Earthquake hazard probability prediction method and device

Abstract

The present invention provides a method and device for predicting earthquake hazard probability, including: obtaining an earthquake catalog and precursor anomaly parameters of a target area; the target area is pre-divided into multiple grid areas; according to the earthquake catalog, the background probability of a target earthquake event in each grid area is calculated by a preset Poisson distribution model; according to the precursor anomaly parameters and the background probability of an earthquake, the prediction result of the earthquake hazard probability of each grid area in the target area is determined. The method automatically predicts the earthquake hazard probability through abnormal data in the observation data of geophysical observation equipment and geochemical observation equipment, and determines the prediction result of the earthquake hazard probability of each grid area by dividing the target area into grids, thereby improving the accuracy of the earthquake hazard probability prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake prediction, and in particular to a method and device for predicting earthquake hazard probability. Background Art

[0002] The current earthquake hazard probability prediction method is mainly based on the short-term or annual prediction opinions given by various single prediction methods such as seismology, crustal deformation, geomagnetism, underground fluid, gravity, etc., combined with the regional background earthquake probability and the historical prediction effectiveness of the prediction method, the Bayesian theorem and comprehensive probability method are used to calculate the short-term or annual earthquake hazard probability prediction results based on multiple single prediction methods.

[0003] In the traditional earthquake hazard probability prediction, short-term or annual earthquake hazard probability predictions are given by artificially using multiple single prediction methods such as seismology, crustal deformation, geomagnetism, underground fluid, gravity, etc. Due to the intervention of human subjective factors, the existing earthquake hazard probability prediction methods cannot objectively reflect the true prediction ability of each abnormal data in different prediction methods, resulting in a low accuracy rate of the final earthquake hazard probability prediction results. Furthermore, the traditional earthquake hazard probability prediction ignores the characteristics of multiple or few earthquakes in certain areas, resulting in untrue results of the background earthquake probability, which further leads to a low accuracy rate of the final earthquake hazard probability prediction results. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for predicting earthquake hazard probability, so as to improve the accuracy of earthquake hazard probability prediction results.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting earthquake hazard probability, which includes: obtaining an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in observation data of geophysical observation equipment and geochemical observation equipment; the target area is pre-divided into multiple grid areas; based on the earthquake catalog, the background earthquake probability of the target earthquake event in each grid area is calculated by a preset Poisson distribution model; based on the precursor anomaly parameters and the background earthquake probability, the prediction result of the earthquake hazard probability of each grid area in the target area is determined.

[0006] In combination with the first aspect, an embodiment of the present invention provides a first possible implementation of the first aspect, wherein the step of determining the earthquake hazard probability prediction result of each grid based on the precursory abnormal parameters and the background earthquake probability includes: determining the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid based on the precursory abnormal parameters and the background earthquake probability; calculating the earthquake hazard probability prediction result of each grid based on the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid.

[0007] In combination with the earthquake hazard probability prediction method of any one of the first aspect to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, wherein the precursor anomaly parameters include: the R value of the anomaly data, the longitude and latitude of the anomaly data, the start time and end time of the anomaly data, the preset prediction radius of the anomaly data, and the preset forecast days of the anomaly data.

[0008] In combination with the second possible implementation of the first aspect, the embodiment of the present invention provides a third possible implementation of the first aspect, wherein the R value of the abnormal data is calculated based on the following formula: Among them, a represents the number of earthquakes reported correctly; b represents the total number of earthquakes that should be predicted; c represents the time taken for prediction; and d represents the total time of prediction research.

[0009] In combination with the third possible implementation of the first aspect, an embodiment of the present invention provides a fourth possible implementation of the first aspect, wherein the step of determining the seismic hazard probability of a single abnormal data in the precursory abnormal parameters of each grid based on the precursory abnormal parameters and the background earthquake probability includes: determining the probability of occurrence of the abnormal data based on the precursory abnormal parameters; determining the seismic hazard probability of a single abnormal data in the precursory abnormal parameters of each grid based on the probability of occurrence of the abnormal data and the background earthquake probability.

