Correspondingly, the probability density function of the PGA or PGV for each intensity level is:
wherein, X is used for representing seismic motion parameters (logPGA or logPGV); i isiI is the seismic intensity of I level, I is more than or equal to I and less than or equal to XI; when X is a logPGA, the sequence is,
is the probability density function of PGA at i-level seismic intensity, and μ and σ are the mean and standard deviation (such as the values shown in Table 1) corresponding to logPGA; when X is logPGV, the compound is,
the probability density function of PGV at i-level seismic intensity, μ and σ are mean and standard deviation (for example, the values shown in table 2) corresponding to logPGV.
The present disclosure may also synthesize PGA and PGV to obtain a joint probability density function, as follows:
wherein, f (x)i,yi) The joint probability density function of PGA and PGV of intensity grade i; x is the number ofiIs the value of logPGA at severity level i; y isiThe value of logPGV for severity grade i; sigmaxiIs the standard deviation of logPGA under the intensity grade i; sigmayiIs the standard deviation of logPGV at severity level i; mu.sxiThe mean value of logPGA under the intensity grade i; mu.syiIs the mean of logPGV under severity level i; rhoiIs the correlation coefficient of logPGA and logPGV under the intensity level i. Sigmaxi、σyi、μxiAnd muyiMay be obtained by averaging and standard deviation similar to the above, piThe method can also be obtained by statistical regression, and will not be described herein.
Through the method, the individual probability density function of the PGA or the PGV can be constructed, and the combined probability density function of the PGA and the PGV can also be constructed, so that when intensity evaluation is carried out, which parameter is used for evaluation can be selected according to needs, evaluation results are diversified, and the referential property is strong.
Furthermore, after the probability density function is obtained, a seismic intensity evaluation model can be constructed, and the Bayesian formula can be adopted to construct the seismic intensity evaluation model in the embodiment of the disclosure.
The Bayesian equation is as follows:
wherein, X is used to represent seismic oscillation parameter (logPGA or logPGV); i isiI is the seismic intensity of I level, and I is more than or equal to I and less than or equal to XI.
Referring to fig. 2, it can be seen that equation (5) can be expressed as the shadow area under a certain intensity (i) divided by the shadow area under all intensities, i.e.,
formula (6) is the seismic intensity evaluation model, P (I)iIx) represents the probability that the seismic intensity is of order i,
is the probability density function of the seismic motion parameter X at i-level seismic intensity (which may be the probability density function of PGA, see, for example, equation (3)), or the probability density function of PGV, see, for example, equation (3), or may also be the joint probability density function of PGA and PGV, see, for example, equation (4)).
After obtaining the seismic intensity evaluation model, the seismic intensity evaluation model may be stored in a computer for estimating the seismic intensity, and the computer may obtain seismic motion parameters of the seismic from a platform issuing the seismic data in time after the seismic occurs (i.e., step S12 is performed), and then calculate the probability that the intensity level of the seismic is each intensity level through the seismic intensity evaluation model (i.e., step S13 is performed). Certainly, after the seismic intensity evaluation model is built, seismic data after a plurality of earthquakes occur in a period of time can be acquired at intervals, and then the seismic intensity evaluation model is updated, so that the accuracy of seismic intensity estimation is improved.
For example, the acquired seismic intensity parameter is PGA, and after an earthquake occurs, the acquired PGA recorded by a strong earthquake observation station (target station) at a certain site is 196gal, and then the probability that the seismic intensity of the site is i degrees can be obtained by substituting the parameters in table 1 into equation (6):
similarly, other II-XI probabilities were obtained, and the results are shown in Table 3.
TABLE 3 probability that the location of the target station may be of various intensity
In one embodiment, after the probabilities that the seismic intensity of the location where the target station is located is at each intensity level are obtained, the seismic intensity expectation and the seismic intensity standard deviation of the location can be determined according to the probabilities that the seismic intensity of the location is at each intensity level.
