CN112684494A - Earthquake early warning method - Google Patents
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Abstract
The invention discloses an earthquake early warning method which comprises the steps of calculating and storing an earthquake wave field database in advance, solving earthquake focus parameters by using the earthquake wave field database and observation data acquired by earthquake observation equipment, calculating earthquake motion parameter data by using the earthquake focus parameters and the earthquake wave field database, delimiting an earthquake early warning area by using the earthquake motion parameter data and early warning conditions, and transmitting an earthquake early warning instruction to the earthquake early warning area. The earthquake early warning area can be accurately divided in real time through the earthquake early warning area dividing method, earthquake early warning and earthquake-proof and disaster-reduction capabilities are further improved, the panic emotion of the public in the non-early warning area is further reduced, and the waste of social resources is reduced.
Description
Technical Field
The invention belongs to the technical field of earthquake monitoring and prediction, and particularly relates to an earthquake early warning method.
Background
The earthquake early warning is a technology of rapidly determining three earthquake elements and an earthquake source mechanism and predicting earthquake motion parameters according to observation records of an earthquake station adjacent to the epicenter and the seismology principle after an earthquake occurs under the condition of implementing earthquake monitoring by using an earthquake observation station network, and issuing warning information to an area where destructive earthquake waves do not arrive and the predicted earthquake motion parameters exceed a defense standard. The earthquake early warning aims to reduce casualties and economic property loss to the maximum extent after destructive earthquakes occur.
In the existing earthquake early warning technology, the intensity of a defense setting position or a position where receiving equipment is located is calculated on the basis of the earthquake center distance, and the intensity threshold are used as early warning judgment conditions (such as invention patent 201910755090.8 and invention patent 202010213813.4), so that the method for judging the earthquake early warning area is relatively rough, the earthquake motion parameters and the building earthquake motion defense setting standard enforced by the state are not fully considered, and false alarm and missed alarm are easily caused. For the areas with the building defense standard higher than the earthquake motion peak acceleration, the wrong earthquake early warning causes the public excessive reaction to cause unnecessary social resource waste.
At present, published and implemented national standards and local standards include, but are not limited to, documents such as Chinese earthquake parameter segmentation charts (GB18306-2015), building earthquake-resistant design specifications (GB50011-2010), nuclear power plant earthquake-resistant design specifications (GB50267-97), etc., which define earthquake-resistant earthquake fortification standards of construction projects and provide earthquake early warning area division basis for earthquake early warning. Therefore, it is necessary to research a reliable earthquake early warning method to realize accurate division of earthquake early warning areas and reduce unnecessary social resource waste caused by large-scale rough earthquake early warning.
Disclosure of Invention
In order to solve the problems, the invention provides an earthquake early warning method for accurately dividing earthquake early warning areas and reducing unnecessary social resource waste.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an earthquake early warning method comprises the following steps;
si01, pre-calculating and storing a seismic wave field database;
s102, solving seismic source parameters by using a seismic wave field database and observation data acquired by seismic observation equipment;
s103, calculating earthquake motion parameter data by using earthquake source parameters and an earthquake wave field database;
and S104, defining an earthquake early warning area by using the earthquake motion parameter data and the early warning condition, and transmitting an earthquake early warning instruction to the earthquake early warning area.
Further, the step S101 of calculating and storing the seismic wave wavefield database includes calculating seismic wave wavefield data by using a digital earth model and a seismic wave wavefield forward algorithm, where the digital earth model includes a digital model which is constructed by describing seismic wave propagation media in the earth and can be described by using a grid, the seismic wave wavefield forward algorithm includes an accurate solution of a wave equation for calculating seismic wave values at grid node positions in the digital earth model and a numerical solution of the wave equation, and the database includes seismic wave wavefield data at grid node positions in the earth model and seismic wave wavefield data at non-grid node positions calculated by using an interpolation algorithm.
Further, the pre-calculating and storing the seismic wave wavefield database in S101 further includes storing the seismic wave wavefield database in the earth model by using a database compression algorithm, where the database compression algorithm includes a lossy compression algorithm and a lossless compression algorithm.
