CN110046454A - Probabilistic Seismic economic loss calculation method and system - Google Patents
Probabilistic Seismic economic loss calculation method and system Download PDFInfo
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Abstract
The present invention provides a kind of Probabilistic Seismic economic loss calculation method and system.This method comprises: selection influences the earthquake zone and Potential earthquake seurce of target area, and determine seismicity model and seismicity parameters；Earthquake event data library is generated by Monte Carlo Method of Stochastic；Attenuation model is chosen, and calculates the earthquake intensity of any site in target area according to earthquake catalogue library；According to earthquake intensity and macroeconomy vulnerability model, the economic loss of target area is calculated；In conjunction with law of great number and total probability formula, the economic loss of site under the earthquake loss probability of happening and specified Probability Condition of any site in t is calculated.The system includes: model and parameter determination unit, earthquake event data library generation unit, earthquake intensity computing unit, economic loss computing unit and earthquake loss probability of happening and costing bio disturbance unit.The present invention, which considers site, repeatedly to be influenced by same earthquake intensity, provide technical support for earthquake loss prediction.
Description
Technical field
The present invention relates to the assessment of Probabilistic Seismic loss risk and earthquake loss electric powder prediction more particularly to a kind of probability
Seismic Economic Losses calculation method and system.
Background technique
Since 20th century, China accounts for about the half of global earthquakes death toll, with the continuous social and economic development, the whole world
The trend increased rapidly is presented in earthquake disaster.Calamity is formed after earthquake is superimposed with natural and humanistic environment fragility, degrees of exposure
Evil risk, and then disaster may occur.Therefore earthquake disaster mitigation risk key is risk identification assessment.How earthquake wind is taken precautions against
Nearly, seismic damage loss, which is effectively reduced, becomes one that the whole mankind faces difficult task for a long time.
The method that traditional seismic damage loss forecast analysis generallys use vulnerability classification inventory, it is desirable that built to studying in area
Facility structure carries out investigation in detail and provides earthquake damage matrix.But the fast development of modern society is faced, so that conventional method is providing
Material is collected and is updated aspect and all there is limitation.
In the seismic damage loss forecast analysis of macroeconomy vulnerability model, such as using GDP loss as index, generally examining
There is probability level when considering seismic risk, but when analyzing earthquake and economic loss, do not fully consider these factors
Probability characteristics.In addition, loss caused by certain earthquake intensity is defined as in currently a popular expected damage cost appraisal procedure
The following place does not account in the given time limit, site may be repeatedly by same by average loss caused by one earthquake
The influence of one earthquake intensity.The loss of earthquake is often underestimated in this way, and when the time limit of especially given assessment is longer, inaccuracy will
It dramatically increases.
Therefore, it is necessary to fully consider the probability level in seismic damage loss prediction, to study earthquake loss probability of happening and ground
The estimation method for shaking economic loss is this field technical problem urgently to be resolved.
Summary of the invention
For solve it is existing in the prior art " because do not consider a site may repeatedly by the influence of same earthquake intensity due to make
At Evaluation of Earthquake result inaccuracy " the problem of, the present invention provides a kind of method that Probabilistic Seismic economic loss calculates and is
System, it is contemplated that in the prediction time limit, site may be predicted repeatedly by same earthquake intensity or the influence of different earthquake intensitys for earthquake loss
With Earthquake risk prevention and control, effectively mitigates Earthquake risk and provide good technical support.
In a first aspect, the present invention provides a kind of Probabilistic Seismic economic loss calculation method, this method comprises:
Step 1, selection influence the earthquake zone and Potential earthquake seurce of target area, and determine that the earthquake of the target area is living
Dynamic property model and seismicity parameters；
Step 2, according to the seismicity model and the seismicity parameters, pass through Monte Carlo stochastic simulation
Method generates earthquake event data library；
Step 3 chooses attenuation model, and the ground of any site in the target area is calculated according to the earthquake catalogue library
Shake earthquake intensity；
Step 4, according to the earthquake intensity and macroeconomy vulnerability model, calculate the economic loss of target area；
Step 5, in conjunction with law of great number and total probability formula, calculate any site in t to be predicted earthquake loss occur
The economic loss of site under probability and specified Probability Condition.
Further, the step 2 specifically includes:
Step 2.1 generates random number r, r ∈ [0,1] using horse spy's Saite rotation algorithm；
Step 2.2, according to the movable Annual Occurrence Rates of earthquake and earthquake frequency cumulative distribution function, obtain primary
The frequency k of earthquake in t to be predicted is tested, the earthquake frequency cumulative distribution function is formula (1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；
Step 2.3, according to magnitude cumulative distribution function, obtain the magnitude distribution of k earthquake, the magnitude is tired
Product distribution function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；
Step 2.4, according to seismic activity in the spatial probability distribution function of Potential earthquake seurce, obtain belonging to each earthquake
Latent source number, repeats k times, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves earthquakes are set different potential
It is uneven distribution, m between focal areajEarthquake magnitude shelves earthquake falls into the spatial summation probability-distribution function of first of Potential earthquake seurce
For formula (3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjEarthquake magnitude shelves
Earthquake year probability of happening；F(l,mj)=r；
It is step 2.5, equally distributed in Potential earthquake seurce based on earthquake centre it is assumed that by random sampling, generate value position
Between [0,1] and meet the equally distributed random number of two-dimensional space to (r1, r2), and according to the random number to (r1, r2)
Earthquake centre latitude and longitude coordinates (the x of i-th earthquake is generated according to formula (4) and formula (5)i,yi), if the earthquake centre latitude and longitude coordinates (xi,
yi) be located inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates (xi,yi) epicentral location as i-th earthquake, weight
It is k times multiple, obtain the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in x-axis side
Upward maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in y-axis side
Upward maximum coordinates and min coordinates；
If step 2.6, Potential earthquake seurce are decayed, the probability at major axis orientation angle is greater than the random number r, it is determined that earthquake side
Parallactic angle is the first preset direction θ1；Otherwise, it determines seismic location angle is the second preset direction θ2；
Step 2.7 repeats step 2.1~step 2.6 until meeting default seismic activity simulated experiment times Nr, generation contains
There is NrThe earthquake event data library of group seismic events.
