CN115391736A - Ten-year-scale major fault strong earthquake occurrence probability prediction method - Google Patents
Ten-year-scale major fault strong earthquake occurrence probability prediction method Download PDFInfo
- Publication number
- CN115391736A CN115391736A CN202210746929.3A CN202210746929A CN115391736A CN 115391736 A CN115391736 A CN 115391736A CN 202210746929 A CN202210746929 A CN 202210746929A CN 115391736 A CN115391736 A CN 115391736A
- Authority
- CN
- China
- Prior art keywords
- fault
- probability
- strong
- time
- 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.)
- Pending
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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention is suitable for the technical field of seismic analysis, and provides a ten-year-scale strong earthquake occurrence probability prediction method for a main fault, which comprises the following steps: step one, establishing a violent shock recurrence model in a China land area; supplementing the average recurrence period of the strong shock; supplementing the fault strong shock departure time; determining the main layer separation loss rate of the boundary zone of the movable plots in the land area of China; and the like; the method predicts the strong earthquake probability of the main fault of the boundary zone of the movable mass in the next 10 years based on the strong earthquake recurrence model of the Chinese land area, and solves the problem that the earthquake prediction research needs to be faced for a long time due to incomplete data by establishing the strong earthquake recurrence model and combining the data analysis of the average recurrence period of the strong earthquake, the strong earthquake departure time of the fault and the like.
Description
Technical Field
The invention belongs to the technical field of seismic analysis, and particularly relates to a ten-year-scale strong earthquake occurrence probability prediction method for a main fault.
Background
The land region in China has a typical continental dynamic environment, the medium property has the characteristics of vertical layering and transverse blocking, and the relative motion and deformation of the continental activity land are one of the important modes of land deformation. The boundary zone of the movable land parcel is late quaternary activity fracture, focuses on the main structural deformation of the Chinese land district, has control effect on earthquakes above 7 level in the Chinese land district, and all earthquakes above 8 level and earthquakes above 7 level which exceed 80 percent of the history records of the Chinese land district are all generated on the boundary zone of the movable land parcel. Therefore, the main fault of the boundary zone of the active land is the main target of the 10-year scale earthquake danger research in the land area of China. On the other hand, to represent the degree of confidence in the seismic predictions, in addition to giving estimates of seismic time, location and magnitude, the probability of occurrence of the earthquake should also be given. Therefore, the quantitative research on the major fault strong earthquake danger of the boundary zone of the movable land mass is significant to the earthquake fortification work.
China land area is vast in breadth, observation data levels in different areas have great difference, earthquake probability research based on an earthquake recurrence model mainly focuses on areas or faults with good observation data, and nationwide research is mainly determined in the pregnancy stage at present and lacks of probability results. Due to the limitation of objective conditions, related data is difficult to develop in a short time, so that incomplete data is a problem which needs to be faced for a long time in earthquake prediction research. Aiming at the problem, a ten-year-scale strong earthquake occurrence probability prediction method for a main fault is provided.
Disclosure of Invention
The embodiment of the invention provides a ten-year-scale major fault strong earthquake occurrence probability prediction method, which is characterized in that the major fault of a boundary zone of an active block is predicted according to a strong earthquake recurrence model in a Chinese land area in the future 10 years, and fault strong earthquake recurrence behaviors are reflected by fault accumulated displacement, motion rate and other data except strong earthquake records, so that the comprehensive earthquake probability prediction by fully utilizing the existing data is realized, and the prediction result precision is improved at the present stage.
The embodiment of the invention is realized in such a way that a ten-year-scale strong earthquake occurrence probability prediction method for a main fault comprises the following steps:
step one, establishing a violent earthquake recurrence model in the land area of China;
supplementing the average recurrence period of the strong shock;
supplementing the fault strong shock departure time;
determining the main fracture layer separation loss rate of the boundary zone of the movable plot in the land area of China;
step five, calculating the strong earthquake accumulation probability and the conditional probability of the main fault of the boundary zone of the movable plots in the land area of China;
and step six, obtaining a calculation conclusion.
As a preferred embodiment of the present invention, the step one, establishing the earthquake recurrence model in the land area of china, comprises the following detailed steps:
selecting earthquake intervals determined based on historical earthquakes in the earthquake-intensifying relapse intervals of the active faults in the land area of China;
taking the interval arithmetic mean of the fault earthquake as the mean recurrence period of the fault;
giving out the evanescent rate of each earthquake of the fault according to the ratio of the earthquake recurrence interval to the average recurrence period;
taking each fault layer evanescent rate result as a sample, selecting two probability density functions of lognormal (LogN) and Brownian Process Time (BPT) for fitting, wherein the formulas are respectively as follows:
where t represents the evanescent rate and μ represents the averaging period, which is 1 in the lognormal function and 0 in the brownian process time function since it has been normalized; sigma represents the concentration degree of seismic intervals and needs to be obtained through fitting, and for the two probability density functions related to the method, when sigma is gradually increased from 0, the seismic recurrence mode is changed according to the sequence of periods, quasiperiods, poisson and clusters;
the two probability density functions are given the same weight, the cumulative probability and the conditional probability result are obtained through weighted summation, and the formula is as follows:
wherein CDF LogN And CDF BPT Respectively represent the cumulative probability of the lognormal distribution and the brownian time process distribution, and t1 and t2 are the departure rates corresponding to the starting time and the ending time of the prediction period, respectively.