Further, in step S101, the seismic wave field includes one or more of a seismic facies travel time field, a P-wave initial motion polar field, a displacement field, a velocity field, an acceleration field, a stress field, and a strain field under the condition of a fundamental seismic source, where the seismic facies includes one or more of a longitudinal wave seismic facies, a transverse wave seismic facies, a surface wave seismic facies, a tail wave seismic facies, and a combined seismic facies, the condition of the fundamental seismic source includes a fundamental seismic source selected for calculating the seismic wave field database, and the fundamental seismic source includes one or more of a unit pulse force source, a combined pulse unit force source, a moment component seismic source, a moment tensor seismic source, and a fault model seismic source.
Further, the observation data collected by the seismological observation device in S102 includes the seismic waveform data, the arrival time data of the seismic facies and the P-wave polarity data of the position of the seismological observation device caused by the seismic event recorded by the seismological observation device, the seismic event is detected by analyzing the change in the attributes of the waveform and using an algorithm for the seismic event, the attributes of the waveform comprise the form of the waveform, the amplitude value of the waveform and the frequency component value of the waveform, the detection algorithm of the earthquake event comprises a long-time window ratio detection algorithm, an abruption information criterion detection algorithm, a cross-correlation detection algorithm, a threshold value detection algorithm and a machine learning and deep learning detection algorithm, the seismic wave waveform comprises one or more of a displacement waveform, a velocity waveform, an acceleration waveform, a stress waveform, a strain waveform, and a voltage value and a current value acquired by seismic observation equipment.
Further, the solving method for the parameters of the seismic source by using the seismic wave field database and the observation data collected by the seismic observation equipment comprises a travel time based solving method, a seismic facies polarity and amplitude based solving method, a seismic wave energy based solving method and a seismic wave waveform based solving method, wherein the travel time based solving method comprises a travel time equation solution, a travel time residual error method and a travel time residual error method, the seismic facies polarity and amplitude based solving method comprises a P wave initial motion method and a shear-longitudinal wave amplitude ratio solving method, the seismic wave energy based solving method comprises a wave field migration solution, a wave field superposition solution and a wave field interference solution, and the seismic wave waveform based solving method comprises a seismic wave waveform matching method, the matching comprises the step of calculating the error of numerical values in two groups of data by using an error function, wherein the error function is realized by calculating the norm of the difference value of the two groups of data, the two groups of data comprise observation data collected by earthquake observation equipment and theoretical data of the position of the earthquake observation equipment calculated by using a seismic wave field database, and the theoretical data comprise the arrival time of seismic facies, the P wave initial motion polarity and seismic wave waveforms.
Further, the calculation of the seismic motion parameter data by using the seismic source parameters and the seismic wave field database refers to calculation of seismic motion parameter data of a surface layer grid node position and seismic motion parameter data of a surface layer non-grid node position in the digital earth model, wherein the calculation of the seismic motion parameter data of the non-grid node position is realized by using an interpolation algorithm.
Further, the earthquake motion parameter data comprises an earthquake motion parameter time function, an earthquake motion parameter peak value and arrival time of the earthquake motion parameter peak value, the earthquake motion parameters comprise earthquake motion acceleration, earthquake motion speed and earthquake motion displacement, and the peak value refers to a maximum absolute value.
Further, the definition of the earthquake early warning area by using the earthquake motion parameter data and the early warning condition is realized by a threshold value judgment method, the earthquake early warning area is defined when the local earthquake parameter data exceeds a threshold value, the threshold value refers to earthquake motion defense standards, and the numerical value is selected according to the laws and local management methods of the people's republic of China including but not limited to the earthquake defense and disaster reduction law, and the national standards of the people's republic of China including but not limited to the earthquake motion parameter area division diagram, the building earthquake resistance design specifications and the nuclear power plant earthquake resistance design specifications.