Further, the step 3 specifically includes:
Step 3.1 chooses earthquake motion elliptical decay model, and the earthquake motion elliptical decay model is formula (6) to formula (8):
Wherein, C1~C7For the attenuation coefficient of long axis；D1~D7For the attenuation coefficient of short axle；RaTransverse is long；RbIt is ellipse
Circle short axle is long；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
Step 3.2, joint type (6) to formula (8), so thatWith dichotomy, by the area where the zero point of equation
Between be constantly divided into two, so that the section newly obtained is constantly become smaller, it is oval to acquire decaying for two endpoint Step wise approximation non trivial solutions
Long axis length and minor axis length, then long axis length and earthquake magnitude size are brought into formula (7), are obtained any under each seismic events
The earthquake intensity of site.
Further, the step 4 specifically includes:
Step 4.1 calculates loss late F (I, GDP) according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is amendment system
Number；
Step 4.2 calculates economic loss LOSS according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
Further, the step 5 specifically includes:
Step 5.1 is set in the earthquake event data library comprising Nr group seismic events, sets g group seismic events
Include kgA seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, site s is in t to be predicted
Earthquake sum be Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10And it is less than LOSS10+1Number be N
(LOSS10+1≥LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
Step 5.2 generates earthquake loss when one earthquake occurs according to site s in formula (14) calculating t to be predicted
LOSS1iIt is greater than the set value LOSS10And it is less than setting value LOSS10+1Probability be p (LOSS10+1>=LOSS1i >=LOSS10:
Step 5.3 calculates in t to be predicted site s in the earthquake for occurring to generate when earthquake more than once according to formula (15)
Lose LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1>=LOSS1con >=LOSS10:
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
Step 5.4 calculates g group seismic events to the maximum value of the site s earthquake loss generated according to formula (16)
LOSSmax(g):
Step 5.5 repeats step 5.4, obtains NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
Second aspect, the embodiment of the present invention also provide a kind of Probabilistic Seismic economic loss computing system, which includes:
Model and parameter determination unit for selecting the earthquake zone and Potential earthquake seurce of influence target area, and determine institute
State the seismicity model and seismicity parameters of target area；
Earthquake event data library generation unit, for being joined according to the seismicity model and the seismicity
Number generates earthquake event data library by Monte Carlo Method of Stochastic；
Earthquake intensity computing unit calculates the target area for choosing attenuation model, and according to the earthquake catalogue library
The earthquake intensity of any site in domain；
Economic loss computing unit, for calculating target area according to the earthquake intensity and macroeconomy vulnerability model
The economic loss in domain；
Earthquake loss probability of happening computing unit calculates in t to be predicted for combining law of great number and total probability formula
The economic loss of site under the earthquake loss probability of happening and specified Probability Condition of any site.
Further, earthquake event data library generation unit specifically includes:
Random number generation module, for generating random number r, r a ∈ [0,1] using horse spy Saite rotation algorithm；
Earthquake frequency computing module, for being accumulated according to the movable Annual Occurrence Rates of earthquake and earthquake frequency
Distribution function is once tested the frequency k of earthquake in t to be predicted, the earthquake frequency cumulative distribution function
For formula (1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；
Magnitude distribution computing module, for obtaining the magnitude distribution of k earthquake according to magnitude cumulative distribution function,
The magnitude cumulative distribution function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；
Latent source position computing module, for, in the spatial probability distribution function of Potential earthquake seurce, being obtained according to seismic activity
Latent source number, repeats k times belonging to each earthquake, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves are set
Earthquake is uneven distribution, m between different Potential earthquake seurcesjThe space that earthquake magnitude shelves earthquake falls into first of Potential earthquake seurce is tired
Product probability-distribution function is formula (3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjEarthquake magnitude shelves
Earthquake year probability of happening；F(l,mj)=r；
Spatial position computing module, for equally distributed it is assumed that by taking out at random in Potential earthquake seurce based on earthquake centre
Sample, generate value between [0,1] and meet the equally distributed random number of two-dimensional space to (r1, r2), and according to it is described with
Machine number generates the earthquake centre latitude and longitude coordinates (x of i-th earthquake to (r1, r2) according to formula (4) and formula (5)i,yi), if the earthquake centre passes through
Latitude coordinate (xi,yi) be located inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates (xi,yi) as i-th earthquake
Epicentral location repeats k times, obtains the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in x-axis side
Upward maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in y-axis side
Upward maximum coordinates and min coordinates；
Seismic location angle determining module, if judgement know the probability at Potential earthquake seurce decaying major axis orientation angle be greater than it is described with
Machine number r, it is determined that seismic location angle is the first preset direction θ1；Otherwise, it determines seismic location angle is the second preset direction θ2；
Number of repetition judgment module, for judging whether current number of repetition reaches default seismic activity simulated experiment number
NrIf then generating and containing NrThe earthquake event data library of group seismic events.
Further, the earthquake intensity computing unit specifically includes:
Earthquake motion elliptical decay model determining module, for choosing earthquake motion elliptical decay model, the earthquake motion is oval
Attenuation model is formula (6) to formula (8):
Wherein, C1~C7Attenuation coefficient, D for long axis1~D7For the attenuation coefficient of short axle；RaTransverse length, RbIt is ellipse
Circle short axle is long；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
Earthquake intensity determining module is used for joint type (6) to formula (8), so thatWith dichotomy, by equation
Zero point where section be constantly divided into two, so that the section newly obtained is constantly become smaller, two endpoint Step wise approximation equations
Solution acquires the elliptical long axis length of decaying and minor axis length, then long axis length and earthquake magnitude size is brought into formula (7), obtains every
The earthquake intensity of any site under one seismic events.