As a preferred embodiment of the invention, the step of determining the main delamination separation vanishing rate of the boundary zone of the movable plots in the land area of China specifically comprises the following steps:
looking up data to obtain the strong earthquake probability of 391 fault sections of the land area of China, which are main active faults of the boundary of the active plot, and predicting;
and (4) estimating the period of the recurrence of the strong shock and the time of the departure of the strong shock.
As a preferred embodiment of the present invention, the detailed steps of supplementing the mean recurrence period of a macroseism comprise:
acquiring the data of the violent shock recurrence period of 254 fault intervals and the violent shock departure time of 231 fault intervals by referring to the data, wherein the data respectively account for 65 percent and 59 percent of the total number, and for fault intervals lacking the violent shock recurrence period: collecting the fault movement rate of each fault section determined based on the seismic geological method, and analyzing the relation between the fault movement rate and the average recurrence period;
fitting the two types of data to obtain the average recurrence period and the movement rate of the fault bed strong earthquake;
the empirical relationship between the mean recurrence period of the fault macroseism and the movement rate is as follows: log (log) 10 T = -0.0842S +3.494, wherein T is the mean recurrence period of the strong shock in years, and S is the fault movement rate in mm/a.
As a preferred embodiment of the invention, the supplementary fault strong earthquake departure time calculation step is as follows:
for fault segments lacking strong seismic departure times: the seismic departure time has lower time limit seismic departure time distribution calculation, and the expression is as follows:
wherein τ represents seismic departure time, TH represents a lower limit of seismic departure time, i.e., a target earthquake does not occur within a preceding time period of at least TH, and F is an accumulated probability function of a recurrent periodic probability density function;
predicting the seismic conditional probability in the time period, wherein the expression is as follows:
wherein Δ T is the predicted duration;
calculating a fault lacking seismic departure time, wherein the expression is as follows:
and (3) carrying out normalization processing on the lower limit of the departure time and the predicted time length by using the strong earthquake average recurrence period of each fault section so as to adapt to the probability density function of the departure rate.
As a preferred embodiment of the invention, for fault sections lacking information of departure time, the integrity starting time of the seismic catalogue is obtained according to the partition where the fault sections are located, and then the lower limit of the seismic departure time of the fault sections is calculated, so that the departure time is supplemented; since the elapsed time obtained by equation (5) is in the form of probability, it is convenient to show that the elapsed time expected to represent such faults is calculated according to equation (5), and the rate of the departure is given therefrom, but this rate of departure is not used in calculating the probability, but equations (6) and (7) are used.
As a preferred embodiment of the invention, the detailed calculation process of the major fault strong earthquake accumulation probability and the conditional probability of the boundary zone of the movable plot in the land area of China is as follows:
acquiring the average recurrence period and the strong shock departure time:
according to the acquisition mode of two types of data, faults can be divided into four types, namely:
both have records; recording the strong shock departure time, and taking the average recurrence period as the inference; recording the average recurrence period, and deducing the time of strong shock departure; neither is recorded;
calculating the strong earthquake cumulative probability and the conditional probability of the main fault section of the boundary of the movable plot in the land area of China in the next 10 years according to the classification:
both are not recorded and for the first two cases, the dissociation rate can be given by combining the strong seismic dissociation time with the average recurrence period (recorded or speculatively obtained), and then the corresponding probability is given according to the formula (3) and the formula (4); for the latter two cases, the corresponding probabilities are given according to equation (6) and equation (7), respectively.
As a preferred embodiment of the present invention, the detailed method for drawing the main conclusions is as follows: according to the result of the rate of departure, the result of the rate of departure is analyzed, and the beneficial effects of the invention are further judged according to the recurrence period: the method predicts the major fault of the boundary zone of the movable plot in the next 10 years based on the severe earthquake recurrence model of the land area in China, and reflects the fault severe earthquake recurrence behavior through the data such as fault accumulated displacement, motion rate and the like besides the severe earthquake record, so that the comprehensive earthquake probability prediction by fully utilizing the existing data is the current stage to improve the prediction result precision; the Bayesian method is an ideal method for synthesizing different observation data, and the posterior probability of the model is calculated by taking various data as the limit, so that a recurrence model which most possibly meets various observation data at the same time can be obtained; when the model contains multiple probability density functions, the bayesian approach can also weight each probability density function.
