A seismic wave positioning method, system, computer equipment and storage medium

Abstract

The present application relates to a method, system, computer equipment and storage medium for seismic wave location. The method comprises: collecting the source signal of the source area through the seismic signal observation station, and transmitting the source signal to the cloud platform; obtaining the source signal of each seismic signal observation station from the cloud platform through the control center, using the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and using the seismic correlation and cross-correlation algorithm to obtain the arrival time difference between each seismic signal observation station; establishing the TDOA solution equation based on the Chan and Taylor algorithm according to the arrival time difference between each seismic signal observation station, estimating the initial value of the source position of the source area, and substituting the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position location result of the source area. The embodiment of the present application is simple to operate and has high calculation efficiency, which improves the solution accuracy of the source position.

Description

Technical Field

[0001] The present application belongs to the technical field of earthquake positioning, and in particular, relates to a method, system, computer equipment and storage medium for positioning earthquake waves. Background Art

[0002] Earthquake location is a basic problem in seismological research. Accurate earthquake location plays a vital role in studying the internal structure and fault structure of the earth, resource and energy exploration and development, and earthquake early warning research. In addition to the location of the source and the study of the source mechanism of natural earthquakes, there is also a wide range of application needs for microseismic source location in unconventional oil and gas exploration, hydraulic fracturing, and mine monitoring.

[0003] For earthquake source location, improving the location accuracy often requires two factors: high-precision arrival time picking and accurate velocity structure. Conventional earthquake location methods are mostly based on travel time earthquake location methods for phase picking and calculation. The P wave (longitudinal wave) and S wave (transverse wave) measured by this method are seriously affected by noise, and the time domain waveform does not have an obvious signal-to-noise ratio, resulting in large errors in the determination of the earthquake source position. Traditional methods for automatically picking up earthquake phases usually use algorithms such as Short Term Average / Long Term Average (STA / LTA) or Akaike Information Criterion (AIC) to pick up the arrival time by studying the changes in the phase amplitude ratio and frequency change characteristics of the waveform signal when an earthquake occurs. However, when the waveform signal-to-noise ratio is not good, it often causes a high false detection rate, which has a great impact on the location results. With the development of machine learning technology, phase picking using machine learning has been widely used. However, in the existing technology, the training model of machine learning often uses natural earthquakes with low sampling rates as samples, which weakens the characteristic information of the actual seismic waveform under high sampling rates to a certain extent. In addition, the generalization ability of the training model needs to further adapt to the application characteristics of active source seismic waves.

[0004] In terms of velocity structure, in order to obtain an accurate velocity model, it is often necessary to use the arrival time information of the direct wave and the travel time tomography method to obtain a detailed three-dimensional velocity structure. The calculation is complex and requires a large amount of known information. Summary of the invention

[0005] The present application provides a seismic wave positioning method, system, computer device and storage medium, aiming to solve at least one of the above-mentioned technical problems in the prior art to a certain extent.

[0006] In order to solve the above problems, this application provides the following technical solutions:

[0007] A seismic wave positioning method, comprising:

[0008] The seismic signal of the source area is collected through the seismic signal observation station, and the seismic signal is transmitted to the cloud platform; wherein the seismic signal observation station is arranged in a grid-like regular pattern according to the topographic conditions of the source area;

[0009] The control center obtains the source signal of each seismic signal observation station from the cloud platform, uses the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and uses the seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station;

[0010] According to the arrival time difference between the seismic signal observation stations, a TDOA solution equation based on the Chan and Taylor algorithms is established, and the initial value of the source position of the source area is obtained by using the two-step maximum likelihood estimation of the Chan algorithm. The initial value of the source position is substituted into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area.