Further, the economic loss rate computing unit specifically includes:
Loss late computing module, for calculating loss late F (I, GDP) according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is amendment system
Number；
Economic loss computing module, for calculating economic loss LOSS according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
Further, the earthquake loss probability of happening and costing bio disturbance unit specifically include:
Parameter initialization module is set in the earthquake event data library comprising NrGroup seismic events, setting g group ground
Shake event includes kgA seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, site s is to pre-
The earthquake sum surveyed in t is Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10And it is less than setting value LOSS10+1's
Number is N (LOSS10+1≥LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
First probability evaluation entity is produced for calculating site s in t to be predicted according to formula (14) when one earthquake occurs
Radix Rehmanniae shock loss LOSS1iIt is greater than the set value LOSS10And it is less than setting value LOSS10+1Probability be p (LOSS10+1≥LOSS1i≥
LOSS10):
Second probability evaluation entity, for being generated according to site s in formula (15) prediction t when earthquake more than once occurs
Earthquake loss LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1≥LOSS1con≥LOSS10):
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
First-loss computing module, for calculating the earthquake loss that g group seismic events generate site s according to formula (16)
Maximum value LOSSmax(g):
Second costing bio disturbance module, for obtaining NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
Beneficial effects of the present invention:
Probabilistic Seismic economic loss calculation method and system provided by the invention are that a kind of loss to tally with the actual situation is commented
Valence method and system, it is contemplated that in the prediction time limit, site may be repeatedly by the influence of same earthquake intensity, by Monte Carlo simulation
The seismic events being calculated are influenced field and are brought into the macroeconomy vulnerability mould lost with GDP GDP as index
Type provides Earthquake Macro economic loss probability of happening figure.It is earthquake loss prediction and Earthquake risk prevention and control to effectively mitigate
Earthquake risk provides good technical support.Meanwhile this system can also set up defences against insuring and provide foundation for earthquake resistant engineering, provide
Region different anti-seismic is set up defences possible economic loss distribution under probability.This method can also be applied to the risk prevention systems such as seismic sea wave and
In earthquake insurance.
Detailed description of the invention
Fig. 1 is the flow diagram of Probabilistic Seismic economic loss calculation method provided in an embodiment of the present invention；
Fig. 2 is the structural schematic diagram of Probabilistic Seismic economic loss computing system provided in an embodiment of the present invention；
Fig. 3 is the structural schematic diagram of earthquake event data library provided in an embodiment of the present invention generation unit；
Fig. 4 is 30 groups of earthquake event data libraries provided in an embodiment of the present invention schematic diagram；
Fig. 5 is that the loss of GDP caused by 50 Annual exceeding probability, 10% Baoji Area earthquake provided in an embodiment of the present invention is pre-
Survey distribution map；
Fig. 6 is the probability of happening schematic diagram provided in an embodiment of the present invention that Probabilistic Seismic loss appraisal is carried out to Baoji.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, technical solution in the embodiment of the present invention are explicitly described, it is clear that described embodiment is a part of the invention
Embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making wound
Every other embodiment obtained under the premise of the property made labour, shall fall within the protection scope of the present invention.
As shown in Figure 1, the embodiment of the present invention provides a kind of Probabilistic Seismic economic loss calculation method, comprising the following steps:
S101, selection influence the earthquake zone and Potential earthquake seurce of target area, and determine that the earthquake of the target area is living
Dynamic property model and seismicity parameters；
S102, according to the seismicity model and the seismicity parameters, pass through Monte Carlo stochastic simulation
Method generates earthquake event data library；
S103, attenuation model is chosen, and calculates the ground of any site in the target area according to the earthquake catalogue library
Shake earthquake intensity；
S104, according to the earthquake intensity and macroeconomy vulnerability model, calculate the economic loss of target area；
S105, in conjunction with law of great number and total probability formula, calculate any site in t to be predicted earthquake loss occur it is general
The economic loss of site under rate and specified Probability Condition.
Using GDP loss generally to consider ground in the seismic damage loss forecast analysis of the macroeconomy vulnerability model of index
There is probability level when shaking risk, but when analyzing earthquake and economic loss, do not fully consider the general of these factors
Rate feature.There are following two aspect is insufficient for existing prediction analysis method: first, based on the formula of full probability, establish
The probability analysis model of region economic loss of seismic damage prediction, but in formulation process, economic loss is influenced in order to obtain
Relationship between element has done many simplification with averagely to formula, project evaluation precision is made to cause a deviation in practical applications.
Second, the place pole often obtained both at home and abroad with the outcross probability difference of adjacent earthquake intensity in the work of seismic damage loss evaluation studies
It is worth the earthquake intensity probability of happening that earthquake intensity probability replaces place, is not matched with the expression way of earthquake motion in vulnerability.Easily
A possibility that damage property is generally represented in the various extent of the destruction under each earthquake intensity or earthquake motion.Probabilistic seismic hazard analysis
The risk that method has a possibility that maximum ground motion parameter of Probabilistic extremely occurs by calculating place, from mathematics
For upper, one is the probability for surmounting a certain Earthquake Intensity, and one is the loss late under some Earthquake Intensity, makes to succeed in one's scheme
Obtained penalty values or probability of happening are relatively low.
Insufficient, the Probabilistic Seismic economic loss calculation method provided by the invention for above-mentioned two aspect,
It is a kind of loss assessment method to tally with the actual situation, it is contemplated that in the prediction time limit, site may be repeatedly by same earthquake intensity
Influence, for earthquake loss prediction and Earthquake risk prevention and control, effectively mitigate Earthquake risk provide good technical support.It should
Method can also be applied in the risk prevention systems and earthquake insurance such as seismic sea wave.
On the basis of the above embodiments, the present invention provides another embodiment, and process is specific as follows:
S201, selection influence the earthquake zone and Potential earthquake seurce of target area, and determine that the earthquake of the target area is living
Dynamic property model and seismicity parameters；
S202, earthquake event data library is generated.It specifically includes:
S2021, random number r, r a ∈ [0,1] is generated using horse spy's Saite rotation algorithm；
S2022, according to the movable Annual Occurrence Rates of earthquake and earthquake frequency cumulative distribution function, obtain primary reality
The frequency k of earthquake in t to be predicted is tested, the earthquake frequency cumulative distribution function is formula (1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；
S2023, according to magnitude cumulative distribution function, obtain the magnitude distribution of k earthquake, the magnitude accumulation
Distribution function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；
S2024, according to seismic activity Potential earthquake seurce spatial probability distribution function, obtain belonging to each earthquake dive
Source number, repeats k times, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves earthquakes are set in different potential shakes
It is uneven distribution, m between source regionjThe spatial summation probability-distribution function that earthquake magnitude shelves earthquake falls into first of Potential earthquake seurce is
Formula (3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjEarthquake magnitude shelves
Earthquake year probability of happening；F(l,mj)=r；
It is S2025, equally distributed in Potential earthquake seurce based on earthquake centre it is assumed that by random sampling, it generates value and is located at
Between [0,1] and meet the equally distributed random number of two-dimensional space to (r1, r2), and (r1, r2) is pressed according to the random number
Illuminated (4) and formula (5) generate the earthquake centre latitude and longitude coordinates (x of i-th earthquakei,yi), if the earthquake centre latitude and longitude coordinates (xi,yi)
Inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates (xi,yi) epicentral location as i-th earthquake, repeat k
It is secondary, obtain the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in x-axis side
Upward maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in y-axis side
Upward maximum coordinates and min coordinates；
If S2026, Potential earthquake seurce are decayed, the probability at major axis orientation angle is greater than the random number r, it is determined that seismic location
Angle is the first preset direction θ1；Otherwise, it determines seismic location angle is the second preset direction θ2；
S2027, S2021~S2026 is repeated until meeting default seismic activity simulated experiment times Nr, generate and contain NrGroup
The earthquake event data library of seismic events.
