Earthquake early warning and false alarm prevention method and system

Abstract

The invention relates to the field of earthquake early warning, in particular to an earthquake early warning false alarm prevention method and system, and the method comprises the steps: obtaining an earthquake data set of each monitoring point, and the earthquake data set comprises the acceleration of an earthquake wave; calculating a characteristic value of the seismic wave, wherein the characteristic value is in positive correlation with the acceleration; and acquiring the acceleration of the mobile equipment and constructing an acceleration vector, calculating a state parameter of the mobile equipment, correcting the state parameter to obtain an optimal state parameter, and predicting the grade of the earthquake according to the optimal state parameter. According to the invention, the phenomenon of earthquake early warning false alarm can be prevented.

Description

Technical Field

[0001] The present invention relates to the field of earthquake early warning, and in particular to an earthquake early warning false alarm prevention method and system. Background Art

[0002] Earthquakes, also known as ground motions or seismic vibrations, are natural phenomena caused by the rapid release of energy from the earth's crust, which generates seismic waves. The plates on the earth squeeze and collide with each other, causing displacement and rupture at the edges and inside the plates, which is the main cause of earthquakes. Earthquake early warning refers to the use of the characteristic that the propagation speed of seismic waves is lower than the propagation speed of radio waves when an earthquake occurs, to warn the target area where the seismic waves have not yet arrived in advance. After receiving the warning, the public and projects take evacuation and emergency measures to reduce casualties and secondary disasters.

[0003] The Chinese patent application document with publication number CN116340757A discloses a feature-adaptive earthquake early warning magnitude prediction method and system, which includes the following steps: S1: obtaining historical earthquake data, performing feature selection from it, and obtaining the selected features; S2: establishing an earthquake early warning magnitude prediction model based on a support vector machine according to the selected features; S3: obtaining real-time earthquake data, and obtaining selected real-time feature data; S4: inputting the selected real-time feature data into the earthquake early warning magnitude prediction model based on a support vector machine to obtain the predicted earthquake early warning magnitude. Through feature selection, representative features are summarized from the earthquake signal, and a classification model is constructed based on this, which improves the magnitude assessment effect and reduces the computational complexity.

[0004] During earthquake early warning, there are non-seismic interferences, such as explosion vibrations and collisions between objects, which will generate signals similar to seismic waves, causing the system to misjudge them as earthquakes, resulting in low accuracy of the system's earthquake early warning. Summary of the invention

[0005] In order to solve the problem of false alarms in the earthquake early warning process, the present invention provides an earthquake early warning false alarm prevention method and system.

[0006] In a first aspect, the present invention provides an earthquake early warning and anti-false alarm method, which adopts the following technical solution: Obtaining a seismic data set at each monitoring point, the seismic data set including the acceleration of the seismic wave; calculating the characteristic value of the seismic wave, the characteristic value is positively correlated with the acceleration; The acceleration of the mobile device is obtained and the acceleration vector is constructed. The state parameters of the mobile device are calculated, the state parameters are corrected to obtain the optimal state parameters, and the earthquake level is predicted based on the optimal state parameters. The expression of the state parameters is: ; In the formula, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the acceleration of mobile device j corresponding to monitoring point i, represents the acceleration vector of mobile device j corresponding to monitoring point i, represents the propagation acceleration vector of the seismic wave in the geological environment corresponding to the monitoring point i, represents the characteristic value of the seismic wave at monitoring point i, represents the geological density difference corresponding to monitoring point i, Represents the distance between mobile device j and monitoring point i.

[0007] By calculating the device status parameters, we can understand the extent to which mobile devices are affected by seismic waves during an earthquake. That is, by observing mobile devices in motion, we can understand the intensity of seismic waves, and thus judge the current earthquake level based on the intensity of seismic waves. The obtained earthquake level can be compared with the earthquake level obtained from the outside world, thereby preventing false earthquake warnings.

[0008] Preferably, the expression of the eigenvalue is: ; In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, Indicates the distance from the epicenter.

[0009] The eigenvalues ​​are calculated by factors of multiple dimensions, which improves the accuracy of the eigenvalue calculation results.

[0010] Preferably, the expression of the eigenvalue is: ; In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, represents the epicenter distance, and exp represents the exponential function with e as the base.

[0011] The characteristic value can be used to understand the degree of influence of seismic waves on the monitoring point and the characteristics of the seismic waves. The larger the characteristic value, the greater the degree of damage caused by the seismic waves. The higher the corresponding earthquake level, the greater the influence of seismic waves on the monitoring point.

[0012] Preferably, the method for correcting the state parameter is: Acquire a historical data set, the historical data set including characteristic values ​​of seismic waves in historical earthquakes; Calculate the loss function of the generator and discriminator in the GAN algorithm, and use the GAN algorithm to adjust the state parameters to obtain the optimal state parameters; ; In the formula, represents the loss function of the generator, represents the loss function of the discriminator, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the characteristic value of the lth seismic wave in the historical earthquake at the monitoring point i, represents the expected value of sampling in the historical real data distribution, represents the expected value of generating data sampled from the prior distribution.

