Method and device for generating aftershock sequence in earthquake

Abstract

The embodiment of the invention discloses a method and equipment for generating an aftershock sequence in an earthquake. The method comprises the following steps: acquiring event waveforms in seismological observation data, and picking up P-wave and S-wave seismic phases; carrying out technical screening and seismic correlation from the picked seismic phases, and determining a target seismic phase with the same event time-space correlation; according to the screened target seismic phase, seismic positioning is carried out, seismic parameter information is generated, and the seismic parameter information comprises position latitude and longitude and depth; processing the waveform of the earthquake event, and calculating a corresponding earthquake magnitude; according to the method, the earthquake parameter information and the earthquake magnitude information are synthesized, repositioning is carried out, and the aftershock sequence catalog containing the earthquake parameter information and the earthquake magnitude information is generated, so that the aftershock information is efficiently captured in the earthquake, the aftershock sequence is rapidly and accurately generated, and a reliable data basis is provided for subsequent earthquake activity analysis.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of geophysics, and in particular, to a method and device for generating an aftershock sequence in an earthquake. BACKGROUND

[0002] A moderate earthquake is often accompanied by a large number of aftershocks, and the location, size and time of the large number of aftershocks are different. Rapid and accurate determination of aftershocks is of great significance for analyzing seismogenic structure and mechanism, evaluating earthquake disaster distribution, etc. The increase in the number of seismic stations can improve the earthquake monitoring capability. In the construction of the aftershock sequence, if the data arrangement, phase picking, event correlation and earthquake location are based on manual operation, there are certain difficulties, and it is difficult to balance timeliness and completeness.

[0003] Therefore, there is a need for an efficient method for generating an aftershock sequence in an earthquake. SUMMARY

[0004] The embodiments of the present specification provide a method and device for generating an aftershock sequence in an earthquake, and a storage medium, to solve the technical problem that an efficient method for generating an aftershock sequence in an earthquake is needed.

[0005] To solve the above technical problem, one or more embodiments of the present specification are implemented as follows:

[0006] In a first aspect, the embodiments of the present specification provide a method for generating an aftershock sequence in an earthquake, comprising: obtaining event waveforms in seismic observation data and picking P-wave and S-wave phases; determining target phases with the same event space-time correlation through technical screening and seismic correlation from the picked phases; performing earthquake location according to the screened target phases to generate seismic parameter information, wherein the seismic parameter information includes position latitude and longitude and depth; processing the waveforms of the seismic events to calculate the corresponding magnitude; repositioning by integrating the seismic parameter information and the magnitude information, and generating an aftershock sequence catalog containing the seismic parameter information and the magnitude information.

[0007] In a second aspect, one or more embodiments of the present specification provide an electronic device, comprising:

[0008] at least one processor; and

[0009] a memory communicatively connected to the at least one processor; wherein

[0010] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0011] In a third aspect, the embodiments of the present specification provide a non-volatile computer storage medium storing computer executable instructions, when the computer executable instructions in the storage medium are read by a computer, the instructions cause one or more processors to execute the method as described in the first aspect.

[0012] The above at least one technical scheme adopted by one or more embodiments of the present specification can achieve the following beneficial effects: by acquiring event waveforms in seismic observation data, and picking up P-wave and S-wave phases; by technical screening and seismic association from the picked-up phases, target phases with the same event space-time association are determined; according to the screened target phases, seismic positioning is performed to generate seismic parameter information, wherein the seismic parameter information includes position latitude and longitude and depth; the waveforms of the seismic events are processed to calculate the corresponding magnitudes; the seismic parameter information and the magnitude information are integrated to perform relocation, and a sequence catalog of aftershocks containing the seismic parameter information and the magnitude information is generated, thereby realizing efficient extraction of aftershock information in earthquakes, and quickly and accurately generating an aftershock sequence, which provides a reliable data basis for subsequent seismic activity analysis. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can also be obtained according to these drawings without creative labor for those skilled in the art.