The formula for calculating the seismic intensity expectation and the seismic intensity standard deviation is as follows:
wherein E (I) is expected seismic intensity, SD (I) is standard deviation of seismic intensity, and P (i) is probability that seismic intensity is i degree.
For example, continuing with the above description of Table 3 as the computed seismic intensity estimate for the location of the target station, substituting the parameters of Table 3 into equations (7) and (8) yields the seismic intensity expected versus seismic intensity standard deviation for that location:
through the mode of obtaining the seismic intensity expectation and the seismic intensity standard deviation, the occurrence situation of the earthquake can be reflected more visually.
In one embodiment, after the seismic intensity expectations and the seismic intensity standard deviations of the multiple positions where the multiple stations are located are respectively determined, the seismic intensity expectations and the seismic intensity standard deviations of the multiple positions in the area where the multiple stations are located are obtained according to an interpolation method, and then a seismic intensity expectation graph and a seismic intensity standard deviation graph of the area are drawn according to the seismic intensity expectations and the seismic intensity standard deviations of the multiple positions.
The disclosed embodiments are not limited as to which interpolation method is used to calculate the seismic intensity expectation and seismic intensity standard deviation for each location. For example, interpolation methods such as kriging interpolation, inverse distance weighting, polynomial method, spline function interpolation, and the like can be used on a GIS (geographic Information System) platform.
In the embodiment of the disclosure, according to 37 groups of strong vibration records recorded in 6.2-level earthquake occurring on the wall of the Xinjiang call chart of 12 months and 8 days in 2016 published by the national strong vibration table network center, an expected earthquake intensity chart and a standard deviation earthquake intensity chart are drawn, which are respectively shown in fig. 3 and fig. 4. The expectation graph reflects the seismic intensity result in the average sense, the standard deviation graph reflects the dispersion degree of the result, the situation of an intensity abnormal area can be reflected, and the result is more comprehensive than that of a traditional evaluation mode.
The technical solution of the present disclosure will be explained by the complete examples below.
Referring to fig. 5, the present disclosure provides a method for rapidly evaluating seismic intensity considering uncertainty, which obtains seismic oscillation parameters PGA and PGV frequency distribution histograms after outlier inspection after obtaining domestic and foreign basic data, and then selects a reasonable mathematical probability distribution model according to the frequency distribution histograms. After uncertainty is considered, the seismic oscillation parameters PGA and PGV are random variables under each intensity, and a probability formula and parameter fitting of the seismic intensity and the seismic oscillation parameters are obtained. The intensity rapid evaluation system automatically triggers, acquires and calculates earthquake motion parameter data of a national strong earthquake table network center, and obtains the intensity evaluation of a certain place considering uncertainty according to a Bayesian formula. Based on a GIS system, a spatial interpolation method is adopted to obtain an expected seismic intensity evaluation graph and a standard deviation graph in a certain area, and a scientific and reliable intensity rapid evaluation graph is rapidly released after an earthquake (within 3 minutes, for example).
Through the technical scheme, the seismic intensity evaluation model can be constructed according to the historical seismic parameters of multiple places and the corresponding historical seismic intensity, so that when an earthquake occurs, the target seismic motion parameter values acquired by the target station can be acquired, and then the probability that the seismic intensity of the position of the target station is in each intensity level is obtained according to the constructed seismic intensity evaluation model. Through the mode, the uncertain seismic intensity rapid evaluation method can rapidly evaluate the possibility of the seismic intensity grade by directly using the target seismic motion parameter value issued by the target station after an earthquake, and solves the problem that the existing seismic intensity value directly estimated according to experience has deviation with an actual value.
Referring to fig. 6, based on the same inventive concept, an embodiment of the present disclosure provides a seismic intensity evaluation device 600, where the device 600 may include:
the model construction module 601 is used for constructing a seismic intensity evaluation model according to the historical seismic motion parameters of multiple places and the corresponding historical seismic intensities;
a data obtaining module 602, configured to obtain a target earthquake motion parameter value acquired by a target station when an earthquake occurs;
and a probability obtaining module 603, configured to obtain probabilities that the seismic intensity of the position where the target station is located is each intensity level according to the target seismic motion parameter value and the seismic intensity evaluation model.