[0011] The technical solution adopted in the embodiment of the present application also includes: collecting the source signal of the source area through the seismic signal observation station, and transmitting the source signal to the cloud platform, specifically:

[0012] The seismic signal observation station includes a seismic signal acquisition module, a GNSS module, a first main control unit, a storage module and a wireless transmission module;

[0013] The seismic signal acquisition module is used to acquire seismic source signals from the seismic source area through a geophone, and send the acquired seismic source signals to the first main control unit;

[0014] The first main control unit is used to locate the seismic signal observation station through the NTRIPClient port of the GNSS module after the seismic signal observation station enters the source signal acquisition, store the source signal sent by the seismic signal acquisition module in the storage module, and send the source signal to the wireless transmission module according to the fixed window size;

[0015] The wireless transmission module is used to package the source signal according to the self-identification of each seismic signal observation station and send it to the cloud platform;

[0016] The GNSS module is connected to the seismic signal acquisition module, and is used to obtain the clock information of the GPS signal before the seismic signal acquisition module collects the source signal, synchronize the internal clock of each seismic signal observation station, and locate each seismic signal observation station after it starts collecting the source signal.

[0017] The technical solution adopted by the embodiment of the present application also includes: before the long-short time window mean ratio algorithm is used to pick up the P wave arrival time of the source signal, it also includes:

[0018] The source signal is preprocessed; the preprocessing includes removing the mean, linear trend and bandpass filtering of specific signal characteristics.

[0019] The technical solution adopted by the embodiment of the present application also includes: the long-short time window mean ratio algorithm is used to pick up the P wave arrival time of the source signal, specifically:

[0020] The following formula is used to pick up the P wave arrival time of the preprocessed source signal:

[0021]

[0022] Among them, S d Indicates that the source signal is at t i The function of STA / LTA at the time, STA is used to reflect the average value of the source signal, and LTA is used to reflect the average value of the background noise. LTA , Δt STA Represent the window length and characteristic function value of the long time window and short time window respectively.

[0023] The technical solution adopted by the embodiment of the present application also includes: the use of seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station is specifically:

[0024] The arrival time information of the seismic correlation of each seismic signal observation station is sorted, and the seismic signal observation station with the earliest record of P wave arrival is used as the reference station. The data of a set length is selected as the window with the reference station as the center, and the arrival time difference between the reference station and other seismic signal observation stations is calculated by cross-correlation:

[0025]

[0026] Among them, t j and T j are the observed travel time and theoretical travel time of phase j, t i and T i are the observed travel time and theoretical travel time of seismic phase i, (x, y, z) is the source location, and n is the number of seismic signal observation stations.

[0027] The technical solution adopted by the embodiment of the present application also includes: establishing a TDOA solution equation based on the Chan and Taylor algorithms according to the arrival time differences between the seismic signal observation stations, using the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, and substituting the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area, specifically:

[0028] The TDOA equations for the reference station and other seismic signal observation stations are:

[0029]

[0030]

[0031] Where (x s ,y s ,z s ) is the focal coordinate, R1 is the distance from the reference station to the focal point, y i1 is the arrival time difference from other seismic signal observation stations to the reference station, v is the average velocity of P wave, i=2;

[0032] After linearizing the above equation, we get:

[0033]

[0034] in,

[0035] After the initial value of the earthquake source position is obtained using the Chan algorithm, the established target positioning model is converted into:

[0036] ψ=hG Taylor δ

[0037] where δ = [dxdydz] T represents the position estimation error; ψ is the measurement error; h represents the difference between the true value and the measured value, R i1 is the distance difference between the reference site and other seismic signal observation stations, r i is the estimated initial position of the target z p =[x p ,y p ,z p ] and the distance between each seismic signal observation station;

[0038]

[0039]

[0040]

[0041] Among them, Q is the covariance matrix of the measured value, and each iteration updates the initial estimated coordinates (x p +dx,y p +dy,z p +dz), when |δ|<positioning accuracy ε, the updated coordinates are the positioning results of the earthquake source position.

[0042] The technical solution adopted by the embodiment of the present application also includes: establishing a TDOA solution equation based on the Chan and Taylor algorithms according to the arrival time differences between the seismic signal observation stations, using the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, and substituting the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area, and further includes:

[0043] The average velocity of the earthquake source area is calculated based on the distance information and arrival time between the earthquake signal observation stations, and the average velocity is re-entered into the TDOA solution equation to calculate the earthquake source position to obtain the corrected earthquake source position positioning result.