Further, the transmitting of the earthquake early warning instruction to the earthquake early warning area means transmitting the early warning information to the earthquake early warning information receiving device in the earthquake early warning area in an electric signal manner under the condition that the national laws of the people's republic of China, the national standards of the people's republic of China and the local standards of the people's republic of China are met, wherein the national laws of the people's republic of China and the national standards of the people's republic of China include, but are not limited to, the earthquake prevention and mitigation law of the people's republic of China and the emergency release of earthquake early warning information.
After the scheme is adopted, the invention has the following beneficial effects:
(1) the earthquake three-element and earthquake focus mechanism can be accurately solved in real time by using the earthquake wave field database and the observation data acquired by the earthquake observation equipment, and the reliability and efficiency of earthquake focus positioning and earthquake focus mechanism solving are further improved.
(2) The earthquake source parameters and the earthquake wave field database can be used for accurately calculating the earthquake motion parameters in real time, and the reliability and efficiency in the aspect of calculating the earthquake motion parameters are further improved.
(3) The earthquake early warning area can be accurately defined by defining the earthquake early warning area by using the earthquake motion parameter data and the early warning condition, so that the earthquake early warning reliability and the refinement degree are further improved, the panic of a non-early warning area is avoided, and the social resource waste is avoided.
Drawings
FIG. 1 is an embodiment of a seismic warning method of the present disclosure;
FIG. 2 is an exemplary diagram of a digital earth model of the present specification;
FIG. 3 is an exemplary graph of a seismic event detection result waveform of the present disclosure;
FIG. 4 is an exemplary illustration of seismic source location results of the present description;
FIG. 5 is an exemplary illustration of seismic center position and seismic source mechanism inversion results of the present description;
FIG. 6 is a diagram illustrating the results of seismic parameter data according to the present disclosure;
fig. 7 is a diagram illustrating the result of the earthquake early warning area delineation in the present specification.
Detailed Description
The following is further detailed by way of specific embodiments: in order to make the technical solutions in the present specification better understood, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to fig. 1 to 7 of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, but not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
Fig. 1 is a diagram of an earthquake early warning method provided in an embodiment of the specification, where the earthquake early warning method includes:
101 pre-compute and store a seismic wave wavefield database,
102 solving the seismic source parameters by using the seismic wave field database and the observation data collected by the seismic observation equipment,
103 calculating seismic motion parameter data using the seismic source parameters and the seismic wave field database,
104 using the earthquake motion parameter data and the early warning condition to define an earthquake early warning area,
105 transmitting the earthquake early warning instruction to the earthquake early warning area.
As shown in fig. 101, the pre-calculating and storing a seismic wave wavefield database refers to calculating seismic wave wavefield data by using a digital earth model and a seismic wave wavefield forward algorithm, where the digital earth model refers to a digital model which is constructed by describing a seismic wave propagation medium in the earth and can be described by using a grid, and as shown in fig. 2, an exemplary diagram of the digital earth model provided in this specification is provided, the database may be seismic wave wavefield data at grid node (201, 203) positions in the earth model, and seismic wave wavefield data at non-grid node (202, 204) positions calculated by using an interpolation algorithm, and generally, the more grid nodes, the larger the database storage space is. Thus, database storage space may be reduced using database compression algorithms, which may be lossy compression and lossless compression.
As shown in fig. 102, the seismic source parameters are solved by using the seismic wave wavefield database and the observation data collected by the seismic observation device, fig. 3 to 5 are examples of calculation results of the seismic source parameters, fig. 3 is an example of a waveform of a detection result of a seismic event (301) provided by the present specification, fig. 4 is an example of a positioning result of a seismic source (401) provided by the present specification, and fig. 5 is an example of an inversion result of a seismic center (501) position and a seismic source mechanism (502) provided by the present specification. The seismic source parameters can be automatically and accurately solved by analyzing the observation data acquired by the seismic observation equipment.
As indicated by 103, the seismic source parameters and the seismic wave wavefield database are used to calculate seismic motion parameter data, and as shown in fig. 6, a seismic motion parameter data result diagram provided in this specification is shown, where the seismic motion parameter data refer to seismic motion parameter data at the surface layer mesh node (203) position and seismic motion parameter data at the surface layer non-mesh node (204) position in the digital earth model. The seismic motion parameter data can be accurately calculated in real time according to the seismic source parameters.