Optionally, the model building module 601 includes:
the histogram obtaining module is used for obtaining a frequency distribution histogram of the historical earthquake motion parameters corresponding to the earthquake intensity according to the historical earthquake motion parameters and the corresponding historical earthquake intensity;
the first determining module is used for determining the probability distribution mode of the earthquake motion parameters according to the frequency distribution histogram;
the second determining module is used for determining probability density functions of the earthquake motion parameters under each intensity level according to the probability distribution mode;
and the model construction submodule is used for constructing the seismic intensity evaluation model according to the probability density function.
Optionally, the seismic motion parameters include peak acceleration and peak velocity, and the second determination module includes at least one of the following sub-modules:
the first determining submodule is used for determining probability density functions of the peak acceleration aiming at all intensity levels respectively;
the second determining submodule is used for determining probability density functions of the peak speed respectively aiming at all intensity levels;
and the third determining submodule is used for determining a combined probability density function of the peak acceleration and the peak speed under each intensity level.
Optionally, the model construction sub-module is configured to:
and constructing the seismic intensity evaluation model according to a Bayesian formula and the probability density function.
Optionally, the apparatus 600 further includes:
the detection module is used for detecting outliers in the historical seismic oscillation parameters of multiple places;
a deletion module to delete the outliers;
the model building module 601 is configured to:
and constructing the seismic intensity evaluation model according to the historical seismic motion parameters with the outliers deleted and the corresponding historical seismic intensity.
Optionally, the apparatus 600 further includes:
and the third determining module is used for determining the seismic intensity expectation and the seismic intensity standard deviation of the position according to the probability that the seismic intensity of the position is respectively the intensity grade.
Optionally, the apparatus 600 further includes:
the acquisition module is used for acquiring seismic intensity expectation and seismic intensity standard deviation of each position in the area where the plurality of stations are located according to an interpolation method after the seismic intensity expectation and the seismic intensity standard deviation of the plurality of positions where the plurality of stations are located are respectively determined;
and the drawing module is used for drawing the seismic intensity expectation graph and the seismic intensity standard deviation graph of the area according to the seismic intensity expectation and the seismic intensity standard deviation of each position.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a non-transitory computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk, or an optical disk.
The above embodiments are only used to describe the technical solutions of the present disclosure in detail, but the above embodiments are only used to help understanding the method and the core idea of the present disclosure, and should not be construed as limiting the present disclosure. Those skilled in the art should also appreciate that various modifications and substitutions can be made without departing from the scope of the present disclosure.
Claims (8)
1. A seismic intensity evaluation method, comprising:
constructing a seismic intensity evaluation model according to the historical seismic oscillation parameters of multiple places and the corresponding historical seismic intensity;
acquiring a target earthquake motion parameter value acquired by a target station when an earthquake occurs;
obtaining the probability that the seismic intensity of the position of the target station is respectively the intensity level according to the target seismic motion parameter value and the seismic intensity evaluation model;
the method comprises the following steps of constructing a seismic intensity evaluation model according to historical seismic motion parameters of multiple places and corresponding historical seismic intensities, wherein the method comprises the following steps:
obtaining a frequency distribution histogram of the historical earthquake motion parameters corresponding to the earthquake intensity according to the historical earthquake motion parameters and the corresponding historical earthquake intensity;
determining a probability distribution mode of earthquake motion parameters according to the frequency distribution histogram;
determining probability density functions of the earthquake motion parameters under each intensity level according to the probability distribution mode;
and constructing the seismic intensity evaluation model according to the probability density function.