Drawings
FIG. 1 is a plot of the evanescent rate and fit results (A, evanescent rate and probability density; B, cumulative probability) for the Pacific seismic zone and the Chinese land areas;
FIG. 2 is an empirical relationship of fault section motion rate to average recurrence period (A, seismic geologic motion rate to average recurrence period; B, geodetic method vs. fault slip rate obtained by seismic geologic method);
FIG. 3 is a probability density curve of the mean recurrence period of the strong shock in the fault segments of different regions (the left side represents the result of the recorded recurrence period, and the right side represents the result of the recorded and presumed recurrence period);
FIG. 4 is a graph of normalized elapsed time expectation versus cumulative probability (A, normalized elapsed time expectation versus normalized elapsed time lower bound;
FIG. 5 is the effect of relative prediction duration on conditional probability and cumulative probability (A, lognormal conditional probability; B, brownian process time conditional probability; C, lognormal cumulative probability; D, brownian process time cumulative probability).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses a ten-year-scale strong earthquake occurrence probability prediction method for a main fault, which comprises the following steps of:
step one, establishing a violent shock recurrence model in a China land area;
determining the main fault layer departure rate of the boundary zone of the movable plots in the land area of China;
supplementing the average recurrence period of the strong shock;
supplementing the fault strong shock departure time;
step five, calculating the strong earthquake accumulation probability and the conditional probability of the main fault of the boundary zone of the movable plots in the land area of China;
and step six, obtaining a calculation conclusion.
In the scheme, the step one, the detailed step of establishing the severe earthquake recurrence model in the land area of China is as follows:
selecting earthquake intervals determined based on historical earthquakes in the active fault strong earthquake recurrence intervals in the land area of China;
taking the interval arithmetic mean of the fault earthquake as the mean recurrence period of the fault;
giving out the departure rate of each earthquake occurrence of the fault according to the ratio of the earthquake recurrence interval to the average recurrence period;
taking each fault layer evanescent rate result as a sample, selecting two probability density functions of lognormal (LogN) and Brownian Process Time (BPT) for fitting, wherein the formulas are respectively as follows:
where t represents the evanescent rate and μ represents the averaging period, which is 1 in the lognormal function and 0 in the brownian process time function since it has been normalized; sigma represents the concentration degree of seismic intervals and needs to be obtained through fitting, and for the two probability density functions related to the method, when the sigma is gradually increased from 0, the seismic recurrence mode is changed according to the sequence of periods, quasi-periods, poisson and clusters;
the two probability density functions are given the same weight, the cumulative probability and the conditional probability result are obtained through weighted summation, and the formula is as follows:
wherein CDF LogN And CDF BPT Each represents a logarithmThe cumulative probability of the normal distribution and the brown time process distribution, and t1 and t2 are the departure rates corresponding to the starting time and the ending time of the prediction period, respectively.
In the scheme, the step of determining the main fault departure rate of the boundary zone of the movable plot in the land area of China specifically comprises the following steps:
looking up data to obtain the strong earthquake probability of 391 fault sections of the land area of China, which are main active faults of the boundary of the active plot, and predicting;
acquiring the strong earthquake recurrence period of 254 fault intervals and the strong earthquake departure time data of 231 fault intervals by looking up the data, wherein the data respectively account for 65 percent and 59 percent of the total number;
in 391 fault sections related to the research, 200 fault sections have two types of data at the same time, and 105 fault sections lack the two types of data at the same time;
and (4) estimating the period of the recurrence of the strong shock and the time of the departure of the strong shock.
In the scheme, the detailed steps of supplementing the mean relapse period of the macroseism comprise:
collecting the fault movement rate of each fault section determined based on the seismic geological method, and analyzing the relation between the fault movement rate and the average recurrence period;
fitting the two types of data to obtain the average recurrence period and the movement rate of the fault layer strong earthquake;
the empirical relationship between the mean recurrence period of the fault macroseism and the movement rate is as follows: log of 10 T = -0.0842S +3.494, wherein T is the mean recurrence period of the macroseism and is in years, and S is the fault movement rate and is in mm/a.
In the scheme, the supplementary fault strong-shock departure time calculation step comprises the following steps:
for fault segments lacking strong seismic departure times: the seismic departure time has lower time limit seismic departure time distribution calculation, and the expression thereof is as follows:
wherein τ represents seismic departure time, TH represents a lower limit of seismic departure time, i.e., a target earthquake does not occur within a preceding time period of at least TH, and F is an accumulated probability function of a recurrent periodic probability density function;
predicting the seismic conditional probability in the time period, wherein the expression is as follows:
wherein Δ T is the predicted duration;
calculating a fault lacking seismic departure time, wherein the expression is as follows:
and (4) carrying out normalization processing on the lower limit of the departure time and the predicted time length by using the strong earthquake average recurrence period of each fault section so as to adapt to a probability density function of the departure rate.
In the scheme, for fault sections lacking the departure time data, the integrity starting time of the earthquake catalogue is obtained according to the partition where the fault sections are located, and then the lower limit of the earthquake departure time of the fault sections is calculated, so that the departure time is supplemented; since the elapsed time obtained by equation (5) is in the form of probability, it is convenient to show that the elapsed time expected to represent such faults is calculated according to equation (5) and the rate of the elapsed time is given based on this, but this rate of the elapsed time is not used in calculating the probability, and equation (6) and equation (7) are used instead.
