Three-dimensional surface wave real-time imaging and monitoring method and system of short-period dense array

Abstract

The invention discloses a three-dimensional surface wave real-time imaging and monitoring method and system for a short-period dense array, and the method comprises the following steps: laying N seismometers in an exploration area, forming a plurality of stations, recording the number and coordinates of each seismometer, and forming a coordinate file; based on the coordinate file, any station O1 is taken as the circle center, a preset distance R is taken as the radius, a grouping array D (O1, R, n) containing n stations is determined, and n is smaller than or equal to N; acquiring seismic data corresponding to n stations in the grouped array D (O1, R, n) from the cloud server; on the basis of the cross correlation coefficients of all station pairs in the n stations, a frequency dispersion curve of the grouping array D (O1, R, n) is obtained through calculation, the frequency dispersion curve is inversed, and a speed-depth curve Vs (O1) below the station O1 is obtained; all N stations are traversed, speed-depth curves Vs (Oi) below all the stations are obtained, and i = 1, 2,..., N; and drawing a three-dimensional surface wave real-time image based on the space coordinates and the speed-depth curve Vs (Oi) of all stations. And the efficiency of dense array three-dimensional imaging is improved.

Description

Technical Field

[0001] This invention relates to the field of micro-motion exploration technology, and in particular to a three-dimensional surface wave real-time imaging and monitoring method and system for a short-period dense array. Background Technology

[0002] Microtremor survey method (MSM) is a geophysical exploration method based on the theory of stationary stochastic processes. This method extracts the Rayleigh surface wave dispersion curve from the vertical component of the natural field microtremor signal, and then inverts the shear wave velocity structure of the subsurface medium to detect subsurface structures. Compared with traditional active source exploration methods, MSM has significant advantages such as convenient field observation, no need for artificial seismic sources, and immunity to electromagnetic interference. Therefore, it is particularly suitable for exploration operations in areas with strong environmental vibration interference, such as urban areas and industrial and mining areas, and has been widely applied and developed rapidly in recent years.

[0003] With advancements in portable integrated short-period seismograph technology, short-period dense array passive source detection technology has become an important means of obtaining the fine structure of the Earth. In this technology, depending on the detection target, the spacing between detectors in the array is typically several meters to several kilometers, requiring continuous data acquisition for one to two months to obtain sufficient background noise data, which is then used to extract stable surface wave dispersion information through subsequent data processing.

[0004] It is evident that the traditional dense array micro-motion exploration method has the following shortcomings: On the one hand, the exploration cycle is too long: the data collection work that takes several months severely restricts the exploration efficiency, making it difficult to apply this method to scenarios with high timeliness requirements, such as rapid exploration in the early stage of engineering construction, emergency investigation and monitoring of geological disasters, etc.

[0005] On the other hand, the data processing workflow is complex and lagging: traditional seismic data processing is usually carried out in a centralized manner after all field data acquisition is completed, and its process includes steps such as array setup, cross-correlation calculation, dispersion curve extraction and inversion. This "acquire first, process later" model makes it impossible to obtain any intermediate or final results at the exploration site or during the data acquisition process, and it is impossible to achieve real-time or near-real-time perception and feedback of underground structures.

[0006] Therefore, there is an urgent need for an improved micro-motion exploration technology that can shorten the exploration cycle, simplify the data processing flow, and achieve rapid, real-time three-dimensional imaging and monitoring of underground velocity structures. Summary of the Invention

[0007] The technical problem this invention aims to solve is how to improve the efficiency of dense array three-dimensional imaging.

[0008] In a first aspect, to address the aforementioned technical problems, this invention provides a method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array, comprising the following steps: S1. N seismographs are deployed in the exploration area to form multiple stations, and the number and coordinates of each seismograph are recorded to form a coordinate file; the seismic data collected by the seismographs are transmitted to a cloud server in real time via a mobile network for storage. S2. Based on the coordinate file, with any of the stations O1 as the center and a preset distance R as the radius, determine a grouped array D(O1,R,n) containing n stations, where n≤N; S3. Obtain the seismic data corresponding to the n stations in the grouped array D(O1,R,n) from the cloud server; calculate the dispersion curve of the grouped array D(O1,R,n) based on the cross-correlation coefficient of all station pairs in the n stations, and invert the dispersion curve to obtain the velocity-depth curve Vs(O1) below the station O1. S4. Traverse all N stations in the exploration area, repeating steps S2 to S3, to obtain the velocity-depth curves Vs(O) below all stations. i ), where i = 1, 2, ..., N; S5. Based on the spatial coordinates of all stations and their corresponding velocity-depth curves Vs(O) i ), and draw a real-time three-dimensional surface wave image below the exploration area.