S203, the earthquake intensity for calculating any site.It specifically includes:
S2031, earthquake motion elliptical decay model is chosen, the earthquake motion elliptical decay model is formula (6) to formula (8):
Wherein, C1~C7For the attenuation coefficient of long axis；D1~D7For the attenuation coefficient of short axle；RaTransverse is long；RbIt is ellipse
Circle short axle is long；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
S2032, joint type (6) to formula (8), so thatWith dichotomy, by the section where the zero point of equation
It is constantly divided into two, the section newly obtained is made constantly to become smaller, it is elliptical to acquire decaying for two endpoint Step wise approximation non trivial solutions
Long axis length and minor axis length, then long axis length and earthquake magnitude size are brought into formula (7), obtain any field under each seismic events
The earthquake intensity of point.
S204, the economic loss for calculating target area.It specifically includes:
S2041, loss late F (I, GDP) is calculated according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is amendment system
Number；
S2042, economic loss LOSS is calculated according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
Specifically, as an embodiment, it is contemplated that the factors such as this area's economic growth, by GDP reduction to certain year
Economic data (GDP) can be evenly distributed to the node of region grid according to 0.05*0.05 degree grid by the case where fixed price
On, it brings macroeconomy vulnerability model (i.e. formula (9) and formula (10)) into, provides Earthquake Macro economic loss probability of happening figure.
S205, earthquake loss under the earthquake loss probability of happening and specified Probability Condition of any site is calculated.Specific packet
It includes:
S2051, it is set in the earthquake event data library comprising NrGroup seismic events, set g group seismic events packet
Containing kgA seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, site s is in t to be predicted
Earthquake sum is Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10And it is less than LOSS10+1Number be N (LOSS10+1
≥LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
S2052, earthquake loss is generated when one earthquake occurs according to site s in formula (14) calculating t to be predicted
LOSS1iIt is greater than the set value LOSS10And it is less than setting value LOSS10+1Probability be p (LOSS10+1>=LOSS1i >=LOSS10:
S2053, to calculate site s in t to be predicted according to formula (15) damaged on the ground for occurring to generate when earthquake more than once
Lose LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1>=LOSS1con >=LOSS10:
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
S2054, g group seismic events are calculated to the maximum value LOSS of the site s earthquake loss generated according to formula (16)max
(g):
S2055, step S2054 is repeated, obtains NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
It should be noted that the seismicity parameters and earthquake zone and Latent focal region ginseng in the embodiment of the present invention
Number using " Earthquake In China moves parameter zoning map " (GB18306-2015) relevant parameter or needs voluntarily to unite according to real work
Meter.The earthquake year Annual occurence rate, lower limit of earthquake magnitude, Upper Magnitude and can by acquisition historical earthquake using statistical method acquisition.
The attenuation relation coefficient can be fitted by observatory data and be obtained；The earthquake zone range of acquisition is in combination with actual requirement of engineering
Setting.GDP data can be obtained from statistical yearbook.
As shown in Fig. 2, the embodiment of the present invention also provides a kind of Probabilistic Seismic economic loss computing system, which includes:
Model and parameter determination unit 201, earthquake event data library generation unit 202, earthquake intensity computing unit 203, economic loss
Computing unit 204 and earthquake loss probability of happening and costing bio disturbance unit 205.Wherein:
The earthquake zone and Potential earthquake seurce that model and parameter determination unit 201 are used to select to influence target area, and determine
The seismicity model and seismicity parameters of the target area；Earthquake event data library generation unit 202 is used for root
According to the seismicity model and the seismicity parameters, seismic events are generated by Monte Carlo Method of Stochastic
Database；Earthquake intensity computing unit 203 calculates the target area for choosing attenuation model, and according to the earthquake catalogue library
The earthquake intensity of any site in domain；Economic loss computing unit 204 is used for according to the earthquake intensity and macroeconomy rapid wear
Property model, calculates the economic loss of target area；Earthquake loss probability of happening computing unit 205 is in conjunction with law of great number and entirely
New probability formula calculates the warp of site under the earthquake loss probability of happening and specified Probability Condition of any site in t to be predicted
Ji loss.