[0014] Figure 1 A flowchart of a method for generating an aftershock sequence in an earthquake and an apparatus provided by the embodiments of the present specification;

[0015] Figure 2 A schematic diagram of a processing flow for seismic data provided by the embodiments of the present specification;

[0016] Figure 3 A schematic diagram of phase picking by the embodiments of the present specification;

[0017] Figure 4 A schematic diagram of travel time clustering in the embodiments of the present specification;

[0018] Figure 5a A schematic diagram of space-time association of phases and determination of heterogeneous points in the embodiments of the present specification;

[0019] Figure 5b A schematic diagram of removing heterogeneous points;

[0020] Figure 6 A schematic diagram of seismic association by the grid search-based association method;

[0021] Figure 7 A schematic diagram of earthquake positioning according to an embodiment of this specification;

[0022] Figure 8 A schematic diagram of generating an aftershock sequence provided in an embodiment of this specification;

[0023] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0024] The embodiments of this specification provide a method, device, and storage medium for generating an aftershock sequence in an earthquake.

[0025] In order to help those skilled in the art better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0026] like Figure 1 As shown, Figure 1 A schematic diagram of a flow chart for generating an aftershock sequence in an earthquake provided in an embodiment of this specification.

[0027] Figure 1 The process in may include the following steps:

[0028] S102: Acquire event waveforms from seismic observation data and pick up P-wave and S-wave phases.

[0029] After an earthquake, there are often a large number of aftershocks. In a network of multiple seismic stations, seismic data (usually continuous seismic waveforms) can be obtained, which can then be processed and analyzed to construct a series of outputs from raw observation data to aftershock sequence catalogs.

[0030] In addition, the formats of earthquake data currently stored in the earthquake monitoring network are: SEED format, miniSEED format is the version without header information; and SAC format for data processing purposes. Figure 2 As shown, Figure 2The schematic diagram of the processing flow of the seismic data provided by the embodiment of the present specification. Based on the SEED format of the continuous waveform archived data produced by the station network MSDP software, initial processing is performed on it, including quality check, removal of 28K empty data files, filling of missing numbers according to time alignment and merging of files, merging according to one file per day, and finally generating data files in miniseed or SAC format, which are stored according to sampling rate, station network, year, station, day, and component.

[0031] The data of a day can also be stored in SAC format by day for ease of subsequent phase picking program or event waveform interception program processing by day. The reading and decompression of SEED data uses the rdseed tool, and the reading and processing of SAC data uses the SAC1.0 tool. The distribution of stations has good distance and azimuth angle coverage relative to the source area, thereby facilitating the automatic construction of aftershock sequences.

[0032] For phase picking, multiple methods are used to pick the S-wave phase in the seismic data in a mutually corroborative manner. This includes traditional digital processing methods and machine learning methods. Figure 3 As shown, Figure 3 The schematic diagram of the phase picking of the embodiment of the present specification.

[0033] In the traditional digital processing method, STA / LTA method is mainly used for threshold detection, AIC method is used for accurate picking of the detection curve, and amplitude and polarity are used to distinguish the phase type. The phase type includes P-wave phase and S-wave phase.

[0034] Specifically, first, the eigenvalue selection needs to be further optimized. According to tests, the use of standard, recursive, and delayed STA / LTA has less impact on the results, and to reduce the number of features, standard STA / LTA is uniformly selected. Originally, three-direction STA / LTA was used as a feature, and amplitude square was used as a feature function. Now, the three-direction energy is fused, and the following three types of feature functions are taken:

[0035] (1) Total energy: E3=Z 2 +N 2 +E 2

[0036] (2) Vertical energy: E e =Z 2

[0037] (3) Plane energy: H e = N 2 +E 2

[0038] Then, the Hilbert transform is applied to the signal to obtain the complex seismic wave. The instantaneous amplitude, frequency, and phase are obtained for each of the three directions. There are 10 types of instantaneous frequency. To prevent too many eigenvalues, the average of the instantaneous frequency of different calculation types is taken.

[0039] The AIC index has certain application limitations. It defines the trigger point as the global minimum value in the study section. Therefore, there must be only one event in the study section, and the judgment result is greatly affected by the signal-to-noise ratio.

[0040] In this process, the polarization analysis-related indicators can better distinguish P waves and S waves. First, the Hilbert transform is applied to the three-component signal to calculate the covariance matrix of the data. The ellipticity, linearity, and polarization strike angle are obtained.

[0041] Further, frequency domain analysis can also be performed, such as using wavelet transform: (1) Discrete wavelet transform: the wavelet coefficients on different scales are judged to time by the above-mentioned feature extraction, and the final result is obtained according to the consistency of different scales. (2) Continuous wavelet transform: the energy in different frequency domains is extracted as the eigenvalue input.