[0044] Another technical solution adopted by the embodiment of the present application is: a seismic wave positioning system, comprising:

[0045] Seismic signal observation station: used to collect seismic source signals in the source area and transmit the seismic source signals to the cloud platform; wherein the seismic signal observation station is arranged in a grid-like regular pattern according to the topographic conditions of the source area;

[0046] Control center: used to obtain the source signal of each seismic signal observation station from the cloud platform, use the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and use the seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station, and establish the TDOA solution equation based on the Chan and Taylor algorithm according to the arrival time difference between each seismic signal observation station, use the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, substitute the initial value of the source position into the Taylor algorithm for iterative calculation, and obtain the source position positioning result of the source area.

[0047] Another technical solution adopted by the embodiment of the present application is: a computer device, the computer device includes a processor and a memory coupled to the processor, wherein:

[0048] The memory stores program instructions for implementing the seismic wave positioning method;

[0049] The processor is used to execute the program instructions stored in the memory to control the seismic wave positioning method.

[0050] Another technical solution adopted by the embodiment of the present application is: a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute the seismic wave positioning method.

[0051] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: the seismic wave positioning method, system, computer equipment and storage medium of the embodiments of the present application use wireless network transmission to realize real-time data transmission of multiple seismic signal observation stations, and use seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station for the two major influencing factors of arrival time picking and velocity structure in high-precision positioning, and use the TDOA algorithm based on the combination of Chan and Taylor to realize the optimal solution of the source position, and use the average velocity calibration method of the source area to correct the velocity structure of the source area, further improving the solution accuracy of the source position. The embodiments of the present application are simple to operate and have high computational efficiency, meeting the application requirements of seismic wave positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the seismic wave positioning method according to an embodiment of the present application;

[0053] Figure 2 This is a schematic diagram of the structure of a seismic signal observation station provided in an embodiment of the present application;

[0054] Figure 3 It is a schematic diagram of the control center structure provided in an embodiment of the present application;

[0055] Figure 4 This is a schematic diagram of timely picking after earthquake correlation according to an embodiment of the present application;

[0056] Figure 5 A schematic diagram of the structure of a seismic wave positioning system according to an embodiment of the present application;

[0057] Figure 6 A schematic diagram of the computer device structure of an embodiment of the present application;

[0058] Figure 7 A schematic diagram of the structure of a storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] The terms "first", "second", "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", "third" can expressly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In the embodiments of this application, all directional indications (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or computer device that includes a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or computer devices.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] See also Figure 1 , is a flow chart of the seismic wave positioning method of the embodiment of the present application. The seismic wave positioning method of the embodiment of the present application comprises the following steps:

[0063] S100: deploying a set number of seismic signal observation stations according to the topographic conditions of the epicenter area, performing seismic observation on the epicenter area through the seismic signal observation stations, obtaining the epicenter signal of the epicenter area, and transmitting the epicenter signal to the cloud platform;

[0064] In this step, in order to achieve high-precision earthquake source location results, the embodiment of the present application arranges at least 4 earthquake signal observation stations in a regular grid pattern according to the topographic conditions of the earthquake source area. The specific number of earthquake signal observation stations can be set according to the actual application scenario. Figure 2 As shown, it is a schematic diagram of the structure of the seismic signal observation station provided by the embodiment of the present application. Each seismic signal observation station includes a seismic signal acquisition module, a GNSS (Global Navigation Satellite System) module, a first main control unit, a storage module, a wireless transmission module and a power module; wherein the seismic signal acquisition module is used to acquire the source signal through the detector, and send the acquired source signal to the first main control unit; the first main control unit is used to perform RTK (Real-time Kinematic, real-time dynamic differential positioning technology) or PPP (precise point positioning, precise single point positioning technology) positioning on the seismic signal observation station through the NTRIPClient port of the GNSS module after the seismic signal observation station enters the source signal acquisition, obtains the high-precision position information of each seismic signal observation station, and stores the source signal sent by the seismic signal acquisition module in the storage module, and sends the source signal to the wireless transmission module according to the fixed window size; the wireless transmission module is used to package the source signal sent by the first main control unit according to the self-identification of each seismic signal observation station and send it to the cloud platform for data management, so as to realize the real-time transmission of the source signal. The GNSS module is connected to the seismic signal acquisition module to obtain the clock information of the GPS signal before the seismic signal acquisition module acquires the source signal, synchronizes the internal clock of each seismic signal observation station, and after each seismic signal observation station starts to acquire the source signal, the first main control unit performs RTK high-precision positioning of each seismic signal observation station through the NTRIPClient port of the GNSS module; the power supply module is connected to each other module respectively to supply power to each other module. Among them, the signal transmission mode of the wireless transmission module includes but is not limited to 4G or 5G.