In the scheme, the detailed calculation process of the major fault strong earthquake accumulation probability and the conditional probability of the boundary zone of the movable plots in the land area of China is as follows:
obtaining the average recurrence period and the strong shock departure time
According to the acquisition mode of two types of data, faults can be divided into four types, namely:
both have records; recording the time of the strong shock departure, and taking the average recurrence period as the inference; recording the average recurrence period, and deducing the time of strong shock departure; both are not recorded;
calculating the strong earthquake cumulative probability and the conditional probability of the main fault section of the boundary of the movable plot in the land area of China in the next 10 years according to the classification
Both are not recorded and for the first two cases, the dissociation rate can be given by combining the strong seismic dissociation time with the average recurrence period (recorded or speculatively obtained), and then the corresponding probability is given according to the formula (3) and the formula (4); for the latter two cases, the corresponding probabilities are given according to equation (6) and equation (7), respectively.
In this scenario, the detailed approach to draw the main conclusions is as follows: and analyzing the result of the rate of departure according to the result of the rate of departure, and further judging according to the recurrence period.
Embodiment I, establishing a severe earthquake recurrence model in the land area of China
The normalized Chinese land area earthquake-intensive recurrence model is established herein with reference to the methods of Nishenko and Buland (1987). Based on the Pacific earthquake zone organized by predecessors and earthquake intervals of land areas in China (Nishenkoand Buland,1987; wenzhize, 1999 b), earthquake intervals determined based on historical earthquakes in the earthquake-violent recurrence intervals of active faults in land areas in China are selected, the average recurrence period of the faults is calculated and averaged by the earthquake intervals of the faults, and the departure rate of each earthquake of the faults is given according to the ratio of the earthquake recurrence intervals to the average recurrence period. According to the data and the method, a total of 69 evanescent rate results are obtained. Statistical tests show that the recurrence interval empirical distributions of two construction environments in the plate of the Chinese land area and the edge of the ring pacific plate are not significantly different (smell, 1999 b), so that 69 probability density functions of lognormal (LogN) and Brownian Process Time (BPT) are selected as samples according to the separation evanescent rate results of each fault and are fitted, and the formulas are respectively as follows:
where t represents the evanescent rate and μ represents the averaging period, which is 1 in the lognormal function and 0 in the brownian process time function since it has been normalized; sigma represents the concentration degree of seismic intervals and needs to be obtained through fitting, and for the two probability density functions involved in the method, when sigma is gradually increased from 0, the seismic recurrence mode changes according to the sequence of periods, quasiperiods, poisson and clusters.
Fig. 1 shows the fitting results of the distribution of the evanescent rate, the cumulative frequency and two probability density functions, and it can be seen from fig. 1 that the evanescent rate corresponding to the occurrence of the strong shock is mainly concentrated near 1, and the upper limit and the lower limit are 0.5 and 1.7 respectively, which indicates that the fault strong shock activity has better periodicity. The fitting result shows that the strong earthquake departure rate of the land area in China conforms to the logarithmic normal distribution of mu =0, sigma =0.2228 or the Brown time process distribution of mu =1, sigma = 0.2411. The fitting results of the two probability density functions are similar, and due to the lack of other limiting conditions, the two probability density functions are given the same weight, and the cumulative probability and the conditional probability results are obtained through weighted summation, wherein the formula is as follows:
wherein CDF LogN And CDF BPT Respectively represent the cumulative probability of the lognormal distribution and the distribution of the Brown time process, and t1 and t2 are the departure rates corresponding to the starting time and the ending time of the prediction period, respectively.
Example II, determining the strong earthquake-leaving vanishing rate of main fault in boundary zone of movable plots in land region of China
After a strong earthquake recurrence model in the land area of China is established based on the Pacific seismic zone and the earthquake evanescent rate distribution in the land area of China, the strong earthquake probability can be calculated according to the normalized evanescent time of faults. Shao Shi just et al (2022) divides the major active faults of the boundary of the active land area of China into 391 fault sections based on seismic geological data and gives analysis results such as strong earthquake fracture empty section, fault motion rate and locking rate. Based on the above, the strong earthquake probability of 391 fault sections is predicted. The data are consulted to find out the cycle of the strong earthquake recurrence of 254 disconnected intervals and the time of the strong earthquake departure of 231 disconnected intervals, which respectively account for 65 percent and 59 percent of the total number. Of 391 fault sections involved in the study, 200 fault sections had two types of data simultaneously, and 105 fault sections lacked two types of data simultaneously. For the interval lacking one or two types of data, the corresponding method is used for deducing the cycle of the violent shock recurrence and the time of the violent shock departure.
EXAMPLE III supplementation of the mean relapse period of a jolt
Elastic rebound theory considers that earthquakes are the result of a sudden release of the previously accumulated stress of the rock, the rate of which may affect the average recurrence period. Therefore, the research simultaneously collects the fault motion rate determined by each fault section based on the seismic geological method and analyzes the relation between the fault motion rate and the average recurrence period. The results show that the mean recurrence period of the fault strong earthquake has a better log-linear relationship with the movement rate, and the recurrence period is shorter when the fault movement rate is higher (fig. 2A). By fitting two types of data, an empirical relation between the average recurrence period of the fault bed strong earthquake and the movement rate is obtained: log (log) 10 T = -0.0842S +3.494, wherein T is the mean recurrence period of the macroseism and is in years, and S is the fault movement rate and is in mm/a.