[0042] Through the foregoing traditional method, the phase type is finally obtained, and the P wave phase and S wave phase are accurately distinguished.

[0043] In addition, a machine learning-based method can also be used to pick up the phase type.

[0044] For example, a pre-trained neural network uses the data point confidence obtained by the network to perform phase picking. When the confidence exceeds a certain threshold, the corresponding P / S phase time and type are output. The selection of the confidence threshold will affect the construction of the final earthquake catalog. Due to the influence of network structure, training data, weighting strategy, etc., different networks also have different confidence performances for the same data.

[0045] The Luding earthquake is used as an example to illustrate the picking of seismic phases. The P wave and S wave phase picking based on neural network is used: According to the characteristics of the long-term operation of the fixed network and the multiple earthquakes in Sichuan, we use the historical earthquake records in the study area to find the optimal confidence threshold to further optimize the phase picking process. The accuracy (Precision), recall (Recall), and F1 of the phase recognition result are used to realize the optimization of the picking threshold. Through the corresponding evaluation, it can be judged how large the confidence threshold of the network used needs to be to identify the P wave and S wave phases that can be distinguished by humans in a complete manner, thereby guiding the intelligent picking of the phase time. Among them, F1 is the harmonic mean of P and R, F1 = 2P*R / (P+R).

[0046] Because the S-wave phase resolution is low, the initial jump point of the phase first arrival is usually not clear. Directly using the STA / LTA method for identification often has poor accuracy. The scheme uses polarity analysis, linearity analysis and artificial intelligence identification method to confirm each other to obtain clear S-wave phase types.

[0047] S104, from the picked phase, through technical screening and earthquake association, determine the target phase with the same event space-time association.

[0048] After picking up a series of phases, it is necessary to associate the phases on the event, that is, to associate based on time and space.

[0049] Specifically, the travel time clustering can be performed first. As shown in Figure 4 , Figure 4 is a schematic diagram of travel time clustering in the embodiments of the present specification. Assuming that the epicenter of the aftershock is t0, the p-wave velocity is Vp, the arrival time at station Station1 is Tp1, and the arrival time at station Station2 is Tp2; the S-wave velocity is Vs, the arrival time at station Station1 is Ts1, and the arrival time at station Station2 is Ts2, then there should be:

[0050] wherein Vp=6km / h, Vs=3.5km / h. In this way, different phases can be associated in time and space, and abnormal points and outlier points can be determined.

[0051] Among them, the abnormal point refers to the S arrival time being abnormal, that is, the difference between the S arrival time and the P arrival time is greater than a certain time length parameter, such as 70s; the outlier point refers to the point deviating from the fitting straight line far.

[0052] Because the abnormal points and outlier points will have a certain impact on the arrival straight line method, the abnormal points are deleted, and the outlier points are retained to the next waveform for rejudgment. As shown in Figure 5a and Figure 5b , Figure 5a is a schematic diagram of time and space association of the phase and determination of the outlier point in the embodiments of the present specification. Under the premise of not containing abnormal points and containing outlier points, the distance of the discrete points in each temporary event to the fitting straight line is 2.0 on average, so the points with a distance greater than or equal to 3 from the fitting straight line are set as outlier points, such as the two points in FIG. 5, with distances of 7.4 and 14.4, which can be determined as outlier points.

[0053] As shown in Figure 5b , Figure 5b is a schematic diagram of removing the outlier points. In the diagram, after removing the outlier points, the remaining points are within a certain range of the straight line, that is, corresponding to the same aftershock event.

[0054] In addition, the space-time correlation of seismic phase can also be based on grid search correlation method. Specifically, according to the one-dimensional velocity acquisition theoretical travel time curve, the seismic phase associated with the theoretical travel time curve is determined as the target seismic phase with space-time correlation through grid search, and the occurrence time and approximate position of the corresponding earthquake are confirmed. Taking Luding earthquake as an example, the search parameters are optimized, and through seismic correlation, 88831 P wave seismic phases and 101209 S wave seismic phases with space-time correlation are obtained from the picked seismic phases. As shown in Figure 6 Figure 6 Schematic diagram for seismic correlation based on grid search correlation method.