[0013] By constructing a loss function, the accuracy of the GAN algorithm in correcting state parameters is improved, and the accuracy of the optimal state parameters is further improved, which facilitates the prediction of earthquake levels.

[0014] Preferably, the expression of propagation acceleration is: ; In the formula, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the component of acceleration on the X-axis of the spatial coordinate system, represents the component of acceleration on the y-axis of the spatial coordinate system, Represents the component of acceleration on the z-axis of the spatial coordinate system.

[0015] Preferably, the method for predicting the level of an earthquake based on the optimal state parameter is: setting multiple state parameter intervals, each state parameter corresponds to an earthquake level, and obtaining the earthquake level according to the state parameter interval into which the optimal state parameter falls.

[0016] Predicting the current earthquake level through optimal state parameters provides a theoretical basis for earthquake early warning and prevents false earthquake alarms.

[0017] Preferably, the seismic waves include longitudinal waves and transverse waves.

[0018] In a second aspect, the present invention provides an earthquake early warning and anti-false alarm system, which adopts the following technical solution: An earthquake early warning and false alarm prevention system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an earthquake early warning and false alarm prevention method according to the above is implemented.

[0019] The above-mentioned earthquake early warning and false alarm prevention method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor. Thus, a system is made based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: By calculating the device status parameters, we can understand the extent to which mobile devices are affected by seismic waves during an earthquake. That is, by observing mobile devices in motion, we can understand the intensity of seismic waves, and thus judge the current earthquake level based on the intensity of seismic waves. The obtained earthquake level can be compared with the earthquake level obtained from the outside world, thereby preventing false earthquake warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 It is a flow chart of an earthquake early warning and false alarm prevention method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a method for preventing false alarms of earthquake early warning, referring to Figure 1 , including the following steps, as follows: S1: Obtain the earthquake dataset for each monitoring point.

[0026] The seismic data set includes the propagation acceleration of seismic waves, the time when seismic waves arrive at the monitoring point, and the distance from the epicenter; among them, the acceleration is calculated from the acceleration components in each direction of the spatial coordinate system, and the seismic waves include longitudinal waves and transverse waves.

[0027] The expression of propagation acceleration is: ; In the formula, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the component of acceleration on the X-axis of the spatial coordinate system, represents the component of acceleration on the y-axis of the spatial coordinate system, Represents the component of acceleration on the z-axis of the spatial coordinate system.

[0028] S2: Calculate the eigenvalue of the seismic wave, which is positively correlated with the acceleration.

[0029] In one embodiment, the expression of the characteristic value is: ; In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, Indicates the distance from the epicenter.

[0030] The greater the propagation acceleration of the seismic wave, the faster the seismic wave reaches the monitoring point, and the greater the impact of the seismic wave on the monitoring point; similarly, the shorter the time required for the seismic wave to reach the monitoring point and the smaller the distance from the epicenter, the greater the impact of the seismic wave on the monitoring point; the characteristic value of the seismic wave at the monitoring point comprehensively reflects the degree of impact of the seismic wave on the monitoring point and the characteristics of the seismic wave. Specifically, the larger the characteristic value, the greater the degree of damage caused by the seismic wave, the higher the corresponding earthquake level, and the greater the impact of the seismic wave on the monitoring point; conversely, the smaller the characteristic value, the smaller the degree of damage caused by the seismic wave, the lower the corresponding earthquake level, and the smaller the impact of the seismic wave on the monitoring point.

[0031] In one embodiment, the expression of the characteristic value is: ; In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, represents the epicenter distance, and exp represents the exponential function with e as the base.

[0032] S3: Obtain the acceleration of the mobile device and construct an acceleration vector, and calculate the state parameters of the mobile device.

[0033] A mobile device is set at the monitoring point. The mobile device may be an electronic device in motion, such as a mobile phone or a tablet. The acceleration of the mobile device in motion is collected, and an acceleration vector is constructed according to the acceleration.

[0034] The expression of the state parameter is: ; In the formula, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the acceleration of mobile device j corresponding to monitoring point i, represents the acceleration vector of mobile device j corresponding to monitoring point i, represents the propagation acceleration vector of the seismic wave in the geological environment corresponding to the monitoring point i, represents the characteristic value of the seismic wave at monitoring point i, represents the geological density difference corresponding to the monitoring point i, specifically the difference between the geological density of the monitoring point and the density of the epicenter. Represents the distance between mobile device j and monitoring point i.

[0035] It reflects the impact of the acceleration of the mobile device on the acceleration of seismic wave propagation. The larger the value, the greater the impact of the moving mobile device on the propagation acceleration of the collected seismic waves. This preliminarily indicates that the mobile device is more affected by the earthquake, and the higher the earthquake level. It reflects the influence of geological conditions and distance on equipment status parameters.