[0065] S110: The control center obtains the source signals of each seismic signal observation station from the cloud platform, and performs preprocessing such as removing the mean, linear trend and bandpass filtering of specific signal characteristics on the source signals;

[0066] In this step, if Figure 3As shown, it is a schematic diagram of the control center structure provided by the embodiment of the present application. The control center includes a cloud platform data management and transmission module, a second main control unit, a data processing module and a display and control module. Among them, the cloud platform data management and transmission module is connected to the second main control unit, and the data of all deployed seismic signal observation stations are downloaded according to the corresponding time and identification by logging into the cloud platform. The second main control unit is responsible for data management and system scheduling. After classifying the data downloaded by the cloud platform data management and transmission module, the source signal of each seismic signal observation station at the same time is sent to the data processing module. The data processing module is used to perform pre-processing such as removing the mean, linear trend and band-pass filtering of specific signal characteristics on the source signal, and then solve the source position to improve the signal-to-noise ratio and eliminate the ring The problem of false detection of arrival time caused by environmental noise; the display and control module is used to display the solved source position and observation system in spatial position, and send a calibration instruction of the velocity structure to the second main control unit when the error of the source position solution is greater than the set threshold. The second main control unit sends the calibration instruction to the cloud platform through the cloud platform data management and transmission module to set the calibration instruction. After receiving the calibration instruction from the cloud platform, each seismic signal observation station calculates the average velocity of the source area according to the source position positioning result and the arrival time. The control center further solves and corrects the solution equation according to the average velocity calculation result. In the embodiment of the present application, in order to reduce the source positioning error, the positioning accuracy of each seismic signal observation station is set to centimeter level.

[0067] S120: using the Short Term Average / Long Term Average (STA / LTA) algorithm to pick up the P-wave arrival time of the preprocessed source signal, and using the seismic correlation and cross-correlation algorithms to obtain the arrival time differences between the seismic signal observation stations;

[0068] In this step, in the STA / LTA algorithm, STA is used to reflect the average value of the source signal, and LTA reflects the average value of the background noise. When the source signal arrives, the corresponding STA / LTA value will increase significantly. When the ratio is greater than a certain threshold, it can be determined that the source event has occurred, achieving the purpose of timely picking.

[0069] Specifically, the embodiment of the present application uses the following formula to pick up the P wave arrival time of the preprocessed source signal:

[0070]

[0071] Among them, S d Indicates that the source signal is at t i Function of time with respect to STA / LTA, d, Δt LTA , Δt STARepresents the window length and characteristic function value of the long time window and short time window.

[0072] After picking up the P-wave arrival time of the earthquake source signal, use the seismic correlation to find the accurate arrival time of the same earthquake source received by each earthquake signal observation station, and sort the arrival time information of each earthquake signal observation station through seismic correlation. The earthquake signal observation station with the earliest record of the P-wave arrival (that is, the earliest reception of the earthquake source information) is used as the reference station, and the arrival time difference between the reference station and other earthquake signal observation stations is calculated through cross-correlation. Specifically, a three-dimensional grid search algorithm is used to associate the P and S arrival times of multiple stations arranged in chronological order with a specific event, and the location is determined by counting the arrival time of the seismic phase and calculating the travel time residual with the theoretical arrival time. The search range is centered on the earthquake signal observation station with the earliest record of the P-wave arrival. According to the set grid size, only when the number of associated P and S arrival times reaches the set threshold can it be considered as the arrival time information stimulated by a earthquake source event. Specifically, Figure 4 As shown, it is a schematic diagram of the arrival time picking after the seismic correlation of the embodiment of the present application. After excluding the other false detection arrival times, the seismic signal observation station with the earliest recorded P wave arrival time is selected as the reference station, and the data of a set length (30s) is selected as the window with the reference station as the center. The arrival time difference between the remaining stations and the reference station is calculated by cross-correlation to further improve the relative arrival time picking accuracy:

[0073]

[0074] Among them, t j and T j are the observed travel time and theoretical travel time of phase j, t i and T i are the observed travel time and theoretical travel time of seismic phase i, (x, y, z) is the source location, and n is the number of seismic signal observation stations.