[0055] Since the seismic phase of a single station needs to be associated after identification, it involves the determination of whether the seismic phases of different stations belong to the same event. In this project, the travel time curve and the arrival method and seismic phase clustering method are used for correlation, and the interference seismic phases near the travel time range of the same event are cleaned up, and the misidentified seismic phases are removed. Thus, reliable earthquake events are obtained.

[0056] S106, according to the selected target seismic phase, performing earthquake positioning to generate earthquake parameter information, wherein the earthquake parameter information includes position latitude and longitude and depth.

[0057] After picking up the seismic phase, there is a preliminary positioning during the correlation. When performing spatial grid search in REAL, the travel time table is calculated according to the velocity model, and the preliminary earthquake position can be obtained according to the travel time table. At the same time, the seismic phase is clarified and screened, and the redundant seismic phase data that cannot be associated with the same earthquake is removed to obtain the seismic phase information meeting the travel time characteristics, which is input to the subsequent mature positioning program for further positioning. As shown in Figure 7 Figure 7 Schematic diagram for earthquake positioning of the embodiment of the present specification.

[0058] The positioning program selects the methods suitable for network earthquake positioning, such as hyposat and HypoInverse, which are currently used more in monitoring network. At the same time, the seismic phase is converted into json format, which is provided to MSDP for import and subsequent positioning work. The positioning method should be selected according to different regions.

[0059] S108, processing the waveform of the earthquake event to calculate the corresponding magnitude.

[0060] The magnitude calculation is performed according to the national standard. The maximum amplitude of S wave (or Lg wave) recorded by DD-1 short-period seismograph in two horizontal directions should be used to determine the magnitude ML, and the maximum amplitude should be greater than 2 times the interference level. For the measured maximum amplitude, we also need to judge whether the seismic phase is the seismic phase we need, so we need to measure the period value (0.1s-3s) corresponding to the maximum amplitude.​​

[0061] S110, integrating earthquake parameter information and magnitude information, performing relocation, and generating an aftershock sequence directory containing earthquake parameter information and magnitude information.

[0062] After determining the magnitude, location, and target phase, an earthquake event can be generated. Each earthquake event corresponds to an aftershock.

[0063] Relocation can then be performed based on the aforementioned earthquake information. For example, relocation can be performed based on the already determined earthquake information, using both absolute and relative P-wave arrival time data to perform a joint inversion of aftershocks. After earthquake relocation, the root mean square residual of the absolute P-wave travel time can be effectively reduced, significantly improving the accuracy of aftershock location. The relocated earthquake epicenter is more concentrated, and the aftershock sequence is clearer.

[0064] like Figure 8 As shown, Figure 8 A schematic diagram of generating an aftershock sequence provided in an embodiment of this specification.

[0065] For the seismic phases picked up through detection, different programs have different output formats. Currently, all stations use MSDP software for analysis, which has certain format requirements for the imported seismic phases, including XML, JSON, and PHA formats. The event data formats are mainly SEED and SAC formats.

[0066] Through data processing, seismic phase extraction, event correlation, earthquake location, and magnitude calculation, event and phase information is obtained and imported into MSDP for storage. Seismic sequence reports are also generated for subsequent research and analysis. This system targets large-scale data operations, typically processing data ranging from hundreds of gigabytes to several terabytes.

[0067] By acquiring seismic data, S-wave phases in the seismic data are picked up in a mutually corroborated manner using a variety of methods; target phases with temporal and spatial correlation are determined from the picked phases; earthquakes are located according to the target phases, earthquake positions are generated, and the magnitude corresponding to the target phases is calculated; an aftershock sequence including the magnitude, earthquake position, and target phase is generated; a reliability assessment is performed on the aftershock sequence, and unreliable aftershock sequences are eliminated, thereby achieving efficient generation of aftershock sequences in earthquakes and providing a reliable data basis for subsequent seismic activity analysis.

[0068] Furthermore, before generating the aftershock sequence, a reliability assessment is performed on the aftershock sequence to eliminate unreliable aftershock sequences.

[0069] In order to improve the transparency of the operation process, some monitoring programs are specially designed, such as monitoring of the picking process, monitoring of the CPU, GPU and hard disk state in the operation process, monitoring of the positioning and data and results of each station in the earthquake positioning process.