[0009] Furthermore, S3 specifically includes: S31. Calculate the spatial autocorrelation coefficients of all station pairs (A,B) in the grouped array D(O1,R,n), and then average them to obtain the average autocorrelation coefficients of the two points (A,B). S32. Using different speeds and frequencies, calculate the fitting difference between the average autocorrelation coefficient and the zero-order Bessel function, which is the dispersion spectrum of the station pair (A,B). S33. The dispersion spectra of all station pairs in the grouped array D(O1,R,n) are superimposed and normalized to obtain the average dispersion spectrum of the grouped array D(O1,R,n). S34. By automatically tracking the energy minimum of the average dispersion spectrum, the dispersion curve of the grouped array D(O1,R,n) is extracted, and the dispersion curve is inverted to obtain the velocity-depth curve Vs(O1) below the station O1.

[0010] Furthermore, the formula for calculating the average autocorrelation coefficient in S31 is as follows:

[0011] In the formula, This indicates the number of time periods into which the total collection time is divided; * indicates complex conjugate. The distance between points A and B of the station pair is (A, B). Indicates frequency; This indicates taking the real part of a complex number; , This represents the Fourier transform of stations A and B in the station pair (A,B).

[0012] Furthermore, the formula for calculating the average dispersion spectrum is as follows:

[0013] In the formula, Indicates frequency; Indicates speed; This represents the average autocorrelation coefficient of the station for points A and B (A, B). This represents the zeroth-order Bessel function.

[0014] Furthermore, S2 also includes: The seismic data corresponding to all stations within the grouped array D(O1,R,n) is stored in a separate data folder; and the station numbers and coordinates corresponding to all stations within the grouped array D(O1,R,n) are read from the coordinate file and copied to the data folder.

[0015] Furthermore, the step of real-time three-dimensional surface wave imaging in S5 includes: Read the coordinate file and plot the velocity-depth curve Vs(O) sequentially. i At least one of the following: scatter plot, inverse distance weighted interpolation plot, three-dimensional volume plot, or slice plot.

[0016] A second aspect of the present invention provides a three-dimensional surface wave real-time imaging and monitoring system for a short-period dense array using the method described above, comprising: Several short-period node seismographs are configured to acquire seismic data and transmit it in real time via a mobile network; A cloud server, configured to receive and store seismic data and coordinate files from the short-period node seismograph; and The data processing and imaging module is communicatively connected to the cloud server, and the data processing and imaging module is configured as follows: a. Dynamically construct a grouped array of stations with each station as the center based on the coordinate file; b. Obtain the seismic data of the corresponding group array from the cloud server, and calculate the dispersion curve and velocity-depth curve of each group array; c. After traversing all stations, generate a 3D image based on all velocity-depth curves and corresponding coordinates.

[0017] Furthermore, the short-period nodal seismograph supports 4G or 5G network communication.

[0018] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention constructs a grouped array of seismographs through a one-time deployment, and obtains the velocity-depth curves below all stations based on the calculation of the average autocorrelation coefficient and the average dispersion spectrum, thereby obtaining real-time three-dimensional surface wave imaging below the exploration area; at the same time, based on the seismic data processing in time periods, the data stream and data acquisition are synchronized to achieve real-time three-dimensional imaging; thus improving the efficiency of dense array three-dimensional imaging. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart disclosed in the present invention; Figure 2 This is a flowchart of the 3D-ESPAC disclosed in this invention; Figure 3 This is the overall flowchart disclosed in this invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The present invention aims to provide a method for real-time three-dimensional surface wave imaging and monitoring of short-period dense arrays, which solves the drawback of the traditional dense array micro-motion exploration work cycle of 1-2 months.