[0075] S130: establishing a TDOA solution equation based on the Chan and Taylor algorithms according to the arrival time differences between the seismic signal observation stations, using the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, and substituting the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the location result of the source position;

[0076] In this step, the seismic signal observation station that records the earliest arrival of the P wave is taken as the reference station, and the TDOA equation between the reference station and other seismic signal observation stations is established as follows:

[0077]

[0078]

[0079] The coordinates of the earthquake source are (xs ,y s ,z s ), R1 is the distance from the reference station to the earthquake source, t i1 is the arrival time difference from other seismic signal observation stations to the reference station, v is the average wave velocity of P waves, which is 5.91 km / s in the embodiment of the present application, and i=2.

[0080] After linearizing the above equation, we get:

[0081]

[0082] in, Taking into account the influence of the error of the earthquake source arrival time picking, the initial value of the non-recursive solution equation based on the Chan algorithm is established. In the embodiment of the present application, the Taylor nonlinear solution algorithm is used to iteratively solve the preliminary optimal solution.

[0083] Specifically, after the initial value of the earthquake source position is obtained using the CHAN algorithm, the established target positioning model is converted into:

[0084] ψ=hG Taylor δ (6)

[0085] where δ = [dxdydz] T represents the position estimation error; ψ is the measurement error; h represents the difference between the true value and the measured value, R i1 is the distance difference between the reference site and other seismic signal observation stations, r i is the estimated initial position z of the target p =[x p ,y p ,z p ] and the distance between each seismic signal observation station.

[0086]

[0087]

[0088] The weighted least squares solution of formula (6) is:

[0089]

[0090] Q is the covariance matrix of the measured values, and each iteration updates the initial estimated coordinates (x p +dx,y p +dy,z p +dz), when |δ|<positioning accuracy ε, the updated coordinates are the positioning results of the earthquake source position.

[0091] S140: Calculate the average velocity of the earthquake source area according to the distance information and arrival time between each earthquake signal observation station, and bring the average velocity back into the TDOA solution equation to calculate the earthquake source position, and obtain a corrected earthquake source position positioning result;

[0092] In this step, in order to further improve the location accuracy of the earthquake source, the velocity structure of the earthquake source area is calibrated, and the distance information D between the earthquake signal observation stations is used. cal and when t cal ,according to The average velocity of the earthquake source area is calculated, and then the average velocity is substituted into the TDOA solution equation constructed in the previous step to further solve the earthquake source position.

[0093] Based on the above, the seismic wave positioning method of the embodiment of the present application uses wireless network transmission to realize real-time data transmission of multiple seismic signal observation stations, and uses seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station for the two major influencing factors of arrival time picking and velocity structure in high-precision positioning, and uses the TDOA algorithm based on the combination of Chan and Taylor to realize the optimal solution of the source position, and uses the average velocity calibration method of the source area to correct the velocity structure of the source area, further improving the solution accuracy of the source position. The embodiment of the present application is simple to operate and has high computational efficiency, meeting the application requirements of seismic wave positioning.

[0094] See also Figure 5 , is a schematic diagram of the structure of the seismic wave positioning system of the embodiment of the present application. The seismic wave positioning system 40 of the embodiment of the present application includes:

[0095] Seismic signal observation station 41: used to collect seismic signal in the source area and transmit the seismic signal to the cloud platform; wherein, in order to achieve high-precision source positioning results, the embodiment of the present application arranges at least 4 seismic signal observation stations in a grid-like regular manner according to the terrain conditions of the source area, and the specific number of seismic signal observation stations can be set according to the actual application scenario. Specifically, each seismic signal observation station includes a seismic signal acquisition module, a GNSS (Global Navigation Satellite System) module, a first main control unit, a storage module, a wireless transmission module and a power supply module; wherein the seismic signal acquisition module is used to acquire source signals through a detector and send the acquired source signals to the first main control unit; the first main control unit is used to perform RTK (Real-time Kinematic) or PPP (precise point positioning) positioning on the seismic signal observation station through the NTRIPClient port of the GNSS module after the seismic signal observation station enters the source signal acquisition, obtain high-precision position information of each seismic signal observation station, and store the source signal sent by the seismic signal acquisition module in the storage module, and at the same time send the source signal to the wireless transmission module according to the fixed window size; the wireless transmission module is used to package the source signal sent by the first main control unit according to the self-identification of each seismic signal observation station and send it to the cloud platform for data management, so as to realize real-time transmission of the source signal. The GNSS module is connected to the seismic signal acquisition module to obtain the clock information of the GPS signal before the seismic signal acquisition module acquires the source signal, synchronizes the internal clock of each seismic signal observation station, and after each seismic signal observation station starts to acquire the source signal, the first main control unit performs RTK high-precision positioning of each seismic signal observation station through the NTRIPClient port of the GNSS module; the power supply module is connected to each other module respectively to supply power to each other module. Among them, the signal transmission mode of the wireless transmission module includes but is not limited to 4G or 5G.

[0096] Control center 42: used to obtain the source signal of each seismic signal observation station from the cloud platform, use the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and use the seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station, and establish the TDOA solution equation based on the Chan and Taylor algorithms according to the arrival time difference between each seismic signal observation station, use the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, substitute the initial value of the source position into the Taylor algorithm for iterative calculation, and obtain the source position positioning result of the source area. Specifically, the control center includes a cloud platform data management and transmission module, a second main control unit, a data processing module, and a display and control module. Among them, the cloud platform data management and transmission module is connected to the second main control unit, and the data of all the deployed seismic signal observation stations are downloaded according to the corresponding time and identification by logging into the cloud platform. The second main control unit is responsible for data management and system scheduling. After classifying the data downloaded by the cloud platform data management and transmission module, the source signal of each seismic signal observation station at the same time is sent to the data processing module. The data processing module is used to perform pre-processing such as removing the mean, linear trend and band-pass filtering of specific signal characteristics on the source signal, and then solve the source position to improve the signal-to-noise ratio and eliminate the ring The display and control module is used to display the solved source position and the spatial position of the observation system, and send a calibration instruction of the velocity structure to the second main control unit when the error of the source position solution is greater than the set threshold. The second main control unit sends the calibration instruction to the cloud platform through the cloud platform data management and transmission module to set the calibration instruction. After receiving the calibration instruction from the cloud platform, each seismic signal observation station calculates the average velocity of the source area according to the source position positioning result and the arrival time. The control center further solves and corrects the solution equation according to the average velocity calculation result.

[0097] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0098] The system provided in the embodiment of the present application can be applied in the aforementioned method embodiment. For details, please refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0099] See also Figure 6 , is a schematic diagram of the computer device structure of an embodiment of the present application. The computer device 50 includes:

[0100] A memory 51 storing executable program instructions;

[0101] A processor 52 connected to the memory 51;

[0102] The processor 52 is used to call the executable program instructions stored in the memory 51 and perform the following steps: collect the source signal of the source area through the seismic signal observation station, and transmit the source signal to the cloud platform; obtain the source signal of each seismic signal observation station from the cloud platform through the control center, use the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and use the seismic correlation and cross-correlation algorithm to obtain the arrival time difference between each seismic signal observation station; establish the TDOA solution equation based on the Chan and Taylor algorithm according to the arrival time difference between the seismic signal observation stations, use the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, and substitute the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area.