[0070] Specifically, the confidence evaluation of the picked seismic phase, the positioning reliability evaluation of the earthquake position, the G-R relationship evaluation of the frequency and magnitude, the comparative evaluation of the magnitude, and the reliability evaluation of the aftershock sequence by comprehensively evaluating the confidence evaluation, the positioning reliability evaluation, the G-R relationship evaluation and the comparative evaluation are included.

[0071] For the structure of sequence picking, the result is tested by G-R relationship, which can check the reliability of the sequence on the one hand, and can test the advantage of the sequence automatically constructed by the machine in the minimum completeness magnitude on the other hand.

[0072] For example, the aftershock sequence of the 2022.9.5 Luding 6.8 earthquake is analyzed, and the obtained aftershock precise positioning spatial distribution is compared with the velocity and wave velocity ratio on the profile. The earthquake depth distribution is well matched with the step change of the velocity interface. The aftershock distribution of Luding earthquake is basically in the shallow boundary area of the strip, which is inferred to be related to the damage and destruction of the internal rock of the fault zone

[0073] , and it may also be related to the lithology or fluid state of the shallow layer. It can also be seen that the distribution of the fracture along the southwest direction is similar to the distribution of the low wave velocity ratio zone, which is inferred to be caused by the fissured fracture produced in the process of Gongga Mountain uplift. By analyzing the seismic activity in different stages, the possible occurrence area distribution of aftershocks can be obtained.

[0074] In addition, from November 2016 to 2020, the China Earthquake Scientific Array Detection 3 Project data were detected by the intelligent picking and screening. The Y3 array in the eastern part of North China retained 6697 earthquake events, picked up 36130 P wave arrival times and 40920 S wave arrival times; the X3 array in the western part of North China retained 5886 earthquake events, 32711 P wave arrival times and 38683 S wave arrival times, and the obtained seismic phase was used for subsequent underground structure imaging.

[0075] In the second aspect, the embodiments of the present specification also provide a kind of. As shown in Figure 9 , a structural schematic diagram of an electronic device provided by the embodiments of the present specification, the device comprises: Figure 9 at least one processor; and,

[0076]

[0077] memory in communication connection with the at least one processor; wherein,

[0078] ​The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0079] In a third aspect, based on the same idea, the embodiments of the present specification also provide a non-volatile computer storage medium corresponding to the above method, which stores computer executable instructions. When the computer reads the computer executable instructions in the storage medium, the instructions enable one or more processors to perform the method of the first aspect.

[0080] In the 1990s, it was quite obvious to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structures of diodes, transistors, switches, etc.) or in software (improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by hardware entity modules. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it, rather than by asking a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating integrated circuit chips, this programming is now mostly implemented by "logic compiler" software, which is similar to software compilers used in program development, and the original code to be compiled is written in a specific programming language, which is called a hardware description language (HDL), and there are many such languages, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in the above-mentioned hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0081] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0082] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0083] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0084] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The specification is presented with reference to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing element or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 Figure 1

[0086] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams block or blocks. Figure 1 Figure 1

[0087] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams block or blocks. Figure 1 Figure 1

[0088] In one typical configuration, the computing device includes one or more processors (CPU's), input / output interfaces, network interfaces, and memory.

[0089] The memory can include non-persistent memory and / or persistent memory, both of which can be volatile and / or non-volatile. Non-persistent memory can include, for example, a random access memory (RAM), which can be a volatile memory device, and does not retain stored contents when turned off. Non-persistent memory can also include a cache or cache-like device, which can be volatile and / or non-volatile. Persistent memory can include, for example, a read-only memory (ROM), a flash memory or other non-volatile memory device. The memory is an example of computer-readable media.

[0090] ​​​​​​Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0091] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0092] The specification can be described in the general context of computer-executable instructions, such as program modules, executed by computers. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0093] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for device, equipment, non-volatile computer storage medium embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0094] The above-described embodiments of the application have special structure and can achieve the desired results. Other embodiments can have different structures and achieve the same results. The purpose of the above-described embodiments is to illustrate the principles of the application and not to limit the scope of the application. The scope of the application is defined by the claims and their equivalents. Other embodiments are within the scope of the claims.

[0095] The above description is merely illustrative of the embodiments of the present application and is not intended to limit the scope of the present application. Various modifications can be made by those skilled in the art based upon the teachings disclosed herein. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present application shall fall within the scope of the claims of the present application.