2. The method of claim 1, wherein the seismic motion parameters comprise peak acceleration and peak velocity, and wherein determining the probability density function of the seismic motion parameters for each intensity level comprises at least one of:
determining probability density functions of the peak acceleration aiming at all intensity levels respectively;
determining probability density functions of the peak speed respectively aiming at all intensity levels;
the joint probability density function of peak acceleration and peak velocity at each severity level is determined.
3. The method of claim 1, wherein constructing the seismic intensity evaluation model from the probability density function comprises:
and constructing the seismic intensity evaluation model according to a Bayesian formula and the probability density function.
4. The method of claim 1 or 2, further comprising, prior to constructing the seismic intensity evaluation model based on the historical seismic motion parameters and corresponding historical seismic intensities from the plurality of locations:
detecting outliers in the historical seismic oscillation parameters of multiple places;
deleting the outliers;
according to the historical seismic motion parameters of multiple places and the corresponding historical seismic intensity, a seismic intensity evaluation model is constructed, and the method comprises the following steps:
and constructing the seismic intensity evaluation model according to the historical seismic motion parameters with the outliers deleted and the corresponding historical seismic intensity.
5. A method as claimed in claim 1 or 2, wherein after obtaining the probabilities of the seismic intensity at the location of the target station being at respective intensity levels, further comprising:
and determining the seismic intensity expectation and the seismic intensity standard deviation of the position according to the probability that the seismic intensity of the position is each intensity grade.
6. The method of claim 5, further comprising:
after seismic intensity expectation and seismic intensity standard deviation of a plurality of positions where a plurality of stations are located are respectively determined, obtaining the seismic intensity expectation and the seismic intensity standard deviation of each position in the area where the plurality of stations are located according to an interpolation method;
and drawing a seismic intensity expected graph and a seismic intensity standard deviation graph of the area according to the seismic intensity expected graph and the seismic intensity standard deviation of each position.
7. A seismic intensity evaluation device, comprising:
the model construction module is used for constructing a seismic intensity evaluation model according to the historical seismic motion parameters of multiple places and the corresponding historical seismic intensity;
the data acquisition module is used for acquiring a target earthquake motion parameter value acquired by the target station when an earthquake occurs;
the probability obtaining module is used for obtaining the probability that the seismic intensity of the position of the target station is in each intensity level according to the target seismic motion parameter value and the seismic intensity evaluation model;
wherein the model building module comprises:
the histogram obtaining module is used for obtaining a frequency distribution histogram of the historical earthquake motion parameters corresponding to the earthquake intensity according to the historical earthquake motion parameters and the corresponding historical earthquake intensity;
the first determining module is used for determining the probability distribution mode of the earthquake motion parameters according to the frequency distribution histogram;
the second determining module is used for determining probability density functions of the earthquake motion parameters under each intensity level according to the probability distribution mode;
and the model construction submodule is used for constructing the seismic intensity evaluation model according to the probability density function.
8. The apparatus of claim 7, wherein the seismic motion parameters comprise peak acceleration and peak velocity, and wherein the second determination module comprises at least one of the following sub-modules:
the first determining submodule is used for determining probability density functions of the peak acceleration aiming at all intensity levels respectively;
the second determining submodule is used for determining probability density functions of the peak speed respectively aiming at all intensity levels;
and the third determining submodule is used for determining a joint probability density function of the peak acceleration and the peak speed under each intensity level.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810087847.6A CN108376125B (en) | 2018-01-29 | 2018-01-29 | Seismic intensity evaluation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810087847.6A CN108376125B (en) | 2018-01-29 | 2018-01-29 | Seismic intensity evaluation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108376125A CN108376125A (en) | 2018-08-07 |