[0010] In combination with the fourth possible implementation of the first aspect, an embodiment of the present invention provides a fifth possible implementation of the first aspect, wherein the step of determining the probability of occurrence of the abnormal data based on the precursory abnormal parameters includes: determining the duration of the abnormal data and the years in which the abnormal data lasted based on the precursory abnormal parameters; determining the probability of occurrence of the abnormal data based on the duration of the abnormal data and the years in which the abnormal data lasted.

[0011] In combination with the fifth possible implementation of the first aspect, the embodiment of the present invention provides a sixth possible implementation of the first aspect, wherein the step of determining the probability of occurrence of the abnormal data according to the duration of the abnormal data and the year in which the abnormal data lasts includes: calculating the probability of occurrence of the abnormal data by the following formula: Among them, P' represents the probability of the abnormal data occurring; T1 represents the duration of the abnormal data; and T2 represents the number of years the abnormal data lasts.

[0012] In combination with the second possible implementation of the first aspect, the embodiment of the present invention provides a seventh possible implementation of the first aspect, wherein the step of determining the seismic hazard probability of a single abnormal data in the precursory abnormal parameter of each grid according to the precursory abnormal parameter and the background earthquake probability comprises: calculating the seismic hazard probability of a single abnormal data in the precursory abnormal parameter of each grid by the following Bayesian formula: Among them, P(A|B) represents the earthquake hazard probability of the single abnormal data; P(B|A) represents the probability of an earthquake occurring and being accurately predicted; P(A) represents the background earthquake probability; and P(B) is the probability of the abnormal data occurring within the prediction time.

[0013] In combination with the first aspect, the embodiment of the present invention provides an eighth possible implementation of the first aspect, wherein, according to the earthquake hazard probability of a single abnormal data in the precursor abnormal parameter of each grid, the step of calculating the earthquake hazard probability prediction result of each grid comprises: calculating the probability of occurrence of the abnormal data by the following comprehensive probability formula: P = 1-(1-P1) × (1-P2) ... (1-P n ), where P represents the earthquake hazard probability of each grid; P1, P2...P n It represents the earthquake hazard probability of 1 to n single abnormal data in each grid.

[0014] In a second aspect, an embodiment of the present invention provides an earthquake hazard probability prediction device, which includes: a data acquisition module, used to obtain an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in the observation data of geophysical observation equipment and geochemical observation equipment; the target area is pre-divided into multiple grid areas; a background earthquake probability calculation module, used to calculate the background earthquake probability of the target earthquake event in each grid area according to the earthquake catalog through a preset Poisson distribution model; an earthquake hazard probability calculation module, used to determine the prediction result of the earthquake hazard probability of each grid area in the target area according to the precursor anomaly parameters and the background earthquake probability.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] The present invention provides a method and device for predicting earthquake hazard probability, which includes: obtaining an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in the observation data of a geophysical observation device and a geochemical observation device; the target area is pre-divided into multiple grid areas; according to the earthquake catalog, the background probability of a target earthquake event in each grid area is calculated by a preset Poisson distribution model; according to the precursor anomaly parameters and the background probability of an earthquake, the prediction result of the earthquake hazard probability of each grid area in the target area is determined. The method automatically predicts the earthquake hazard probability through the abnormal data in the observation data of the geophysical observation device and the geochemical observation device, and determines the prediction result of the earthquake hazard probability of each grid area by dividing the target area into grids, thereby improving the accuracy of the earthquake hazard probability prediction result.

[0017] Other features and advantages disclosed in this embodiment will be described in the subsequent description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technology of the present disclosure.

[0018] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A schematic diagram of a flow chart of a method for predicting earthquake hazard probability provided by an embodiment of the present invention;

[0021] Figure 2 A schematic flow chart of another earthquake hazard probability prediction method provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of earthquake hazard probability prediction effect is provided for an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of an earthquake hazard probability prediction device is provided for an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention.