As an embodiment, as shown in figure 3, earthquake event data library generation unit 202 specifically includes: with
Machine number generation module 2021, earthquake frequency computing module 2022, magnitude distribution computing module 2023, latent source position calculate mould
Block 2024, spatial position computing module 2025, seismic location angle determining module 2026 and number of repetition judgment module 2027.Its
In:
Random number generation module 2021, for generating random number r, r ∈ [0,1] using horse spy Saite rotation algorithm；Ground
Frequency computing module 2022 is shaken to be used for according to the movable Annual Occurrence Rates of earthquake and earthquake frequency cumulative distribution letter
Number, is once tested the frequency k of earthquake in t to be predicted, and the earthquake frequency cumulative distribution function is formula
(1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；Magnitude distribution computing module 2023 is used for according to magnitude
Cumulative distribution function obtains the magnitude distribution of k earthquake, and the magnitude cumulative distribution function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；Latent source
Position computation module 2024 is used to obtain each earthquake institute in the spatial probability distribution function of Potential earthquake seurce according to seismic activity
The latent source number belonged to, repeats k times, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves earthquakes are set in difference
It is uneven distribution, m between Potential earthquake seurcejEarthquake magnitude shelves earthquake falls into the spatial summation probability distribution of first of Potential earthquake seurce
Function is formula (3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjEarthquake magnitude shelves
Earthquake year probability of happening；F(l,mj)=r；Spatial position computing module 2025 is used for uniform in Potential earthquake seurce based on earthquake centre
Distribution it is assumed that by random sampling, generate value and between [0,1] and meet the equally distributed random number pair of two-dimensional space
(r1, r2), and (r1, r2) is sat according to the earthquake centre longitude and latitude that formula (4) and formula (5) generate i-th earthquake according to the random number
Mark (xi,yi), if the earthquake centre latitude and longitude coordinates (xi,yi) be located inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates
(xi,yi) epicentral location as i-th earthquake, it repeats k times, obtains the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in x-axis side
Upward maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in y-axis side
Upward maximum coordinates and min coordinates；If Potential earthquake seurce decaying long axis side is known in the judgement of seismic location angle determining module 2026
The probability of parallactic angle is greater than the random number r, it is determined that seismic location angle is the first preset direction θ1；Otherwise, it determines seismic location
Angle is the second preset direction θ2；Number of repetition judgment module 2027 is for judging it is living whether current number of repetition reaches default earthquake
Dynamic model draft experiment times NrIf then generating and containing NrThe earthquake event data library of group seismic events.
Specifically, primary complete seismic activity simulation process are as follows: random number generation module 2021 generates a random number r,
Random number r is respectively sent to earthquake number generation technique module 2022, latent source position computing module 2024, spatial position meter
Calculate module 2025 and seismic location angle determining module 2026.Earthquake frequency computing module 2022 utilizes random number r, and combines
Earthquake frequency is calculated in Annual Occurrence Rates and earthquake frequency cumulative distribution function.Magnitude distribution computing module
2023 are calculated the earthquake magnitude of each earthquake；Latent source position computing module 2024 is living in conjunction with earthquake according to the random number r received
Move the latent source position that each earthquake is calculated in the spatial probability distribution function in Potential earthquake seurce.Spatial position computing module
2025 determine the spatial position that seismic events occur according to the random number r random sampling received.Seismic location angle determining module
2026 determine the seismic location angle of each earthquake according to the random number r received.In this way, being directed to each earthquake, the earthquake is established
Earthquake event data, for example, an earthquake event data include: earthquake occur time, the earthquake magnitude of earthquake, earthquake latent source
Position, the spatial position of earthquake, earthquake the information such as azimuth.In order to improve Probabilistic Seismic Economic loss assessment result
Accuracy rate needs to improve earthquake event data library as much as possible, it is therefore desirable to repeat simulation seismic events as much as possible.Setting
Seismic activity number realization Nr(Nr=100 ten thousand), and number of repetition judgment module is then used to judge whether present day analog number reaches Nr
It is secondary, contain N if so, generatingrThe earthquake event data library of a seismic events.If it is not, system then continues according to above-mentionedly
Shake activity mimics process simulation seismic events.
As an embodiment, the earthquake intensity computing unit 203 specifically includes: earthquake motion elliptical decay model
Determining module and earthquake intensity determining module.Wherein:
For choosing earthquake motion elliptical decay model, the earthquake motion ellipse declines earthquake motion elliptical decay model determining module
Subtracting model is formula (6) to formula (8):
(8) wherein, C1~C7Attenuation coefficient, D for long axis1~D7For the attenuation coefficient of short axle；RaTransverse length, RbFor
Ellipse short shaft is long；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
Earthquake intensity determining module is used for joint type (6) to formula (8), so thatWith dichotomy, by equation
Section where zero point is constantly divided into two, and the section newly obtained is made constantly to become smaller, two endpoint Step wise approximation non trivial solutions,
The elliptical long axis length of decaying and minor axis length are acquired, then long axis length and earthquake magnitude size are brought into formula (7), is obtained eachly
The earthquake intensity of any site under shake event.
As an embodiment, the economic loss rate computing unit 204 specifically includes: loss late computing module and
Economic loss computing module.Wherein:
Loss late computing module, for calculating loss late F (I, GDP) according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is amendment system
Number；
Economic loss computing module, for calculating economic loss LOSS according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
Mode as an embodiment, the earthquake loss probability of happening and costing bio disturbance unit 205 specifically include: ginseng
Number initialization module, the first probability evaluation entity, the second probability evaluation entity, first-loss computing module and the second costing bio disturbance
Module.Wherein:
Parameter initialization module is set in the earthquake event data library comprising NrGroup seismic events, setting g group ground
Shake event includes kgA seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, site s is to pre-
The earthquake sum surveyed in t is Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10, it is less than LOSS10+1Number be N
(LOSS10+1≥LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
First probability evaluation entity is used to calculate site s in t to be predicted according to formula (14) and produce when one earthquake occurs
Radix Rehmanniae shock loss LOSS1iIt is greater than the set value LOSS10And it is less than LOSS10+1Probability be p (LOSS10+1≥LOSS1i≥
LOSS10):
Second probability evaluation entity is used to generate according to site s in formula (15) prediction t when earthquake more than once occurs
Earthquake loss LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1≥LOSS1con≥LOSS10):
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
First-loss computing module, for calculating the earthquake loss that g group seismic events generate site s according to formula (16)
Maximum value LOSSmax(g):
Second costing bio disturbance module, for obtaining NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
In order to verify the validity of Probabilistic Seismic economic loss calculation method and system provided by the invention, the present invention is also mentioned
For following experiment.
Baoji Area is mainly influenced by 7 earthquake zones.Its Annual occurence rate and each latent source region parameter can be according to earthquakes
Zoning map obtains.
This experiment uses Poisson model, in conjunction with the statistical method of five generation figures and as a result, the earthquake zone as provided in table 1
Parameter.According to medium propagation characteristic subregion Statistic features and Seismotectonic Environment, the earthquake for being suitable for Guanzhong basin area is obtained
Dynamic attenuation relation, such as formula (17).