[0023] Please see Figure 1-3 The simple and rapid short-period dense array three-dimensional imaging technology proposed in this invention mainly includes the following steps: S1. Deploy N seismometers in the exploration area to form multiple stations, and record the number and coordinates of each seismometer to create a coordinate file. The seismic data collected by the seismometers is transmitted in real-time to a cloud server for storage via a mobile network.

[0024] Specifically, N(O) is evenly distributed at certain intervals in one go. i ) seismographs to cover the entire exploration area. Where N is the number of devices deployed at once, O i Number the equipment. For each piece of equipment deployed, RTK measurement points need to be set and recorded.

[0025] In a specific example, the record is divided into 5 columns: instrument number, measuring point number, and so on. i (i=1,2,…,N), coordinates X, Y, and Z; and save the data as a dat.txt file to the specified folder 3D-ESPAC. After all the seismographs are deployed, seismic data acquisition will begin.

[0026] In this scheme, seismic data acquired by each seismograph is transmitted in real time to a cloud server via a mobile network, and then stored in real time by a networked computer system. The data format is seismic-specific data, such as SAC data, with the measurement point number O... i Name the i (i=1,2,…,N) files and store them in the specified file 3D-ESPAC.

[0027] S2. Based on the coordinate file, take any station O1 as the center and the preset distance R as the radius to determine the grouped array D(O1,R,n) containing n stations, where n≤N.

[0028] Specifically, take one of the stations O1 as the center and draw a circle with a distance R as the radius. The circle must contain n (N>n≥2) stations. These n stations can form a grouped array D(O1,R,n) with station O1 as the center and R as the radius.

[0029] In a further proposed solution, the seismic data corresponding to all stations within the grouped array D(O1,R,n) is stored in a separate data folder; and the station numbers and coordinates corresponding to all stations within the grouped array D(O1,R,n) are read from the coordinate file and copied to the data folder.

[0030] Specifically, the coordinate file (i.e., the dat.txt file) from step S1 is plotted on the interactive software interface. After setting the radius R, any station O1 is selected. Then, the SAC seismic data corresponding to all stations in the corresponding group array D(O1,R,n) are selected from the file 3D-ESPAC and stored in a folder named D(O1,R,n). The coordinates and numbers of each station are read from the coordinate file and saved in the D(O1,R,n) folder. At this time, the D(O1,R,n) folder contains n SAC data and one txt coordinate data.

[0031] In a specific example, if the radius is set to 10m and O1 is selected, then O1 is the center and 10m is the radius. Draw a circle in the interactive software. Assuming that the circle encloses 5 devices (including O1), namely O1, O2, O3, O4, and O5, then copy the SAC seismic data files corresponding to these 5 devices from the 3D-ESPAC folder stored in step S1 and store them in the D(O1,10,5) file. At the same time, read and copy the coordinates corresponding to the five stations O1, O2, O3, O4, and O5 from the dat.txt file saved in the first step and save them in the D(O1, 10, 5) file.

[0032] S3. Obtain the seismic data corresponding to the n stations in the grouped array D(O1,R,n) from the cloud server; calculate the dispersion curve of the grouped array D(O1,R,n) based on the cross-correlation coefficient of all station pairs in the n stations, and invert the dispersion curve to obtain the velocity-depth curve Vs(O1) below station O1.

[0033] Specifically: S31. Calculate the spatial autocorrelation coefficients of all station pairs (A,B) in the grouped array D(O1,R,n), and then average them to obtain the average autocorrelation coefficients of the two points (A,B).

[0034] Let the station pairs in the array D(1,R,n) be denoted as (A, B) (A,B=1,2,…,n and A≠B). The Fourier transforms of stations A and B are SA and SB, respectively. Then the cross-correlation coefficient of the station pair (A,B) is: (1) In the formula, * represents complex conjugate; Let (A,B) be the distance between the station and points A and B. Indicates frequency; This indicates taking the real part of a complex number; , This represents the Fourier transform of stations A and B in the station pair (A,B).