[0025] Icons: 41 - data acquisition module; 42 - background earthquake probability calculation module; 43 - earthquake hazard probability calculation module; 51 - memory; 52 - processor; 53 - bus; 54 - communication interface. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In the existing earthquake hazard probability prediction, short-term or annual predictions of earthquake hazard probability are given by artificially using multiple single prediction methods such as seismometry, crustal deformation, geomagnetism, underground fluid, gravity, etc. Due to the intervention of human subjective factors, the existing earthquake hazard probability prediction method cannot objectively reflect the true prediction ability of each abnormal data in different prediction methods, resulting in a low accuracy rate of the final earthquake hazard probability prediction results. Furthermore, the traditional earthquake hazard probability prediction ignores the characteristics of multiple or few earthquakes in certain areas, resulting in untrue results of the background earthquake probability, which further leads to a low accuracy rate of the final earthquake hazard probability prediction results.

[0028] Based on this, an embodiment of the present invention provides a method and device for predicting earthquake hazard probability, which can alleviate the above technical problems and improve the accuracy of earthquake hazard probability prediction results. To facilitate understanding of the embodiment of the present invention, a method for predicting earthquake hazard probability disclosed in an embodiment of the present invention is first introduced in detail.

[0029] Example 1

[0030] like Figure 1 As shown, it is a flow chart of a method for predicting earthquake hazard probability provided by an embodiment of the present invention.

[0031] like Figure 1 As can be seen, the method comprises the following steps:

[0032] Step S101: Obtain earthquake catalog and precursor anomaly parameters of the target area; wherein the precursor anomaly parameters are abnormal data in the observation data of geophysical observation equipment and geochemical observation equipment; the target area is pre-divided into multiple grid areas.

[0033] Furthermore, the geophysical observation equipment and geochemical observation equipment include: the monitoring station has various observation instruments (deformation, electromagnetic, underground fluid). Earthquake prediction mainly relies on data from different observation methods (such as water level and water temperature observed in underground fluid, etc.). Each time series has its own normal dynamic characteristics. When changes that do not conform to normal dynamic characteristics occur, they are called abnormal data.

[0034] In actual operation, the target area is pre-divided into a plurality of grid areas of 0.5°×0.5°.

[0035] Step S102: According to the earthquake catalog, the background probability of the target earthquake event in each grid area is calculated by a preset Poisson distribution model.

[0036] In this embodiment, it is generally believed that after an earthquake occurs, the possibility of the next earthquake occurring immediately is small, but as time goes by, the possibility of the next earthquake occurring increases (it can be understood that the occurrence of an earthquake means that the elastic strain energy accumulated in the crust has been released; if an earthquake does not occur, the energy is accumulating, and the more it accumulates, the greater the possibility of an earthquake occurring).

[0037] Assume that the probability density function of the earthquake recurrence interval in a certain area is f(T), and the time elapsed since the last earthquake is Te. If no earthquake with a magnitude above the target magnitude occurs from the last earthquake to Te, then the probability of an earthquake occurring between Te and Te+ΔT is:

[0038]

[0039] The traditional earthquake hazard probability analysis model assumes that the occurrence of earthquakes follows a Poisson distribution, and the probability of k earthquakes occurring within the elapsed time Te since the last earthquake is:

[0040]

[0041] Among them, k is the number of earthquakes, λ is the average occurrence rate of random events per unit time (or unit area), and for earthquake events, the normalized value is used. As λ, where is the median of the earthquake recurrence interval in the study area, which can eliminate the absolute differences in earthquake recurrence intervals between different fault segments or different magnitude ranges. An earthquake occurring within the earthquake elapsed time Te is equivalent to the inverse event of no earthquake (k = 0). The probability calculation of no earthquake (k = 0) within the elapsed time is relatively simple:

[0042]

[0043] Therefore, the probability of an earthquake occurring within the elapsed time Te (k ≥ 1) is:

[0044]

[0045] The situation faced in actual work is that it is known that no earthquake with a magnitude above the target has occurred in a certain area within the elapsed time, and the probability of an earthquake with a magnitude above the preset target occurring within a certain time range in the future (i.e., the background earthquake probability) is calculated. Therefore:

[0046]

[0047] Among them, the preset target magnitude is a magnitude 5 or above earthquake in area A by default; and a magnitude 7 or above earthquake in area B by default.