| CN108376125B true CN108376125B (en) | 2021-04-16 |
Family
ID=63017028
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810087847.6A Expired - Fee Related CN108376125B (en) | 2018-01-29 | 2018-01-29 | Seismic intensity evaluation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108376125B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111427984B (en) * | 2020-03-24 | 2022-04-01 | 成都理工大学 | Regional seismic probability space distribution generation method |
| CN113053064B (en) * | 2021-03-15 | 2022-11-04 | 中国地震局地球物理研究所 | Regional earthquake early warning system evaluation method and system |
| CN116046079B (en) * | 2023-04-03 | 2023-06-30 | 山东省地质调查院(山东省自然资源厅矿产勘查技术指导中心) | Geological environment thematic data integrated management system based on position |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011053069A (en) * | 2009-09-01 | 2011-03-17 | Asahi Kasei Homes Co | Method for predicting earthquake response of building |
| CN103399344A (en) * | 2013-07-24 | 2013-11-20 | 北京师范大学 | Prediction method for predicating collapse disaster position after earthquake |
| CN105654414A (en) * | 2015-12-25 | 2016-06-08 | 浙江大学城市学院 | Urban multi-disaster risk loss evaluation system based on open source system framework and building spatial database and method thereof |
-
2018
- 2018-01-29 CN CN201810087847.6A patent/CN108376125B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011053069A (en) * | 2009-09-01 | 2011-03-17 | Asahi Kasei Homes Co | Method for predicting earthquake response of building |
| CN103399344A (en) * | 2013-07-24 | 2013-11-20 | 北京师范大学 | Prediction method for predicating collapse disaster position after earthquake |
| CN105654414A (en) * | 2015-12-25 | 2016-06-08 | 浙江大学城市学院 | Urban multi-disaster risk loss evaluation system based on open source system framework and building spatial database and method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 地震烈度与峰值加速度、峰值速度相关性研究;丁宝荣 等;《地震工程与工程振动》;20170430;第37卷(第2期);第1-11页 * |
| 考虑不确定性的地震烈度与地震动参数关系研究;丁宝荣 等;《地震工程与工程振动》;20180430;第38卷(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108376125A (en) | 2018-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110839016B (en) | Abnormal flow monitoring method, device, equipment and storage medium | |
| CN108376125B (en) | Seismic intensity evaluation method and device | |
| Mohler et al. | Self-exciting point process modeling of crime | |
| KR101880548B1 (en) | method for evaluating performance level of construction | |
| CN114611941A (en) | Landslide disaster evaluation method and system considering space dangerousness | |
| CN109934120B (en) | Step-by-step point cloud noise removing method based on space density and clustering | |
| KR102143805B1 (en) | Device for Predicting Fragility of Structures due to Disasters and the Method | |
| CN109639478A (en) | There are the method, apparatus of family relationship client, equipment and media for identification | |
| CN113538857B (en) | Landslide geological disaster early warning method, device, terminal and storage medium | |
| CN108197347B (en) | method for detecting geographic space distribution uniformity of continuous irregular area sampling points | |
| Schoenberg et al. | Description of earthquake aftershock sequences using prototype point patterns | |
| Rota et al. | Typological fragility curves from Italian earthquake damage data | |
| JPH10508109A (en) | Earthquake monitoring | |
| CN116089405A (en) | Power consumption data outlier detection and cleaning method based on DBSCAN and KNN algorithms | |
| JP2020112445A (en) | Earthquake information processing device | |
| Krivoruchko | Using linear and non-linear kriging interpolators to produce probability maps | |
| CN112986948B (en) | Building deformation monitoring method and device based on InSAR technology | |
| Han et al. | A non-destructive approach to estimate buttress volume using 3D point cloud data | |
| D’Amato et al. | Risk analysis of existing building heritage through damage assessment after L’Aquila earthquake 2009 | |
| CN111553986A (en) | Construction method and construction device of triangulation network and generation method of digital surface model | |
| CN115859569A (en) | Vector type main and aftershock risk analysis model construction and early warning method and system | |
| CN115982968B (en) | Algorithm for revealing space-time evolution difference of ground | |
| CN115311574B (en) | Building monitoring method, equipment and medium | |
| CN112084286B (en) | Spatial data processing method and device, computer equipment and storage medium | |
| Harte et al. | Quantifying the M8 algorithm: model, forecast, and evaluation |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210416 Termination date: 20220129 |