[0035] Typically, a dense array of radio stations collects background noise data for a certain period of time. Therefore, the total time can be divided into M time periods, and the spatial autocorrelation coefficients of points A and B in each time period can be calculated separately. Then, the average autocorrelation coefficients of points A and B are obtained by summing and averaging the data, which satisfies the formula: (2) In the formula, This indicates the number of time periods into which the total collection time is divided.

[0036] S32. Using different speed and frequency scans, calculate the fitting difference between the average autocorrelation coefficient and the zero-order Bessel function, which is the dispersion spectrum of the station pair (A,B).

[0037] In this scheme, the formula for the dispersion spectrum of station pair (A, B) within the given velocity and frequency range is: (3) In the formula, Indicates frequency; Indicates speed; This represents the zeroth-order Bessel function of the first kind.

[0038] S33. The dispersion spectra of all station pairs in the grouped array D(O1,R,n) are superimposed and normalized to obtain the average dispersion spectrum of the grouped array D(O1,R,n).

[0039] In this scheme, the formula for calculating the average dispersion spectrum is: (4) In the formula, Indicates frequency; Indicates speed; This represents the average autocorrelation coefficient of the station with respect to points A and B (A,B). This represents the zeroth-order Bessel function of the first kind.

[0040] S34, Through automatic tracking The energy minimum of the average dispersion spectrum is used to extract the dispersion curve of the grouped array D(O1,R,n). The dispersion curve is then inverted to obtain the velocity-depth curve Vs(O1) below station O1. Vs(O1).dat is saved to the Vs folder.

[0041] S4. Automatically traverse all N stations in the exploration area, repeating steps S2 to S3, to obtain the velocity-depth curves Vs(O) below all stations. i ), where i=1,2,...,N, are all stored in the Vs folder.

[0042] S5. Based on the spatial coordinates of all stations and their corresponding velocity-depth curves Vs(O) i), to generate real-time three-dimensional surface wave images of the area beneath the exploration zone.

[0043] Specifically, all N Vs(Oi) (i=1,2,…,N) files are read, and the array coordinate position file dat.txt in S1 is read. Scatter plots, inverse distance weighted interpolation plots, three-dimensional volume plots and slice plots of Vs(Oi) are drawn in sequence to realize three-dimensional velocity structure imaging under the entire short-period dense array.

[0044] It should be noted that, because the equipment transmits data in real time, and as can be seen from step S31, when calculating dispersion, the seismic data needs to be calculated in time segments. Therefore, the equipment transmits one... A time period can be used to calculate a Dispersion and imaging over time periods. In other words, once the S1 seismic data acquisition begins, the data stream can be continuously looped to achieve real-time 3D imaging and update the data results in real time.

[0045] This invention also protects a three-dimensional surface wave real-time imaging and monitoring system for a short-period dense array of seismographs using the above-described method, which mainly includes several short-period nodal seismographs, a cloud server, and a data processing and imaging module.

[0046] Among them, several short-period node seismometers are configured to acquire seismic data and transmit it in real time via mobile networks. These short-period node seismometers support 4G or 5G network communication.

[0047] The cloud server is configured to receive and store seismic data and coordinate files from short-period node seismometers.

[0048] The data processing and imaging module communicates with the cloud server and is configured as follows: a. Dynamically construct grouped arrays with each station as the center based on coordinate files.

[0049] b. Obtain the seismic data of the corresponding group arrays from the cloud server, and calculate the dispersion curves and velocity-depth curves of each group array.

[0050] c. After traversing all stations, generate a 3D image based on all velocity-depth curves and corresponding coordinates.