For the fault section lacking the record of the mean recurrence period of the strong shock, the mean recurrence period of the strong shock can be deduced by using the empirical relation, however, due to the limitation of data, the fault section lacking the record of the mean recurrence period of the strong shock does not have the fault movement rate determined by the seismic geological method. Therefore, a GPS horizontal velocity field published by Zheng et al (2017) is taken as a constraint, a GPS station speed with a large error is eliminated in calculation, a fault connection element model is established in a partitioning mode according to the distribution of 391 fault sections (Wangshi et al, 2008), all fault inclination angles are assumed to be 90 degrees, the locking depth is 15km, and the motion speed of each fault section is given through inversion. FIG. 2B shows the comparison of two movement rates of each fault section, and it can be seen from FIG. 2B that the movement rate of the fault provided by the geodetic method is closer to the result of the seismic geological method, and the difference is mostly within 2 mm/a. Under the existing conditions, the earthquake geological motion rate can be approximated by the fault motion rate given by the geodetic method, so that the average recurrence period of the macroseism is calculated for the fault section lacking the record of the average recurrence period of the macroseism and the earthquake geological motion rate by combining the motion rate given by the geodetic method and an empirical formula. The recorded results and the inferred results are combined, and the mean recurrence period of the strong shock of 391 fault intervals is given.
Fig. 3 shows probability density curves of the average recurrence periods of different regions, and in combination with fig. 3, it can be seen that the average recurrence periods of the strong earthquakes in each region have unimodal distribution and have better consistency with the earthquake activity characteristics in the land regions of china. The average recurrence period of the broken layer sections in the west region is short, wherein the Qinghai-Tibet plateau has the shortest average recurrence period and is concentrated in about 500 years, the Chuanhan region and the Qinghai-Tibet plateau are respectively concentrated in about 800 years and 1000 years, only the average recurrence period of the thoracotomy and the red river fracture is long (Allen et al, 1984; xuxi Wei et al, 2014; luhai Peak, 2015), and the Xinjiang region is concentrated in about 2500 years; the average recurrence period of the fault layer sections in the eastern region is long and is concentrated in about 3500 years. The probability density of the average recurrence period after the supplementation in the Sichuan Yunnan region, the northeast edge of the Qinghai-Tibet plateau, the Xinjiang region and the east region is not obviously different from the probability density curve of the recorded average recurrence period, and the probability density curve of the average recurrence period after the supplementation in the Qinghai-Tibet plateau region is different from the probability density curve of the recorded result. This is mainly because the related data of the internal fault of the tibetan plateau is less, the recurrence period is mainly estimated based on geodetic data, and the motion speed of the internal fault of the tibetan plateau obtained by inversion is mostly higher than the speed range in fig. 2A. The relation of the fault movement speed-strong earthquake recurrence period may need to increase high-speed samples, but the research degree of the internal fault of the Qinghai-Tibet plateau is relatively low at present, and the required data is difficult to provide. Because the recurrence period range after supplementation is consistent with the recorded result and the interior of the Qinghai-Tibet plateau is lack of recorded data limitation, the current supplementation result is still used for strong earthquake prediction.
Example four supplement of Strong seismic departure time of fault
The strong earthquake departure time is an important parameter in time-dependent earthquake probability prediction, but not all faults are recorded, so that difficulty is caused in prediction of the regional strong earthquake probability. For this problem, field and Jordan (2015) give an expression for the seismic departure time with a lower bound seismic departure time profile:
where τ represents seismic departure time, TH represents the lower seismic departure time limit, i.e., the target seismic has not occurred for at least the duration of TH before, and F is the cumulative probability function of the recurrent periodic probability density function. On this basis, field and Jordan (2015) give the expression for the seismic conditional probability in the prediction period in this case:
where Δ T is the prediction duration.
The method of Field and Jordan was applied to the third edition of california earthquake break integrated prediction model (Field et al, 2015), and therefore reference is also made to this method to deal with faults lacking seismic departure time and to refine it according to the study content, on the one hand, the cumulative probability expression with only the lower bound of departure time is given according to equation (5):
on the other hand, the lower limit of departure time and the predicted time length are normalized by using the strong earthquake average recurrence period of each fault section so as to adapt to a probability density function of departure rate (figure 4);
FIG. 4A, normalized elapsed time expected as a function of lower normalized elapsed time limit; b, comparing the same normalized time length as the elapsed time with the accumulated probability of the lower limit of the elapsed time, wherein the solid line is the case of using the abscissa value as the lower limit of the normalized elapsed time, and the dotted line is the case of using the abscissa value as the normalized elapsed time
(both using a lognormal distribution of μ =0, σ =0.2228 and a brownian time course distribution of μ =1, σ = 0.2411)
The research results of the seismic catalogue integrity initial time research in different areas of China are given by Huangweiqiong et al (1994), xuweijin and Gaobantan (2014), the seismic catalogue integrity initial time is obtained according to the partition where the fault section lack of information of the departure time, and the lower limit of the seismic departure time is further calculated, so that the departure time is supplemented. Since the elapsed time obtained by equation (5) is in the form of probability, it is convenient to show that the elapsed time expected to represent such faults is calculated according to equation (5), and the rate of the departure is given therefrom, but this rate of departure is not used in calculating the probability, but equations (6) and (7) are used.