1 earthquake zone activity parameters of table
Table 2 is with the economic vulnerability analysis result of earthquake that macro-performance indicator (GDP) characterizes
From Shaanxi Statistics Bureau of Shanxi Province website (http://www.shaanxitj.gov.cn/), Shaanxi Province's statistical yearbook
(http://www.shaanxitj.gov.cn/upload/2018/7/zk/indexce.htm) is collected into Shaanxi Province in 2016
The GDP data in each small towns in Baoji such as table 2.Consider the factors such as Shaanxi future 50 years population, economic growth, GDP reduction is arrived
Economic data (GDP) and population distribution data are evenly distributed to by the case where fixed price in 2016 according to 0.05*0.05 degree grid
On the node of region grid, macroscopical vulnerability model is brought into, obtaining earthquake according to formula (10) may cause administrative area
Loss.
Step S201~S203 according to the present invention generates simulation earthquake catalogue, wherein 30 groups of earthquake catalogues are as shown in the figure.According to
According to step S204~S205 of the present invention, the loss of GDP caused by the earthquake of Baoji Area hair under the region different set GDP threshold value
Raw probabilistic forecasting distribution map result is as shown in Figure 4.Fig. 5 is that GDP caused by 50 Annual exceeding probability, 10% Baoji Area earthquake is damaged
Lose (hundred million yuan) prediction distribution figure.In Fig. 6: it be 0.27 < GDP < 200,000,000, c is 2 < GDP < 3.5 hundred million that a, which is GDP < 0.27 hundred million, b,.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. Probabilistic Seismic economic loss calculation method characterized by comprising
Step 1, selection influence the earthquake zone and Potential earthquake seurce of target area, and determine the seismicity of the target area
Model and seismicity parameters；
Step 2, according to the seismicity model and the seismicity parameters, pass through Monte Carlo Method of Stochastic
Generate earthquake event data library；
Step 3 chooses attenuation model, and strong according to the earthquake that the earthquake catalogue library calculates any site in the target area
Degree；
Step 4, according to the earthquake intensity and macroeconomy vulnerability model, calculate the economic loss of target area；
Step 5, in conjunction with law of great number and total probability formula, calculate the earthquake loss probability of happening of any site in t to be predicted
And under specified Probability Condition site economic loss.
2. the method according to claim 1, wherein the step 2 specifically includes:
Step 2.1 generates random number r, r ∈ [0,1] using horse spy's Saite rotation algorithm；
Step 2.2, according to the movable Annual Occurrence Rates of earthquake and earthquake frequency cumulative distribution function, once tested
The frequency k of earthquake in t to be predicted, the earthquake frequency cumulative distribution function are formula (1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；
Step 2.3, according to magnitude cumulative distribution function, obtain the magnitude distribution of k earthquake, the magnitude iterated integral
Cloth function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；
Step 2.4, according to seismic activity in the spatial probability distribution function of Potential earthquake seurce, obtain latent source belonging to each earthquake
Number repeats k times, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves earthquakes are set in different Latent focal regions
It is uneven distribution, m between areajThe spatial summation probability-distribution function that earthquake magnitude shelves earthquake falls into first of Potential earthquake seurce is formula
(3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjThe earthquake of earthquake magnitude shelves
Year probability of happening；F(l,mj)=r；
It is step 2.5, equally distributed in Potential earthquake seurce based on earthquake centre it is assumed that by random sampling, generate value be located at [0,
1] between and meet the equally distributed random number of two-dimensional space to (r1, r2), and according to the random number to (r1, r2) according to formula
(4) and formula (5) generate i-th earthquake earthquake centre latitude and longitude coordinates (xi,yi), if the earthquake centre latitude and longitude coordinates (xi,yi) be located at
Inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates (xi,yi) epicentral location as i-th earthquake, it repeats k times, obtains
To the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in the direction of the x axis
Maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in the y-axis direction
Maximum coordinates and min coordinates；
If step 2.6, Potential earthquake seurce are decayed, the probability at major axis orientation angle is greater than the random number r, it is determined that seismic location angle
For the first preset direction θ1；Otherwise, it determines seismic location angle is the second preset direction θ2；
Step 2.7 repeats step 2.1~step 2.6 until meeting default seismic activity simulated experiment times Nr, generate and contain Nr
The earthquake event data library of group seismic events.
3. the method according to claim 1, wherein the step 3 specifically includes:
Step 3.1 chooses earthquake motion elliptical decay model, and the earthquake motion elliptical decay model is formula (6) to formula (8):
Wherein, C1~C7For the attenuation coefficient of long axis；D1~D7For the attenuation coefficient of short axle；RaTransverse is long；RbIt is oval short
Axial length；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
Step 3.2, joint type (6) to formula (8), so thatWith dichotomy, not by the section where the zero point of equation
It is divided into two disconnectedly, the section newly obtained is made constantly to become smaller, two endpoint Step wise approximation non trivial solutions acquire the elliptical length of decaying
Shaft length and minor axis length, then long axis length and earthquake magnitude size are brought into formula (7), obtain any site under each seismic events
Earthquake intensity.
4. the method according to claim 1, wherein the step 4 specifically includes:
Step 4.1 calculates loss late F (I, GDP) according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is correction factor；
Step 4.2 calculates economic loss LOSS according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
5. the method according to claim 1, wherein the step 5 specifically includes:
Step 5.1 is set in the earthquake event data library comprising NrGroup seismic events, setting g group seismic events include kg
A seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, ground of the site s in t to be predicted
Shake sum is Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10And it is less than LOSS10+1Number be N (LOSS10+1≥
LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
Step 5.2 generates earthquake loss LOSS1 when one earthquake occurs according to site s in formula (14) calculating t to be predictediGreatly
In setting value LOSS10And it is less than setting value LOSS10+1Probability be p (LOSS10+1>=LOSS1i >=LOSS10:
Step 5.3 calculates in t to be predicted site s in the earthquake loss for occurring to generate when earthquake more than once according to formula (15)
LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1>=LOSS1con >=LOSS10:
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
Step 5.4 calculates g group seismic events to the maximum value LOSS of the site s earthquake loss generated according to formula (16)max
(g):
Step 5.5 repeats step 5.4, obtains NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
6. Probabilistic Seismic economic loss computing system characterized by comprising
Model and parameter determination unit for selecting the earthquake zone and Potential earthquake seurce of influence target area, and determine the mesh
Mark the seismicity model and seismicity parameters in region；
Earthquake event data library generation unit, for leading to according to the seismicity model and the seismicity parameters
It crosses Monte Carlo Method of Stochastic and generates earthquake event data library；
Earthquake intensity computing unit calculates in the target area for choosing attenuation model, and according to the earthquake catalogue library
The earthquake intensity of any site；
Economic loss computing unit, for calculating target area according to the earthquake intensity and macroeconomy vulnerability model
Economic loss；
Earthquake loss probability of happening and costing bio disturbance unit calculate t to be predicted for combining law of great number and total probability formula
The economic loss of site under the earthquake loss probability of happening and specified Probability Condition of interior any site.