As shown in fig. 7, a result diagram of an earthquake early warning area (701) provided in this specification is defined, the earthquake early warning area can be defined in real time by using earthquake motion parameter data and early warning conditions, the early warning conditions are implemented by a threshold determination method, the threshold defined as the earthquake early warning area when the earthquake motion parameter data exceeds the threshold is an earthquake motion defense standard, and the numerical value is selected according to the national laws and local management methods of the people's republic of china including but not limited to the earthquake defense reduction law, and the national standards of the people's republic of china including but not limited to the regional drawings of the earthquake motion parameters of the china, the building earthquake resistance design specifications, and the nuclear power plant earthquake resistance design specifications.
After the earthquake early warning area is defined, as indicated by 105, transmitting an earthquake early warning instruction to the earthquake early warning area, wherein the transmitting of the earthquake early warning instruction to the earthquake early warning area refers to transmitting early warning information to earthquake early warning information receiving equipment in the earthquake early warning area in an electric signal manner under the condition that the national laws of the people's republic of China and the national standards and local standards of the people's republic of China, including but not limited to the earthquake prevention and disaster reduction law of the people's republic of China and the earthquake information issuing earthquake early warning information law of the emergency earthquake.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (8)
1. An earthquake early warning method is characterized in that: comprises the following steps;
s101, pre-calculating and storing a seismic wave field database;
s102, solving seismic source parameters by using a seismic wave field database and observation data acquired by seismic observation equipment;
s103, calculating earthquake motion parameter data by using earthquake source parameters and an earthquake wave field database;
and S104, defining an earthquake early warning area by using the earthquake motion parameter data and the early warning condition, and transmitting an earthquake early warning instruction to the earthquake early warning area.
2. A seismic early warning method according to claim 1, characterized in that: the step S101 of calculating and storing the seismic wave wavefield database includes calculating seismic wave wavefield data by using a digital earth model and a seismic wave wavefield forward algorithm, where the digital earth model includes a digital model which is constructed by describing a seismic wave propagation medium in the earth and can be described by using a grid, the seismic wave wavefield forward algorithm includes an accurate solution of a wave equation and a numerical solution of a wave equation for calculating a seismic wave wavefield value at a grid node position in the digital earth model, and the database includes seismic wave wavefield data at a grid node position in the earth model and seismic wave wavefield data at a non-grid node position calculated by using an interpolation algorithm.
3. A seismic early warning method according to claim 1, characterized in that: the pre-calculating and storing of the seismic wave field database in S101 further includes storing the seismic wave field database by using a database compression algorithm, where the database compression algorithm includes a lossy compression algorithm and a lossless compression algorithm.
4. A seismic early warning method according to claim 1, characterized in that: the seismic wave field in the step S101 includes one or more of a seismic facies travel time field, a P-wave initial motion polar field, a displacement field, a velocity field, an acceleration field, a stress field, and a strain field under the condition of a basic seismic source, where the seismic facies includes one or more of a longitudinal wave seismic facies, a transverse wave seismic facies, a surface wave seismic facies, a tail wave seismic facies, and a combined seismic facies, the basic seismic source condition includes a basic seismic source selected when a seismic wave field database is calculated, and the basic seismic source includes one or more of a unit pulse force source tensor, a combined unit pulse force source, a moment tensor component seismic source, a moment seismic source, and a fault model seismic source.
5. A seismic early warning method according to claim 1, characterized in that: the observation data collected by the seismological observation equipment in the S102 comprises seismic waveform data, arrival time data of seismic facies and P wave polarity data of the position of the seismological observation equipment caused by the seismic event, which are recorded by the seismological observation equipment, the seismic event is detected by analyzing the change in the attributes of the waveform and using an algorithm for the seismic event, the attributes of the waveform comprise the form of the waveform, the amplitude value of the waveform and the frequency component value of the waveform, the detection algorithm of the earthquake event comprises a long-time window ratio detection algorithm, an abruption information criterion detection algorithm, a cross-correlation detection algorithm, a threshold value detection algorithm and a machine learning and deep learning detection algorithm, the seismic wave waveform comprises one or more of a displacement waveform, a velocity waveform, an acceleration waveform, a stress waveform, a strain waveform, and a voltage value and a current value acquired by seismic observation equipment.