[0048] Step S103: Determine the prediction result of the earthquake hazard probability of each grid area in the target area according to the precursor anomaly parameter and the background earthquake probability.

[0049] In this embodiment, since the precursor anomaly parameter includes multiple abnormal data, the earthquake hazard probability of a single abnormal data in the precursor anomaly parameter of each grid is determined by the precursor anomaly parameter and the background earthquake probability. Then, the earthquake hazard probability of a single abnormal data in the precursor anomaly parameter of each grid is calculated by comprehensive probability, thereby obtaining the earthquake hazard probability prediction result of each grid.

[0050] An embodiment of the present invention provides a method for predicting earthquake hazard probability, which includes: obtaining an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in the observation data of a geophysical observation device and a geochemical observation device; the target area is pre-divided into multiple grid areas; according to the earthquake catalog, the background probability of a target earthquake event in each grid area is calculated by a preset Poisson distribution model; according to the precursor anomaly parameters and the background probability of an earthquake, the prediction result of the earthquake hazard probability of each grid area in the target area is determined. The method automatically predicts the earthquake hazard probability through the abnormal data in the observation data of the geophysical observation device and the geochemical observation device, and determines the prediction result of the earthquake hazard probability of each grid area by dividing the target area into grids, thereby improving the accuracy of the earthquake hazard probability prediction result.

[0051] Example 2

[0052] exist Figure 1 Based on the method shown, the present invention also provides another earthquake hazard probability prediction method. Figure 2 A flow chart of another earthquake hazard probability prediction method provided by an embodiment of the present invention is shown as follows: Figure 2As can be seen, the method comprises the following steps:

[0053] Step S201: Obtain an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in observation data of geophysical observation equipment and geochemical observation equipment; the target area is pre-divided into a plurality of grid areas.

[0054] In one embodiment, the precursor anomaly parameters include: the R value of the anomaly data, the longitude and latitude of the anomaly data, the start time and end time of the anomaly data, the preset prediction radius of the anomaly data, and the preset forecast days of the anomaly data.

[0055] The R value of the abnormal data is calculated based on the following formula:

[0056]

[0057] Among them, a represents the number of earthquakes reported correctly; b represents the total number of earthquakes that should be predicted; c represents the time taken for prediction; and d represents the total time of prediction research.

[0058] Here, the R-value score actually reflects the degree of correlation between the anomaly and the earthquake. The higher the accuracy rate of the anomaly and the lower the time occupancy rate, the higher its R-value will be. Therefore, when an anomaly with a higher R-value occurs, the possibility of an earthquake is increasing, that is, a high probability; when an anomaly with a lower R-value occurs or no anomaly occurs, the possibility of an earthquake is smaller, that is, a low probability. The prediction range of the anomaly is determined based on the effective earthquake cases of the anomaly. When the prediction area overlaps, it indicates that the probability of an earthquake is higher. Therefore, the earthquake hazard probability based on the R-value score can more objectively and accurately express the abnormal earthquake prediction results in different prediction methods.

[0059] Step S202: According to the earthquake catalog, the background probability of the target earthquake event in each grid area is calculated by a preset Poisson distribution model.

[0060] The calculated data comes from the earthquake catalog, which includes some information about the earthquake, such as time, longitude and latitude, magnitude, focal depth, etc. By selecting earthquakes within a certain period of time, the background earthquake probability can be calculated using the Poisson distribution model.

[0061] Step S203: Determine the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid according to the precursory abnormal parameters and the background earthquake probability.

[0062] In this embodiment, the probability of occurrence of the abnormal data is first determined based on the precursor abnormal parameters, and then the earthquake hazard probability of a single abnormal data in the precursor abnormal parameters of each grid is determined based on the probability of occurrence of the abnormal data and the background earthquake probability.