[0103] The processor 52 may also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0104] See also Figure 7 , Figure 7It is a structural diagram of the storage medium of the embodiment of the present application. The storage medium of the embodiment of the present application stores a program instruction 61 that can implement the following steps: collect the source signal of the source area through the seismic signal observation station, and transmit the source signal to the cloud platform; obtain the source signal of each seismic signal observation station from the cloud platform through the control center, use the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and use the seismic correlation and cross-correlation algorithm to obtain the arrival time difference between each seismic signal observation station; establish the TDOA solution equation based on the Chan and Taylor algorithm according to the arrival time difference between each seismic signal observation station, use the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, and substitute the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area. Among them, the program instruction 61 can be stored in the above storage medium in the form of a software product, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network computer device, etc.) or a processor (processor) to perform all or part of the steps of each implementation method of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program instructions, or terminal computer devices such as computers, servers, mobile phones, tablets, etc. Among them, the server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0105] In the several embodiments provided in the present application, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0106] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the description and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A seismic wave positioning method, characterized in that: include: The seismic signal of the source area is collected through the seismic signal observation station, and the seismic signal is transmitted to the cloud platform; wherein the seismic signal observation station is arranged in a grid-like regular pattern according to the topographic conditions of the source area; The control center obtains the source signal of each seismic signal observation station from the cloud platform, uses the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and uses the seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station; According to the arrival time difference between the seismic signal observation stations, a TDOA solution equation based on the Chan and Taylor algorithms is established, and the initial value of the source position of the source area is obtained by using the two-step maximum likelihood estimation of the Chan algorithm, and the initial value of the source position is substituted into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area; The TDOA solution equation based on the Chan and Taylor algorithm is established according to the arrival time difference between the seismic signal observation stations, the initial value of the source position of the source area is obtained by using the two-step maximum likelihood estimation of the Chan algorithm, and the initial value of the source position is substituted into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area, which is specifically: The TDOA equation between the reference station and other seismic signal observation stations is: Where (x s ,y s , z s ) is the focal coordinate, R1 is the distance from the reference station to the focal point, t i1 is the arrival time difference from other seismic signal observation stations to the reference station, v is the average velocity of P wave, i=2; After linearizing the above equation, we get: Among them, x i，1 = x i - x1, y i，1 = y i - y1, z i，1 = z i - z1; After the initial value of the earthquake source position is obtained using the Chan algorithm, the established target positioning model is converted into: ψ=hG Taylor d where δ = [dxdydz] T represents the position estimation error; ψ is the measurement error; h represents the difference between the true value and the measured value, R i1 is the distance difference between the reference site and other seismic signal observation stations, r i is the estimated initial position of the target z p =[x p ,y p , z p ] and the distance between each seismic signal observation station; Among them, Q is the covariance matrix of the measured value, and each iteration updates the initial estimated coordinates (x p +dx,y p +dy,z p +dz), when |δ|<positioning accuracy ε, the updated coordinates are the positioning results of the earthquake source position.

2. The seismic wave positioning method according to claim 1, characterized in that: The seismic signal in the source area is collected by the seismic signal observation station, and the seismic signal is transmitted to the cloud platform, specifically: The seismic signal observation station includes a seismic signal acquisition module, a GNSS module, a first main control unit, a storage module and a wireless transmission module; The seismic signal acquisition module is used to acquire seismic source signals from the seismic source area through a geophone, and send the acquired seismic source signals to the first main control unit; The first main control unit is used to locate the seismic signal observation station through the NTRIPClient port of the GNSS module after the seismic signal observation station enters the source signal acquisition, store the source signal sent by the seismic signal acquisition module in the storage module, and send the source signal to the wireless transmission module according to the fixed window size: The wireless transmission module is used to package the source signal according to the self-identification of each seismic signal observation station and send it to the cloud platform; The GNSS module is connected to the seismic signal acquisition module, and is used to obtain the clock information of the GPS signal before the seismic signal acquisition module collects the source signal, synchronize the internal clock of each seismic signal observation station, and locate each seismic signal observation station after it starts collecting the source signal.

3. The seismic wave locating method according to claim 2, characterized in that: Before the source signal is subjected to the P wave arrival time picking by using the long-short time window mean ratio algorithm, the method further comprises: The source signal is preprocessed; the preprocessing includes removing the mean, linear trend and bandpass filtering of specific signal characteristics.

4. The seismic wave locating method according to claim 3, characterized in that: The P wave arrival time is picked up by using the long-short time window mean ratio algorithm for the source signal, specifically: The following formula is used to pick up the P wave arrival time of the preprocessed source signal: Among them, S d Indicates that the source signal is at t i The function of STA / LTA at the time, STA is used to reflect the average value of the source signal, and LTA is used to reflect the average value of the background noise. LTA , Δt STA Represent the window length and characteristic function value of the long time window and short time window respectively.