6. A seismic early warning method according to claim 5, characterized in that: the solving method for the earthquake focus parameters comprises a travel time based solving method, a seismic facies polarity and amplitude based solving method, a seismic wave energy based solving method and a seismic wave waveform based solving method, wherein the travel time based solving method comprises a travel time equation solving method, a travel time residual error method and a travel time difference residual error method, the seismic facies polarity and amplitude based solving method comprises a P wave initial motion method and a shear-longitudinal wave amplitude ratio method, the seismic wave energy based solving method comprises a seismic wave migration solution, a wave field superposition solution and a wave field interference solution, and the seismic wave waveform based solving method comprises a seismic wave waveform matching method, the matching comprises the step of calculating the error of numerical values in two groups of data by using an error function, wherein the error function is realized by calculating the norm of the difference value of the two groups of data, the two groups of data comprise observation data collected by earthquake observation equipment and theoretical data of the position of the earthquake observation equipment calculated by using a seismic wave field database, and the theoretical data comprise the arrival time of seismic facies, the P wave initial motion polarity and seismic wave waveforms.
7. A seismic early warning method according to claim 6, characterized in that: the calculation of the seismic motion parameter data by using the seismic source parameters and the seismic wave field database refers to the calculation of seismic motion parameter data of a surface layer grid node position and seismic motion parameter data of a surface layer non-grid node position in the digital earth model, wherein the calculation of the seismic motion parameter data of the non-grid node position is realized by using an interpolation algorithm.
8. A seismic early warning method according to claim 7, characterized in that: the earthquake motion parameter data comprise earthquake motion parameter time functions, earthquake motion parameter peak values and arrival times of the earthquake motion parameter peak values, the earthquake motion parameters comprise earthquake motion acceleration, earthquake motion speed and earthquake motion displacement, and the peak values refer to maximum absolute values.
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| CN114298104A (en) * | 2021-12-28 | 2022-04-08 | 中国地震局工程力学研究所 | Earthquake early warning method, earthquake early warning device, electronic equipment and computer readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1701243A (en) * | 2003-08-27 | 2005-11-23 | 恩益禧慕百霖株式会社 | Earthquake prediction method and system thereof |
| CN104462814A (en) * | 2014-12-03 | 2015-03-25 | 沈阳工业大学 | Grid grading method for near-surface ground motion simulation |
| CN111796321A (en) * | 2019-05-23 | 2020-10-20 | 南京理工大学 | Earthquake early warning method and system for high-speed railway |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1701243A (en) * | 2003-08-27 | 2005-11-23 | 恩益禧慕百霖株式会社 | Earthquake prediction method and system thereof |
| CN104462814A (en) * | 2014-12-03 | 2015-03-25 | 沈阳工业大学 | Grid grading method for near-surface ground motion simulation |
| CN111796321A (en) * | 2019-05-23 | 2020-10-20 | 南京理工大学 | Earthquake early warning method and system for high-speed railway |
Non-Patent Citations (3)
| Title |
|---|
| 中国大百科全书总编辑委员会编: "《中国大百科全书 固体地球物理学、测绘学、空间科学》", 30 September 2002, 中国大百科全书出版社 * |
| 王天韵: "地震动场实时预测方法研究", 《中国优秀博硕士学位论文全文数据库(博士)基础科学辑》 * |
| 高抒 等, 海洋出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114298104A (en) * | 2021-12-28 | 2022-04-08 | 中国地震局工程力学研究所 | Earthquake early warning method, earthquake early warning device, electronic equipment and computer readable storage medium |
| CN114298104B (en) * | 2021-12-28 | 2022-09-27 | 中国地震局工程力学研究所 | Earthquake early warning method, earthquake early warning device, electronic equipment and computer readable storage medium |
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