[0063] In one embodiment, the step of determining the probability of occurrence of the abnormal data according to the precursor abnormality parameter includes: first, determining the duration of the abnormal data and the year in which the abnormal data lasts according to the precursor abnormality parameter. Then, determining the probability of occurrence of the abnormal data according to the duration of the abnormal data and the year in which the abnormal data lasts.

[0064] Here, the probability of the abnormal data occurring is calculated by the following formula:

[0065]

[0066] Among them, P' represents the probability of the abnormal data occurring; T1 represents the duration of the abnormal data; and T2 represents the number of years the abnormal data lasts.

[0067] Further, the step of determining the seismic hazard probability of a single abnormal data in the precursory abnormal parameters of each grid according to the precursory abnormal parameters and the background earthquake probability includes: calculating the seismic hazard probability of a single abnormal data in the precursory abnormal parameters of each grid by the following Bayesian formula:

[0068]

[0069] Among them, P(A|B) represents the probability of earthquake danger of the single abnormal data; P(B|A) represents the probability of an earthquake occurring and being predicted accurately; P(A) represents the probability of background earthquakes; P(B) is the probability of the abnormal data occurring within the prediction time. The probability of an earthquake occurring and being predicted accurately represented by P(B|A) is the R value of the abnormal data mentioned above.

[0070] Step S204: Calculate the earthquake hazard probability prediction result of each grid according to the earthquake hazard probability of a single abnormal data in the precursor abnormal parameter of each grid.

[0071] In this embodiment, the probability of the abnormal data occurring is calculated by the following comprehensive probability formula:

[0072] P=1-(1-P1)×(1-P2)……(1-P n )

[0073] Where P represents the earthquake hazard probability of each grid; P1, P2...P n It represents the earthquake hazard probability of 1 to n single abnormal data in each grid.

[0074] For ease of understanding, Figure 3 A schematic diagram of earthquake hazard probability prediction effect is provided for an embodiment of the present invention. Figure 3 As can be seen, taking the earthquake hazard probability of the underground fluid anomaly R value score in 2021 as an example, the results show that multiple earthquakes of magnitude 5 or above have occurred in high-probability nearby areas such as Yunnan and Sichuan. In particular, the Yangbi earthquake in 2021 occurred in the most significant place in the high-probability area. Some earthquakes of magnitude 5 or above have also occurred in low-probability areas such as Qinghai and Tibet, which is mainly related to the weak earthquake monitoring capabilities in the region and the absence of abnormalities.

[0075] An earthquake hazard probability prediction method provided by an embodiment of the present invention includes: obtaining an earthquake catalog and a precursor anomaly parameter of a target area; wherein the precursor anomaly parameter is an abnormal data in the observation data of a geophysical observation device and a geochemical observation device; the target area is pre-divided into multiple grid areas; according to the earthquake catalog, the background seismic probability of the target earthquake event in each grid area is calculated by a preset Poisson distribution model; according to the precursor anomaly parameter and the background seismic probability, the earthquake hazard probability of a single abnormal data in the precursor anomaly parameter of each grid is determined; according to the earthquake hazard probability of a single abnormal data in the precursor anomaly parameter of each grid, the earthquake hazard probability prediction result of each grid is calculated. The method automatically predicts the earthquake hazard probability through the abnormal data in the observation data of the geophysical observation device and the geochemical observation device, and determines the earthquake hazard probability of a single abnormal data in each grid area by dividing the target area into grids, and finally further improves the accuracy of the earthquake hazard probability prediction result according to the prediction result of the earthquake hazard probability of a single abnormal data in the precursor anomaly parameter of each grid.

[0076] Example 3

[0077] The embodiment of the present invention also provides a device for predicting earthquake hazard probability, such as Figure 4 The present invention provides a schematic diagram of a structure of an earthquake hazard probability prediction device, including:

[0078] The data acquisition module 41 is used to obtain the earthquake catalog and precursor anomaly parameters of the target area; wherein the precursor anomaly parameters are abnormal data in the observation data of the geophysical observation equipment and the geochemical observation equipment; the target area is pre-divided into multiple grid areas.