Claims

1. A method for generating an aftershock sequence in an earthquake, comprising: Obtain event waveforms from seismic observation data and pick up P-wave and S-wave phases; From the picked seismic phases, through technical screening and seismic correlation, target seismic phases with the same event temporal and spatial correlation are determined; Perform earthquake positioning according to the selected target seismic phase and generate earthquake parameter information, wherein the earthquake parameter information includes location latitude, longitude and depth; Process the waveform of the earthquake event and calculate the corresponding magnitude; The earthquake parameter information and magnitude information are integrated to perform relocation and generate an aftershock sequence catalog containing earthquake parameter information and magnitude information.

2. The method according to claim 1, wherein The method further comprises: The aftershock sequence directory is converted into a format supported by MSDP software, wherein the format includes .json, XML or PHA format.

3. The method according to claim 1, wherein Obtain event waveforms from seismic observation data, including: Polarity, linearity, and artificial intelligence are used to mutually verify the event waveform, wherein the mutual verification includes the following methods: Polarity analysis indicators are used to identify seismic phase types and distinguish between P-wave and S-wave phases. The STA / LTA method is used to perform threshold detection to obtain three-dimensional features, and the seismic phase type is judged according to the three-dimensional features, wherein the three-dimensional features include STA / LTA features and three-dimensional energy features; The confidence level of the data points obtained by the pre-trained neural network model is used to pick up the seismic phase. When the confidence level of the network point exceeds a specific threshold, the corresponding seismic phase time and phase type are output. The consistency of the seismic phase types obtained by the above three methods is compared, and when the consistency meets the preset requirements, the corresponding event waveform is determined.

4. The method according to claim 1, wherein From the picked seismic phases, through technical screening and seismic correlation, target seismic phases with the same event temporal and spatial correlation are identified, including: A clustering method based on travel time is used to determine the abnormal points and outliers in the seismic phase type, a fitting line excluding the abnormal points is generated, and the outliers are retained until the next waveform is judged again. The clustering method based on travel time is used to determine the abnormal points and outliers in the seismic phase type, specifically using the following method: Alternatively, a theoretical travel time curve is obtained based on the one-dimensional velocity, and the seismic phase associated with the theoretical travel time curve is determined as a target seismic phase with a time-space correlation through grid search, and the time and approximate location of the corresponding earthquake are confirmed.

5. The method according to claim 1, wherein Based on the selected target phases, earthquake positioning is performed, including: Calculate the travel time curve based on the velocity model and obtain the earthquake location based on the travel time curve; Filter and remove target phases that cannot be associated with the same earthquake based on the earthquake location, and obtain target phases that meet the travel time characteristics; The positioning program is used to re-position the target seismic phase that meets the travel time characteristics to generate an accurate positioning.

6. The method according to claim 5, wherein: The method further comprises: Using the identification data of medium and long-distance stations, the range of near-station seismic phases is estimated based on the travel time curves and velocity model, and then the overlapping events are removed by combining the phase types and the paired or spaced paired characteristics of the phases.

7. The method of claim 3, wherein: Polarity analysis indicators are used to identify seismic phase types and distinguish between P-wave and S-wave phases, including: Perform Hilbert transform on the seismic signal, analyze the transformed seismic signal, and calculate its covariance matrix. This will yield ellipticity, linearity, and polarization strike angle. The S-wave phase is obtained by screening from the phase types according to the ellipticity, linearity and polarization strike angle.

8. The method of claim 1, wherein: Process the waveform of the earthquake event and calculate the corresponding magnitude, including: Determining the maximum amplitude of the S wave corresponding to the target phase; measuring a period value corresponding to the maximum amplitude; The corresponding magnitude is calculated according to the maximum amplitude and period value.

9. The method of claim 1, wherein: Before generating the aftershock sequence catalog containing earthquake parameter information and magnitude information, the method further includes: Conduct reliability assessment on the aftershock sequence and eliminate unreliable aftershock sequences, specifically including: A confidence assessment is used for the picked seismic phases, a positioning reliability assessment is used for the earthquake positions, a GR relationship assessment is used for the frequency and magnitude, and a comparative assessment is performed on the magnitude. The reliability assessment of the aftershock sequence is performed by combining the confidence assessment, positioning reliability assessment, GR relationship assessment and comparative assessment.

10. An electronic device comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

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