[0036] The device status parameters as a whole reflect the degree to which mobile devices are affected by seismic waves during an earthquake. That is, by observing mobile devices in motion, the intensity of seismic waves can be understood, reducing the interference of external factors on the earthquake level during the earthquake early warning process.

[0037] S4: Correct the state parameters to obtain the optimal state parameters.

[0038] Obtain a historical data set, which includes the characteristic values ​​of seismic waves in historical earthquakes; calculate the loss function of the generator and the discriminator in the GAN algorithm, and use the GAN algorithm to adjust the state parameters to obtain the optimal state parameters; ; In the formula, represents the loss function of the generator, represents the loss function of the discriminator, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the characteristic value of the lth seismic wave in the historical earthquake at the monitoring point i, represents the expected value of sampling in the historical real data distribution, represents the expected value of generating data sampled from the prior distribution, Represents the logarithmic function with base e.

[0039] pass Reflecting the confidence of real data, It is an existing technology to reflect the confidence of the generated data and adjust the state parameters using the GAN algorithm. The specific steps will not be repeated here.

[0040] S5: Predict the magnitude of an earthquake based on the optimal state parameters.

[0041] Set multiple state parameter intervals, each state parameter corresponds to an earthquake level, and obtain the earthquake level according to the state parameter interval that the optimal state parameter falls into. Compare the obtained earthquake level with the earthquake level in the external earthquake information received. If the two results are inconsistent, issue a prompt message of inconsistent levels to prevent false alarms of earthquake early warnings.

[0042] Exemplarily, the state parameter interval is , and , corresponding to the earthquake levels of 1, 2 and 3 respectively. When the state parameter falls into the interval in earthquake prediction It indicates that the current earthquake level is 2.

[0043] An embodiment of the present invention further discloses an earthquake early warning false alarm prevention system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an earthquake early warning false alarm prevention method according to the present invention is implemented.

[0044] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0045] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0046] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0047] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preventing false alarms of earthquake early warning, characterized in that: Includes steps: Obtaining a seismic data set at each monitoring point, the seismic data set including the acceleration of the seismic wave; calculating the characteristic value of the seismic wave, the characteristic value is positively correlated with the acceleration; The acceleration of the mobile device is obtained and the acceleration vector is constructed. The state parameters of the mobile device are calculated, the state parameters are corrected to obtain the optimal state parameters, and the earthquake level is predicted based on the optimal state parameters. The expression of the state parameters is: ； In the formula, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the acceleration of mobile device j corresponding to monitoring point i, represents the acceleration vector of mobile device j corresponding to monitoring point i, represents the propagation acceleration vector of the seismic wave in the geological environment corresponding to the monitoring point i, represents the characteristic value of the seismic wave at monitoring point i, represents the geological density difference corresponding to monitoring point i, Represents the distance between mobile device j and monitoring point i.

2. The earthquake early warning and false alarm prevention method according to claim 1, characterized in that: The expression of the eigenvalue is: ； In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, Indicates the distance from the epicenter.

3. The earthquake early warning and false alarm prevention method according to claim 1, characterized in that: The expression of the eigenvalue is: ； In the formula, represents the characteristic value of the seismic wave at monitoring point i, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the time required for the seismic wave to reach monitoring point i, represents the epicenter distance, and exp represents the exponential function with e as the base.

4. The earthquake early warning and false alarm prevention method according to claim 2 or 3, characterized in that: The method for correcting the state parameters is: Acquire a historical data set, the historical data set including characteristic values ​​of seismic waves in historical earthquakes; Calculate the loss function of the generator and discriminator in the GAN algorithm, and use the GAN algorithm to adjust the state parameters to obtain the optimal state parameters; ； In the formula, represents the loss function of the generator, represents the loss function of the discriminator, represents the device state parameter of mobile device j corresponding to monitoring point i, represents the characteristic value of the lth seismic wave in the historical earthquake at the monitoring point i, represents the expected value of sampling in the historical real data distribution, represents the expected value of generating data sampled from the prior distribution.

5. The earthquake early warning and false alarm prevention method according to claim 2 or 3, characterized in that: The expression of propagation acceleration is: ； In the formula, represents the propagation acceleration of seismic waves in the geological environment corresponding to the monitoring point i, represents the component of acceleration on the X-axis of the spatial coordinate system, represents the component of acceleration on the y-axis of the spatial coordinate system, Represents the component of acceleration on the z-axis of the spatial coordinate system.

6. The earthquake early warning and false alarm prevention method according to claim 1, characterized in that: The method for predicting the level of an earthquake based on the optimal state parameters is: setting multiple state parameter intervals, each state parameter corresponds to an earthquake level, and obtaining the earthquake level based on the state parameter interval into which the optimal state parameter falls.

7. The earthquake early warning and false alarm prevention method according to claim 1, characterized in that: Seismic waves include longitudinal waves and transverse waves.

8. An earthquake early warning and anti-false alarm system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an earthquake early warning and false alarm prevention method according to any one of claims 1-7 is implemented.

Images