The integrity of the data of the strong earthquake departure time has spatial distribution similar to the integrity of the data of the recurrence period, the record of the Qinghai-Tibet plateau is relatively lacked, the record of the east region is relatively complete, and the integrity of the records of other regions is at a medium level. According to the recorded and inferred average recurrence period and the departure time of the strong shock, the departure rate of each broken interval can be obtained. The fault sections with the evanescent rate of 0.5 to 1.0 are the most, and the fault sections with the evanescent rate of less than 0.5 are the next, and the fault sections are recorded to mostly generate earthquakes of more than 7 grades, so that the risk of strong earthquake is low (figure 1).
Example five, earthquake accumulation probability and conditional probability of main fault in boundary zone of movable land block in China land area
Based on the supplement of seismic geological data and the method, the strong earthquake average recurrence period and the strong earthquake departure time of the main fault section of the boundary of the moving plot in the land area of China are obtained, and the fault can be divided into four types according to the obtaining mode of the two types of data, namely: both have records; recording the time of the strong shock departure, and taking the average recurrence period as the inference; recording the average recurrence period, and deducing the time of strong shock departure; both are not recorded. For the former two cases, the dissociation rate can be given by combining the time of strong seismic dissociation and the average recurrence period (recorded or obtained by conjecture), and then corresponding probability is given according to the formula (3) and the formula (4); for the latter two cases, the corresponding probabilities are given according to equation (6) and equation (7), respectively. According to the method, the strong earthquake accumulation probability and the conditional probability of the main fault section of the movable block boundary of the land area in China in the next 10 years are respectively given. In the next 10 years, broken layer sections with higher strong earthquake accumulation probability in the land area of China are mainly concentrated on east boundaries of diamond-shaped plots of Sichuan Yunnan, east-north edges of Qinghai-Tibet plateau, east boundaries of Erdos plots and northwest boundaries. In addition, the Tianshan area and the Himalayan arc section disjunction layer section have higher accumulation probability. The spatial distribution of the fault section with high accumulation probability is more consistent with that of the fault section with high evanescent rate. The Qinghai-Tibet plateau and the fault sections around the Qinghai-Tibet plateau have the highest strong earthquake conditional probability, the conditional probability of other fault sections is reduced along with the increase of the distance from the Qinghai-Tibet plateau, the strong earthquake conditional probability of the west part of the land area of China is higher than that of the east part, the characteristic negatively related to the average recurrence period of the strong earthquake is presented on the whole, and the relevance to the departure rate is not obvious.
The M7 special workgroup (2012) and the xuxi Wei and the like (2017) give the distribution of the violent earthquake cracking blank sections in the land area of China according to earthquake geological data, and the distribution is divided into two types of high-departure-rate blank sections and historical lack earthquake blank sections, wherein the departure time is far away from the super-average recurrence period. Comparing the two types of blank sections with the distribution of fault sections with the 10-year accumulation probability of more than 30%, the spatial distribution of the high-departure-rate blank sections and the high-accumulation-probability fault sections has better consistency, and the historical earthquake-lacking blank sections generally lack the high-probability fault sections. This is mainly because faults in the high-evanescent rate null segment generally have a high evanescent rate, whereas the seismic quasi-periodic recurrence mode considers that the probability of a strong shock is positively correlated with the evanescent rate, and therefore faults in the high-evanescent rate null segment generally have a high accumulation probability. Historical lacking strong shock records exist in the empty sections, the distribution of the departure time of the empty sections is presumed through the complete time of the seismic catalogue, and then various probabilities are given, the method is greatly influenced by the complete time of the seismic catalogue, the expectation of the given departure time is usually slightly earlier than the complete initial time of the seismic catalogue (figure 4), and therefore the departure rate and the strong shock accumulation probability of the empty sections are generally lower;
EXAMPLE VI drawing computational conclusions
The departure rate result shows that the departure rate of the major faults in the boundary zone of the active land area of most China exceeds 0.5, and theoretically, the probability of the occurrence of the strong earthquake exists, but the recurrence period of the strong earthquake of the faults is mostly more than 1000 years (figure 3), so that when 10-year scale earthquake risk analysis is carried out, the research target should be concentrated on fault sections with the departure rate of 1.0 or higher.