[0079] The background earthquake probability calculation module 42 is used to calculate the background earthquake probability of the target earthquake event in each grid area according to the earthquake catalog through a preset Poisson distribution model.

[0080] The earthquake hazard probability calculation module 43 is used to determine the prediction result of the earthquake hazard probability of each grid area in the target area according to the precursor anomaly parameters and the background earthquake probability.

[0081] The data acquisition module 41, the background earthquake probability calculation module 42 and the earthquake hazard probability calculation module 43 are connected in sequence.

[0082] In one embodiment, the earthquake hazard probability calculation module 43 is also used to determine the earthquake hazard probability prediction result of each grid according to the precursory abnormal parameters and the background earthquake probability, and the steps include: determining the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid according to the precursory abnormal parameters and the background earthquake probability; calculating the earthquake hazard probability prediction result of each grid according to the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid.

[0083] In one embodiment, the earthquake hazard probability calculation module 43 is also used to determine the probability of occurrence of the abnormal data based on the precursory abnormal parameters; based on the probability of occurrence of the abnormal data and the background earthquake probability, determine the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid.

[0084] In one of the embodiments, the earthquake hazard probability calculation module 43 is also used to determine the duration of the abnormal data and the years in which the abnormal data persist based on the precursor anomaly parameters; and determine the probability of the abnormal data occurring based on the duration of the abnormal data and the years in which the abnormal data persist.

[0085] In one embodiment, the earthquake hazard probability calculation module 43 is further used to calculate the earthquake hazard probability of a single abnormal data in the precursor abnormal parameter of each grid by using the following Bayesian formula: Among them, P(A|B) represents the earthquake hazard probability of the single abnormal data; P(B|A) represents the probability of an earthquake occurring and being accurately predicted; P(A) represents the background earthquake probability; and P(B) is the probability of the abnormal data occurring within the prediction time.

[0086] In one embodiment, the earthquake hazard probability calculation module 43 is further used to calculate the probability of occurrence of the abnormal data by the following comprehensive probability formula: P = 1-(1-P1) × (1-P2) ... (1-P n ), where P represents the earthquake hazard probability of each grid; P1, P2...P n It represents the earthquake hazard probability of 1 to n single abnormal data in each grid.

[0087] The earthquake hazard probability prediction device provided by the embodiment of the present invention has the same technical features as the earthquake hazard probability prediction method provided by the above embodiment, so it can also solve the same technical problems and achieve the same technical effects. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the above method embodiment, and will not be repeated here.

[0088] Example 4

[0089] This embodiment provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the earthquake hazard probability prediction method.

[0090] This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the earthquake hazard probability prediction method are implemented.

[0091] See also Figure 5 The schematic diagram of the structure of an electronic device shown in the figure comprises: a memory 51 and a processor 52. The memory 51 stores a computer program that can be run on the processor 52. When the processor executes the computer program, the steps provided by the above-mentioned earthquake hazard probability prediction method are implemented.

[0092] like Figure 5 As shown, the device further includes: a bus 53 and a communication interface 54, a processor 52, a communication interface 54 and a memory 51 are connected via the bus 53; the processor 52 is used to execute executable modules stored in the memory 51, such as computer programs.

[0093] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 54 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0094] The bus 53 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0095] Among them, the memory 51 is used to store the program, and the processor 52 executes the program after receiving the execution instruction. The method performed by the earthquake hazard probability prediction device disclosed in any embodiment of the present invention can be applied to the processor 52, or implemented by the processor 52. The processor 52 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 52. The above processor 52 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 52 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0096] Furthermore, an embodiment of the present invention also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor 52, the machine-executable instructions prompt the processor 52 to implement the above-mentioned earthquake hazard probability prediction method.

[0097] The electronic device and computer-readable storage medium provided by the embodiments of the present invention have the same technical features, and therefore can solve the same technical problems and achieve the same technical effects.