[0051] Traditional dense seismic arrays typically have a spacing of several meters to several kilometers, with a working cycle of 1 to 2 months. The 3D-ESPAC solution in this project fully leverages the advantages of IoT technology, deploying 4G node seismic instruments at high density to significantly improve spatial sampling, reduce temporal sampling, and monitor changes in underground velocity structure in real time. This greatly improves the construction efficiency of dense seismic arrays and allows for application in various exploration and monitoring environments such as roads, mines, and goaf areas.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array, characterized in that, Includes the following steps: S1. Deploy N seismographs in the exploration area to form multiple stations, and record the number and coordinates of each seismograph to form a coordinate file; The seismic data collected by the seismograph is transmitted in real time to a cloud server for storage via a mobile network; S2. Based on the coordinate file, with any of the stations O1 as the center and a preset distance R as the radius, determine a grouped array D(O1,R,n) containing n stations, where n≤N; S3. Obtain the seismic data corresponding to the n stations in the grouped array D(O1,R,n) from the cloud server; calculate the dispersion curve of the grouped array D(O1,R,n) based on the cross-correlation coefficient of all station pairs in the n stations, and invert the dispersion curve to obtain the velocity-depth curve Vs(O1) below the station O1. S4. Traverse all N stations in the exploration area, repeating steps S2 to S3, to obtain the velocity-depth curves Vs(O) below all stations. i ), where i = 1, 2, ..., N; S5. Based on the spatial coordinates of all stations and their corresponding velocity-depth curves Vs(O) i ), and draw a real-time three-dimensional surface wave image below the exploration area.

2. The method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array according to claim 1, characterized in that, S3 specifically includes: S31. Calculate the spatial autocorrelation coefficients of all station pairs (A,B) in the grouped array D(O1,R,n), and then average them to obtain the average autocorrelation coefficients of the two points (A,B). S32. Using different speeds and frequencies, calculate the fitting difference between the average autocorrelation coefficient and the zero-order Bessel function, which is the dispersion spectrum of the station pair (A,B). S33. The dispersion spectra of all station pairs in the grouped array D(O1,R,n) are superimposed and normalized to obtain the average dispersion spectrum of the grouped array D(O1,R,n). S34. By automatically tracking the energy minimum of the average dispersion spectrum, the dispersion curve of the grouped array D(O1,R,n) is extracted, and the dispersion curve is inverted to obtain the velocity-depth curve Vs(O1) below the station O1.

3. The method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array according to claim 2, characterized in that, The formula for calculating the average autocorrelation coefficient in S31 is as follows: In the formula, This indicates the number of time periods into which the total collection time is divided; * indicates complex conjugate. The distance between points A and B of the station pair is (A, B). Indicates frequency; This indicates taking the real part of a complex number; , This represents the Fourier transform of stations A and B in the station pair (A,B).

4. The method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array according to claim 3, characterized in that, The formula for calculating the average dispersion spectrum is: In the formula, Indicates frequency; Indicates speed; This represents the average autocorrelation coefficient of the station for points A and B (A, B). This represents the zeroth-order Bessel function.

5. The method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array according to claim 1, characterized in that, S2 further includes: The seismic data corresponding to all stations within the grouped array D(O1,R,n) is stored in a separate data folder; and the station numbers and coordinates corresponding to all stations within the grouped array D(O1,R,n) are read from the coordinate file and copied to the data folder.

6. The method for real-time imaging and monitoring of three-dimensional surface waves using a short-period dense array according to claim 1, characterized in that, The steps for rendering the real-time three-dimensional surface wave image in S5 include: Read the coordinate file and plot the velocity-depth curve Vs(O) sequentially. i At least one of the following: scatter plot, inverse distance weighted interpolation plot, three-dimensional volume plot, or slice plot.

7. A three-dimensional surface wave real-time imaging and monitoring system using a short-period dense array employing the method described in any one of claims 1-6, characterized in that, include: Several short-period node seismographs are configured to acquire seismic data and transmit it in real time via a mobile network; A cloud server, configured to receive and store seismic data and coordinate files from the short-period node seismograph; and The data processing and imaging module is communicatively connected to the cloud server, and the data processing and imaging module is configured as follows: a. Dynamically construct a grouped array of stations with each station as the center based on the coordinate file; b. Obtain the seismic data of the corresponding group array from the cloud server, and calculate the dispersion curve and velocity-depth curve of each group array; c. After traversing all stations, generate a 3D image based on all velocity-depth curves and corresponding coordinates.

8. The three-dimensional surface wave real-time imaging and monitoring system for short-period dense arrays according to claim 7, characterized in that, The short-period nodal seismograph supports 4G or 5G network communication.

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