5. The seismic wave locating method according to claim 4, characterized in that: The method of using seismic correlation and cross-correlation algorithms to obtain the arrival time differences between the seismic signal observation stations is specifically as follows: The arrival time information of the seismic correlation of each seismic signal observation station is sorted, and the seismic signal observation station with the earliest record of P wave arrival is used as the reference station. The reference station is used as the center to select the data of a set length as the window, and the arrival time difference between the reference station and other seismic signal observation stations is calculated by cross-correlation: Among them, t j and T j are the observed travel time and theoretical travel time of phase j, t i and T i are the observed travel time and theoretical travel time of seismic phase i, (x, y, z) is the source location, and n is the number of seismic signal observation stations.

6. The seismic wave positioning method according to any one of claims 1 to 5, characterized in that: The method further includes establishing a TDOA solution equation based on the Chan and Taylor algorithms according to the arrival time differences between the seismic signal observation stations, obtaining an initial value of the source position of the source area by using the two-step maximum likelihood estimation of the Chan algorithm, and substituting the initial value of the source position into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area, and further includes: The average velocity of the earthquake source area is calculated based on the distance information and arrival time between the earthquake signal observation stations, and the average velocity is re-entered into the TDOA solution equation to calculate the earthquake source position to obtain the corrected earthquake source position positioning result.

7. A seismic wave positioning system, characterized in that: include: Seismic signal observation station: used to collect seismic source signals in the source area and transmit the seismic source signals to the cloud platform; wherein the seismic signal observation station is arranged in a grid-like regular pattern according to the topographic conditions of the source area; Control center: used to obtain the source signal of each seismic signal observation station from the cloud platform, use the long-short time window mean ratio algorithm to pick up the P wave arrival time of the source signal, and use the seismic correlation and cross-correlation algorithms to obtain the arrival time difference between each seismic signal observation station, and establish the TDOA solution equation based on the Chan and Taylor algorithm according to the arrival time difference between each seismic signal observation station, use the two-step maximum likelihood estimation of the Chan algorithm to obtain the initial value of the source position of the source area, substitute the initial value of the source position into the Taylor algorithm for iterative calculation, and obtain the source position positioning result of the source area; According to the arrival time difference between the seismic signal observation stations, a TDOA solution equation based on the Chan and Taylor algorithm is established, and the initial value of the source position of the source area is obtained by using the two-step maximum likelihood estimation of the Chan algorithm. The initial value of the source position is substituted into the Taylor algorithm for iterative calculation to obtain the source position positioning result of the source area, which is specifically: The TDOA equation between the reference station and other seismic signal observation stations is: Where (x s ,y s , z s ) is the focal coordinate, R1 is the distance from the reference station to the focal point, t i1 is the arrival time difference from other seismic signal observation stations to the reference station, v is the average velocity of P wave, i=2; After linearizing the above equation, we get: Among them, x i，1 = x i - x1, y i，1 = y i - y1, z i，1 = z i - z1; After the initial value of the earthquake source position is obtained using the Chan algorithm, the established target positioning model is converted into: ψ=hG Taylor d where δ = [dxdydz] T represents the position estimation error; ψ is the measurement error; h represents the difference between the true value and the measured value, R i1 is the distance difference between the reference site and other seismic signal observation stations, r i is the estimated initial position of the target z p =[x p ,y p , z p ] and the distance between each seismic signal observation station; Among them, Q is the covariance matrix of the measured value, and each iteration updates the initial estimated coordinates (x p +dx,y p +dy,z p +dz), when |δ|<positioning accuracy ε, the updated coordinates are the positioning results of the earthquake source position.

8. A computer device, characterized in that: The computer device includes a processor and a memory coupled to the processor, wherein: The memory stores program instructions for implementing the seismic wave locating method according to any one of claims 1 to 6; The processor is used to execute the program instructions stored in the memory to control the seismic wave positioning method.

9. A storage medium, characterized in that: The device stores program instructions executable by a processor, wherein the program instructions are used to execute the seismic wave locating method according to any one of claims 1 to 6.

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