The probability prediction result of the future 10 years shows that the rhombic landmass east boundary of the Sichuan Yunnan, the east-north edge of the Qinghai-Tibet plateau, the east boundary of the Ordos landmass, the northwest boundary of the Ordos landmass, the Tianshan area and the disjointed segment of the Himalayan arc part have higher strong shock accumulation probability; the fault sections with higher probability of strong earthquake condition are mainly concentrated on the Qinghai-Tibet plateau and the periphery thereof. Since the average recurrence period of the fault has a great influence on the condition probability, further work should be performed mainly on fault sections with high earthquake accumulation probability.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of sub-steps or stages of other steps.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A ten-year-scale major fault strong earthquake occurrence probability prediction method is characterized by comprising the following steps:
step one, establishing a violent shock recurrence model in a China land area;
supplementing the average recurrence period of the strong shock;
supplementing the fault strong shock departure time;
determining the main layer separation loss rate of the boundary zone of the movable plots in the land area of China;
step five, calculating the strong earthquake accumulation probability and the conditional probability of the main fault of the boundary zone of the movable plots in the land area of China;
and step six, obtaining a calculation conclusion.
2. The method for predicting the ten-year-scale major fault strong earthquake occurrence probability of claim 1, wherein the step one of establishing the strong earthquake recurrence model in the land area of China comprises the following detailed steps:
selecting earthquake intervals determined based on historical earthquakes in the active fault strong earthquake recurrence intervals in the land area of China;
taking the interval arithmetic mean of the fault earthquake as the mean recurrence period of the fault;
giving out the evanescent rate of each earthquake of the fault according to the ratio of the earthquake recurrence interval to the average recurrence period;
taking each fault layer evanescent rate result as a sample, selecting two probability density functions of lognormal (LogN) and Brownian Process Time (BPT) for fitting, wherein the formulas are respectively as follows:
where t represents the evanescent rate and μ represents the averaging period, which has been normalized so that it has a value of 1 in the lognormal function and a value of 0 in the brownian process time function; sigma represents the concentration degree of seismic intervals and needs to be obtained through fitting, and for the two probability density functions related to the method, when the sigma is gradually increased from 0, the seismic recurrence mode is changed according to the sequence of periods, quasi-periods, poisson and clusters;
the two probability density functions are given the same weight, the cumulative probability and the conditional probability result are obtained through weighted summation, and the formula is as follows:
wherein CDFLogN and CDF BPT Respectively represent the cumulative probability of the lognormal distribution and the distribution of the Brown time process, and t1 and t2 are the departure rates corresponding to the starting time and the ending time of the prediction period, respectively.
3. The method for predicting ten-year-scale major fault occurrence probability of the strong earthquake as claimed in claim 1, wherein the step of determining the major fault departure rate of the boundary zone of the active land area in China specifically comprises the following steps:
the data is consulted to obtain the strong earthquake probability of 391 fault sections of main active faults of the boundary of the active plot in the land area of China for prediction;
and (4) estimating the period of the recurrence of the strong shock and the time of the departure of the strong shock.
4. The method for predicting ten-year-scale major fault macroseism occurrence probability as claimed in claim 1, wherein the detailed steps of supplementing the mean recurrence period of macroseisms comprise:
acquiring the data of the violent shock recurrence period of 254 fault intervals and the violent shock departure time of 231 fault intervals by referring to the data, wherein the data respectively account for 65 percent and 59 percent of the total number, and for fault intervals lacking the violent shock recurrence period: collecting the fault movement rate of each fault section determined based on the seismic geological method, and analyzing the relation between the fault movement rate and the average recurrence period;
fitting the two types of data to obtain the average recurrence period and the movement rate of the fault bed strong earthquake;
the empirical relationship between the average recurrence period of fault macroseism and the movement rate is as follows: log10T = -0.0842s +3.494, where T is the mean recurrence period of the jolt in years and S is the fault motion rate in mm/a.
5. The method for predicting ten-year-scale violent shock occurrence probability of a main fault according to claim 3, wherein the step of calculating the time elapsed for the violent shock of the supplementary fault comprises the following steps:
for fault segments lacking strong seismic departure times: the seismic departure time has lower time limit seismic departure time distribution calculation, and the expression is as follows:
wherein τ represents seismic departure time, TH represents a lower limit of seismic departure time, i.e., a target earthquake does not occur within a preceding time period of at least TH, and F is an accumulated probability function of a recurrent periodic probability density function;
predicting the seismic conditional probability in the time period, wherein the expression is as follows:
wherein Δ T is the predicted duration;
calculating a fault lacking seismic departure time, wherein the expression is as follows:
and (3) carrying out normalization processing on the lower limit of the departure time and the predicted time length by using the strong earthquake average recurrence period of each fault section so as to adapt to the probability density function of the departure rate.
6. The ten-year-scale major fault strong earthquake occurrence probability prediction method according to claim 5, characterized in that for fault sections lacking data of departure time, the integrity starting time of the seismic catalogue is obtained according to the partition where the fault sections are located, and then the lower limit of the seismic departure time of the fault sections is calculated, so that the departure time is supplemented; since the elapsed time obtained by equation (5) is in the form of probability, it is convenient to show that the elapsed time expected to represent such faults is calculated according to equation (5), and the rate of the departure is given therefrom, but this rate of departure is not used in calculating the probability, but equations (6) and (7) are used.