7. system according to claim 6, which is characterized in that earthquake event data library generation unit specifically includes:
Random number generation module, for generating random number r, r ∈ [0,1] using horse spy Saite rotation algorithm；
Earthquake frequency computing module, for according to the movable Annual Occurrence Rates of earthquake and earthquake frequency cumulative distribution
Function, is once tested the frequency k of earthquake in t to be predicted, and the earthquake frequency cumulative distribution function is formula
(1):
Wherein, v0For Annual Occurrence Rates；P (n≤k)=r；
Magnitude distribution computing module, it is described for obtaining the magnitude distribution of k earthquake according to magnitude cumulative distribution function
Magnitude cumulative distribution function is formula (2):
Wherein, m0For lower limit of earthquake magnitude, muzFor Upper Magnitude, β=2.3b, b are the coefficient of target area G-R relationship；
Latent source position computing module, for, in the spatial probability distribution function of Potential earthquake seurce, being obtained each according to seismic activity
Latent source number, repeats k times belonging to earthquake, obtains latent source number belonging to k earthquake；Wherein, different earthquake magnitude shelves earthquakes are set
It is uneven distribution, m between different Potential earthquake seurcesjThe spatial summation that earthquake magnitude shelves earthquake falls into first of Potential earthquake seurce is general
Rate distribution function is formula (3):
Wherein,For mjEarthquake magnitude shelves the are the Seismic annual occurrence rate of a Potential earthquake seurce of l '；Indicate mjThe earthquake of earthquake magnitude shelves
Year probability of happening；F(l,mj)=r；
Spatial position computing module is used for equally distributed in Potential earthquake seurce based on earthquake centre it is assumed that passing through random sampling, raw
Between [0,1] and meet the equally distributed random number of two-dimensional space to (r1, r2) at value, and according to the random number pair
(r1, r2) generates the earthquake centre latitude and longitude coordinates (x of i-th earthquake according to formula (4) and formula (5)i,yi), if the earthquake centre longitude and latitude is sat
Mark (xi,yi) be located inside Potential earthquake seurce, then by the earthquake centre latitude and longitude coordinates (xi,yi) as i-th earthquake earthquake centre position
It sets, repeats k times, obtain the epicentral location of k seismic events；
xi=(x_max-x_min) r1+x_min (4)
yi=(y_max-y_min) r2+y_min (5)
Wherein, i=1,2 ..., k；X_max and x_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in the direction of the x axis
Maximum coordinates and min coordinates；Y_max and y_min respectively indicates the minimum circumscribed rectangle of Potential earthquake seurce in the y-axis direction
Maximum coordinates and min coordinates；
Seismic location angle determining module, if judgement knows that the probability at Potential earthquake seurce decaying major axis orientation angle is greater than the random number
R, it is determined that seismic location angle is the first preset direction θ1；Otherwise, it determines seismic location angle is the second preset direction θ2；
Number of repetition judgment module, for judging whether current number of repetition reaches default seismic activity simulated experiment times NrIf
It is that generation contains NrThe earthquake event data library of group seismic events.
8. system according to claim 6, which is characterized in that the earthquake intensity computing unit specifically includes:
Earthquake motion elliptical decay model determining module, for choosing earthquake motion elliptical decay model, the earthquake motion elliptical decay
Model is formula (6) to formula (8):
Wherein, C1~C7Attenuation coefficient, D for long axis1~D7For the attenuation coefficient of short axle；RaTransverse length, RbIt is oval short
Axial length；ε is uncertainty；Indicate long axis direction earthquake intensity；Indicate short-axis direction earthquake intensity；M indicates magnitude；
Earthquake intensity determining module is used for joint type (6) to formula (8), so thatWith dichotomy, by the zero point of equation
The section at place is constantly divided into two, and the section newly obtained is made constantly to become smaller, and two endpoint Step wise approximation non trivial solutions acquire
Decay elliptical long axis length and minor axis length, then long axis length and earthquake magnitude size are brought into formula (7), obtains each earthquake thing
The earthquake intensity of any site under part.
9. system according to claim 6, which is characterized in that the economic loss rate computing unit specifically includes:
Loss late computing module, for calculating loss late F (I, GDP) according to formula (9):
F (I, GDP)=CAIB (9)
Wherein, F (I, GDP) indicates GDP loss late, and I is earthquake intensity, and A, B are vulnerability statistical parameter, and C is correction factor；
Economic loss computing module, for calculating economic loss LOSS according to formula (10)GDP:
LOSSGDP=F (I, GDP) × GDP (10).