[0098] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0099] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

Claims

1. A method for predicting earthquake hazard probability, characterized in that: include: Obtaining an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in the observation data of a geophysical observation device and a geochemical observation device; the target area is pre-divided into a plurality of grid areas; the precursor anomaly parameters also include: the R value of the abnormal data, the longitude and latitude of the abnormal data, the start time and the end time of the abnormal data, the preset prediction radius of the abnormal data, and the preset forecast days of the abnormal data; According to the earthquake catalog, the background probability of a target earthquake event in each grid area is calculated by a preset Poisson distribution model; The seismic hazard probability of a single abnormal data in the precursory abnormal parameters of each grid area in the target area is calculated by the following Bayesian formula: Wherein, P(A|B) represents the earthquake hazard probability of the single abnormal data; P(B|A) represents the probability of an earthquake occurring and being predicted accurately; P(A) represents the background earthquake probability; P(B) represents the probability of the abnormal data occurring within the prediction time; The probability of the abnormal data occurring is calculated by the following comprehensive probability formula: P=1-(1-P1)×(1-P2)……(1-P n ) Wherein, P represents the earthquake hazard probability of each grid; P1, P2...P n Represents the seismic hazard probability of 1 to n single abnormal data in each grid.

2. The earthquake risk probability prediction method according to claim 1, characterized in that: The R value of the abnormal data is calculated based on the following formula: Among them, a represents the number of earthquakes reported correctly; b represents the total number of earthquakes that should be predicted; c represents the time taken for prediction; and d represents the total time of prediction research.

3. The earthquake hazard probability prediction method according to claim 2, characterized in that: The step of determining the earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid according to the precursory abnormal parameters and the background earthquake probability comprises: Determining the probability of occurrence of the abnormal data according to the precursor abnormality parameter; The earthquake hazard probability of a single abnormal data in the precursory abnormal parameters of each grid is determined according to the probability of occurrence of the abnormal data and the background earthquake probability.

4. The earthquake hazard probability prediction method according to claim 3, characterized in that: The step of determining the probability of occurrence of the abnormal data according to the precursor abnormality parameter comprises: Determine the duration of the abnormal data and the year in which the abnormal data lasts according to the precursor anomaly parameters; The probability of occurrence of the abnormal data is determined according to the duration of the abnormal data and the year in which the abnormal data persists.

5. The earthquake hazard probability prediction method according to claim 4, characterized in that: The step of determining the probability of occurrence of the abnormal data according to the duration of the abnormal data and the year in which the abnormal data lasts comprises: The probability of the abnormal data occurring is calculated by the following formula: Among them, P ， represents the probability of occurrence of the abnormal data; T1 represents the duration of the abnormal data; T2 represents the year in which the abnormal data lasts.

6. An earthquake hazard probability prediction device, characterized in that: include: A data acquisition module is used to acquire an earthquake catalog and precursor anomaly parameters of a target area; wherein the precursor anomaly parameters are abnormal data in the observation data of a geophysical observation device and a geochemical observation device; the target area is pre-divided into a plurality of grid areas; the precursor anomaly parameters also include: an R value of the abnormal data, the longitude and latitude of the abnormal data, the start time and end time of the abnormal data, a preset prediction radius of the abnormal data, and a preset forecast number of days for the abnormal data; A background earthquake probability calculation module, used to calculate the background earthquake probability of the target earthquake event in each grid area according to the earthquake catalog by using a preset Poisson distribution model; The earthquake hazard probability calculation module is used to calculate the earthquake hazard probability of a single abnormal data in the precursor abnormal parameter of each grid area in the target area by the following Bayesian formula: Wherein, P(A|B) represents the earthquake hazard probability of the single abnormal data; P(B|A) represents the probability of an earthquake occurring and being predicted accurately; P(A) represents the background earthquake probability; P(B) represents the probability of the abnormal data occurring within the prediction time; The probability of the abnormal data occurring is calculated by the following comprehensive probability formula: P=1-(1-P1)×(1-P2)……(1-P n ) Wherein, P represents the earthquake hazard probability of each grid; P1, P2...P n Represents the seismic hazard probability of 1 to n single abnormal data in each grid.

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