7. The method for predicting the ten-year-scale major fault strong earthquake occurrence probability according to claim 1, wherein the detailed calculation process of the major fault strong earthquake cumulative probability and the conditional probability of the boundary zone of the active block in the land area of China is as follows:
taking the average recurrence period and the strong seismic departure time derived from claims 1-6:
according to the acquisition mode of two types of data, faults can be divided into four types, namely:
both of them have records; recording the strong shock departure time, and taking the average recurrence period as the inference; the average recurrence period is recorded, and the strong shock departure time is used as the inference; both are not recorded;
calculating the strong earthquake cumulative probability and the conditional probability of the main fault section of the boundary of the movable plots in the land areas of China in the next 10 years according to the classification;
both are not recorded and for the former two cases, the evanescent rate can be given by combining the strong shock evanescent time and the average recurrence period, and then the corresponding probability is given according to the formula (3) and the formula (4); for the latter two cases, the corresponding probabilities are given according to equation (6) and equation (7), respectively.
8. The main fault ten-year-scale strong earthquake occurrence probability prediction method according to claim 1, characterized in that the detailed method for obtaining the main conclusion is as follows: and analyzing the result of the rate of departure according to the result of the rate of departure, and further judging according to the recurrence period.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210746929.3A CN115391736A (en) | 2022-06-28 | 2022-06-28 | Ten-year-scale major fault strong earthquake occurrence probability prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210746929.3A CN115391736A (en) | 2022-06-28 | 2022-06-28 | Ten-year-scale major fault strong earthquake occurrence probability prediction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115391736A true CN115391736A (en) | 2022-11-25 |
Family
ID=84115893
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210746929.3A Pending CN115391736A (en) | 2022-06-28 | 2022-06-28 | Ten-year-scale major fault strong earthquake occurrence probability prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115391736A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116663752A (en) * | 2023-07-31 | 2023-08-29 | 山东省地质测绘院 | Geological disaster intelligent early warning system based on big data analysis |
-
2022
- 2022-06-28 CN CN202210746929.3A patent/CN115391736A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116663752A (en) * | 2023-07-31 | 2023-08-29 | 山东省地质测绘院 | Geological disaster intelligent early warning system based on big data analysis |
| CN116663752B (en) * | 2023-07-31 | 2023-10-10 | 山东省地质测绘院 | Geological disaster intelligent early warning system based on big data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101618713B1 (en) | 3-Dimensional Space Modeling Method based on the Geotechnical Information | |
| Guo et al. | Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model | |
| CN104615866B (en) | A kind of life-span prediction method based on physical-statistical model | |
| CN115391736A (en) | Ten-year-scale major fault strong earthquake occurrence probability prediction method | |
| CN107895014B (en) | Time series bridge monitoring data analysis method based on MapReduce framework | |
| CN114580260B (en) | Landslide interval prediction method based on machine learning and probability theory | |
| CN114429189A (en) | High-universality landslide displacement rate early warning method | |
| Oncel et al. | Evaluation of earthquake potential along the Northern Anatolian Fault Zone in the Marmara Sea using comparisons of GPS strain and seismotectonic parameters | |
| Geist et al. | Reconstruction of far-field tsunami amplitude distributions from earthquake sources | |
| Kong et al. | A modified forecast method of ionosphere VTEC series based on ARMA model | |
| CN111767624A (en) | Resource evaluation method based on geological pareto parameter prior solution | |
| CN115563802A (en) | Landslide and critical-sliding-time forecasting method based on self-adaptive time window and multiple criteria | |
| Olivetti et al. | Dating the topography through thermochronology: application of Pecube code to inverted vertical profile in the eastern Sila Massif, southern Italy | |
| Karaca | Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution | |
| Goyal et al. | Multivariate regression modeling of bridge deterioration: Identifying factors influencing deterioration over the life-cycle | |
| Zhou | Application of data mining in coal mine safety decision system based on rough set | |
| Jaya et al. | Forecasting for the Arrival of International Tourists After Two Years of the COVID-19 Pandemic in Indonesia | |
| CN117314218B (en) | Coal mine water damage real-time early warning method based on big data analysis | |
| Utami et al. | Classification of Village Development Index at Regency/Municipality Level Using Bayesian Network Approach With K-Means Discretization | |
| CN108304510B (en) | Method for extracting association rule between city access behavior and POI type of public map service user | |
| CN117875496A (en) | Impact dangerous area dynamic identification method based on multiple geophysical indexes | |
| Zio et al. | Reliability analysis of discrete multi-state systems by means of subset simulation | |
| CN117805892A (en) | Earthquake prediction method based on graphic neural network | |
| Imoto | Use of potential foreshocks to estimate the short-term probability of large earthquakes, Tohoku, Japan | |
| Sudom et al. | Iceberg sightings, shapes and management techniques for offshore Newfoundland and Labrador: Historical data and future applications |
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 |