10. system according to claim 6, which is characterized in that the earthquake loss probability of happening and costing bio disturbance unit
It specifically includes:
Parameter initialization module is set in the earthquake event data library comprising NrGroup seismic events, set g group earthquake thing
Part includes kgA seismic activity, i-th of seismic activity are LOSS1 to the earthquake loss that site s is generatedi, site s is in t to be predicted
Interior earthquake sum is Ksum(g), earthquake loss LOSS1iIt is greater than the set value LOSS10, it is less than LOSS10+1Number be N
(LOSS10+1≥LOSS1i≥LOSS10)；Total earthquake number that earthquake zone where site s occurs is Nmean, in which:
N(LOSS10+1≥LOSS1i≥LOSS10)=count [LOSS10+1≥LOSS1i≥LOSS10] (12)
Nmean=v0*t (13)
Wherein,v0For Annual Occurrence Rates；
First probability evaluation entity generates ground when one earthquake occurs for calculating site s in t to be predicted according to formula (14)
Shock loss LOSS1iIt is greater than the set value LOSS10And it is less than LOSS10+1Probability be p (LOSS10+1≥LOSS1i≥LOSS10):
Second probability evaluation entity, for predicting that site s is on the ground for occurring to generate when earthquake more than once in t according to formula (15)
Shock loss LOSS1conIn LOSS10+1And LOSS10Between probability of happening be p (LOSS10+1≥LOSS1con≥LOSS10):
p(LOSS10+1≥LOSS1con≥LOSS10)=1- (1-p (LOSS10+1≥LOSS1i≥LOSS10)Nmean) (15)
First-loss computing module, for calculating earthquake loss that g group seismic events generate site s most according to formula (16)
Big value LOSSmax(g):
Second costing bio disturbance module, for obtaining NrEach group of LOSS in group seismic eventsmaxValue, by Nr* p% is corresponding
LOSSmaxValue is as the earthquake loss under outcross probability p%.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751399A (en) * | 2019-10-22 | 2020-02-04 | 中国地震局地球物理研究所 | Earthquake danger parallel analysis method, device and equipment |
| CN111382908A (en) * | 2020-03-11 | 2020-07-07 | 中国地震局地球物理研究所 | Earthquake random event set simulation method considering large earthquake time correlation |
| CN111427984A (en) * | 2020-03-24 | 2020-07-17 | 成都理工大学 | Regional seismic probability space distribution generation method |
| CN112200399A (en) * | 2020-07-20 | 2021-01-08 | 杭州叙简科技股份有限公司 | Earthquake disaster risk assessment and economic loss prediction method |
| CN112986731A (en) * | 2021-02-08 | 2021-06-18 | 天津大学 | Electrical interconnection system toughness assessment and improvement method considering seismic uncertainty |
| CN113268852A (en) * | 2021-04-14 | 2021-08-17 | 西南交通大学 | Monte Carlo simulation-based earthquake landslide probability risk analysis method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN103399344A (en) * | 2013-07-24 | 2013-11-20 | 北京师范大学 | Prediction method for predicating collapse disaster position after earthquake |
| CN105093285A (en) * | 2014-05-16 | 2015-11-25 | 许丁友 | Artificial intelligence earthquake judgment method and earthquake detection system |
| CN106251032A (en) * | 2016-06-28 | 2016-12-21 | 中国人民解放军后勤工程学院 | A kind of Earthquake-landslide disaster Disaster rapid evaluation method |
| CN107368938A (en) * | 2017-06-03 | 2017-11-21 | 中国人民解放军后勤工程学院 | Quantitative evaluation method for risk loss of single landslide |
| CN108254782A (en) * | 2018-02-09 | 2018-07-06 | 中国地质大学（北京） | A kind of acquisition methods and system of side slope eaerthquake damage failure probability |
| CN108319717A (en) * | 2018-02-11 | 2018-07-24 | 福州大学 | The computational methods of optimum lattice size range when a kind of vector point is converted to grid point |
| KR101921557B1 (en) * | 2018-06-07 | 2018-11-23 | 김선겸 | Exterior panel insulation for construction |
-
2019
- 2019-04-25 CN CN201910339717.1A patent/CN110046454A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN103399344A (en) * | 2013-07-24 | 2013-11-20 | 北京师范大学 | Prediction method for predicating collapse disaster position after earthquake |
| CN105093285A (en) * | 2014-05-16 | 2015-11-25 | 许丁友 | Artificial intelligence earthquake judgment method and earthquake detection system |
| CN106251032A (en) * | 2016-06-28 | 2016-12-21 | 中国人民解放军后勤工程学院 | A kind of Earthquake-landslide disaster Disaster rapid evaluation method |
| CN107368938A (en) * | 2017-06-03 | 2017-11-21 | 中国人民解放军后勤工程学院 | Quantitative evaluation method for risk loss of single landslide |
| CN108254782A (en) * | 2018-02-09 | 2018-07-06 | 中国地质大学（北京） | A kind of acquisition methods and system of side slope eaerthquake damage failure probability |
| CN108319717A (en) * | 2018-02-11 | 2018-07-24 | 福州大学 | The computational methods of optimum lattice size range when a kind of vector point is converted to grid point |
| KR101921557B1 (en) * | 2018-06-07 | 2018-11-23 | 김선겸 | Exterior panel insulation for construction |
Non-Patent Citations (4)
| Title |
|---|
| 冀昆: "我国不同抗震设防需求下的强震动记录选取研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 刘吉夫 等: "中国大陆地震风险分析模型研究", 《北京师范大学学报( 自然科学版)》 * |
| 邵霄怡: "概率地震危险性的蒙特卡洛方法研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
| 马思远: "基于Newmark模型的地震滑坡危险性评价研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751399A (en) * | 2019-10-22 | 2020-02-04 | 中国地震局地球物理研究所 | Earthquake danger parallel analysis method, device and equipment |
| CN110751399B (en) * | 2019-10-22 | 2023-02-24 | 中国地震局地球物理研究所 | Earthquake danger parallel analysis method, device and equipment |
| CN111382908A (en) * | 2020-03-11 | 2020-07-07 | 中国地震局地球物理研究所 | Earthquake random event set simulation method considering large earthquake time correlation |
| CN111382908B (en) * | 2020-03-11 | 2023-08-22 | 中国地震局地球物理研究所 | Earthquake random event set simulation method considering correlation of large earthquake time |
| CN111427984A (en) * | 2020-03-24 | 2020-07-17 | 成都理工大学 | Regional seismic probability space distribution generation method |
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| CN112200399A (en) * | 2020-07-20 | 2021-01-08 | 杭州叙简科技股份有限公司 | Earthquake disaster risk assessment and economic loss prediction method |
| CN112986731A (en) * | 2021-02-08 | 2021-06-18 | 天津大学 | Electrical interconnection system toughness assessment and improvement method considering seismic uncertainty |
| CN112986731B (en) * | 2021-02-08 | 2022-12-02 | 天津大学 | Electrical interconnection system toughness assessment and improvement method considering seismic uncertainty |
| CN113268852A (en) * | 2021-04-14 | 2021-08-17 | 西南交通大学 | Monte Carlo simulation-based earthquake landslide probability risk analysis method |
| CN113268852B (en) * | 2021-04-14 | 2022-02-22 | 西南交通大学 | Monte Carlo simulation-based earthquake landslide probability risk analysis method |
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