An imaging method and device for urban background noise based on short-period dense array

Abstract

This application discloses a method and device for imaging urban background noise based on a short-period dense array. The method comprises: obtaining a background noise signal corresponding to the short-period dense array and preprocessing the background noise signal; performing frequency domain correlation calculations on the processed background noise signal to obtain Rayleigh wave phase velocity and attenuation parameters; iteratively inverting and training a preset initial model based on the Rayleigh wave phase velocity to obtain shear wave velocity; and performing mapping processing based on the shear wave velocity and attenuation parameters. By performing frequency domain correlation calculations on the power spectrum of the short-period dense array, the corresponding Rayleigh wave phase velocity, attenuation coefficient, and medium anisotropic structure information are obtained based on least squares fitting. The Rayleigh wave phase velocity and attenuation coefficient are then inverted to obtain the underground shear wave velocity structure. This effectively avoids the complex time domain Green's function calculation and low signal-to-noise ratio defects of traditional short-period dense array calculation methods.

Description

Technical Field

[0001] The present application belongs to the technical field of urban background noise processing, and in particular relates to an imaging method and device for urban background noise based on a short-period dense array. Background Art

[0002] Currently, precise measurement technology for urban shallow subsurface structures can be applied in areas such as urban underground structure detection, underground structure safety monitoring, and geological collapse early warning. In recent years, short-period dense array underground measurement technology, due to its green, environmentally friendly, portable, and no need for active source signal excitation, has attracted increasing attention from researchers and users in the monitoring field both domestically and internationally. With the continuous development and improvement of short-period dense array technology, the application of short-period dense array technology to observe shallow subsurface velocity structures has demonstrated significant advantages over traditional underground structure monitoring technologies in terms of monitoring range, environmental adaptability, and instrument deployment.

[0003] Imaging shallow surface velocity and attenuation structure using urban background noise signals from short-period dense arrays is an important part of studying urban shallow surface velocity structure. A common method of underground exploration based on background noise signals recorded by short-period dense arrays is to perform cross-correlation calculations on the signals recorded at different locations of the short-period dense array to obtain the approximate Green's functions corresponding to different locations of the short-period dense array to study the underground structure.

[0004] However, the application of existing short-period dense array technology in cities is still in its infancy and lacks a systematic and complete workflow. Although there have been some application cases of short-period dense array technology, existing application research has only achieved the study of shallow surface velocity structure. It is not only strictly limited in array distribution, but also cannot guarantee the accuracy and effectiveness of the results. Summary of the Invention

[0005] This application aims to address the aforementioned technical issues: the application of existing short-period dense array technology in cities is still in its infancy and lacks a systematic and comprehensive workflow. Although some short-period dense array technology applications have been reported, existing application research has only achieved the study of shallow surface velocity structure. Not only is the array distribution strictly limited, but the accuracy and effectiveness of the results cannot be guaranteed. Therefore, a method and device for imaging urban background noise based on a short-period dense array is proposed. The specific solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides an imaging method for urban background noise based on a short-period dense array, comprising:

[0007] Obtain the background noise signal corresponding to the short-period dense array and pre-process the background noise signal;

[0008] Perform frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal;

[0009] The preset initial model is iteratively inverted based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained based on the minimum fitting error between the dispersion curve of the preset initial model in the iterative inversion calculation and the observed value. The number of layers in the preset initial model is n, and the minimum thin layer thickness is h, where n and h are both positive integers.

[0010] The plot is processed based on the shear wave velocity and attenuation parameters corresponding to the background noise signal.

[0011] In an optional solution of the first aspect, preprocessing the background noise signal includes:

[0012] Determine at least m acquisition point locations based on a short-period dense array, and classify and process background noise signals based on the at least m acquisition point locations; where m is a positive integer greater than or equal to 5;

[0013] Performing signal preprocessing on the background noise signal after classification processing;

[0014] The background noise signal after signal preprocessing is subjected to bandpass filtering.

[0015] In another optional solution of the first aspect, frequency domain correlation calculation is performed on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameter corresponding to the background noise signal, including:

[0016] Determine a target collection point position from at least m collection point positions; wherein the target collection point position is any one of the at least m collection point positions;

[0017] Screening out background noise signals corresponding to a first set of collection point positions that are within a preset distance from a target collection point position from the background noise signals after bandpass filtering; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point positions;

[0018] Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency;

[0019] A first frequency domain parameter at a target acquisition point position at a preset frequency is fitted to obtain a first Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

[0020] In another optional solution of the first aspect, frequency domain correlation calculation is performed based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency, including:

[0021] Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method;

[0022] Based on the signal segments corresponding to the target acquisition point positions and the signal segments corresponding to the first acquisition point position set, obtaining frequency domain parameters corresponding to each signal segment at the target acquisition point position at a preset frequency;

[0023] The frequency domain parameters corresponding to each signal segment are superimposed and calculated to obtain the first frequency domain parameters of the target acquisition point position at the preset frequency.

[0024] In yet another optional solution of the first aspect, after fitting the first frequency domain parameters at the target acquisition point at a preset frequency to obtain the first Rayleigh wave phase velocity at the target acquisition point and the attenuation parameter corresponding to the first Rayleigh wave phase velocity, the method further includes:

[0025] Dividing intervals within a preset distance from the target collection point location according to a preset angle, and determining a second collection point location set corresponding to each divided interval from the first collection point location set; wherein the second collection point location set includes at least s collection point locations; wherein s is a positive integer greater than or equal to 5;

[0026] Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency;

[0027] The second frequency domain parameters at the target acquisition point position at the preset frequency are fitted to obtain the second Rayleigh wave phase velocity at the target acquisition point position and the attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0028] In another optional solution of the first aspect, iterative inversion training is performed on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained according to the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation, including:

[0029] Performing an inversion calculation on the first Rayleigh wave phase velocity at the target acquisition point, and iteratively training a preset initial model based on the calculated first Rayleigh wave phase velocity at the target acquisition point until the iterative error of the preset initial model is within a preset error range;

[0030] Input the first Rayleigh wave phase velocity at the target acquisition point into the trained preset initial model to obtain the first shear wave velocity at the target acquisition point;

[0031] The second Rayleigh wave phase velocity at the target acquisition point is input into the trained preset initial model to obtain the second shear wave velocity at the target acquisition point.

[0032] In another optional solution of the first aspect, performing mapping processing according to the shear wave velocity and attenuation parameter corresponding to the background noise signal includes:

[0033] Determining at least one mapping acquisition point position based on a short-period dense array, and performing mapping processing according to a first shear wave velocity at the at least one mapping acquisition point position and an attenuation parameter corresponding to the first shear wave velocity;

[0034] The mapping process is performed according to the second shear wave velocity at the at least one mapping acquisition point and the attenuation parameter corresponding to the second shear wave velocity.

[0035] In a second aspect, an embodiment of the present application provides an imaging device for urban background noise based on a short-period dense array, comprising:

[0036] A preprocessing module is used to obtain the background noise signal corresponding to the short-period dense array and preprocess the background noise signal;

[0037] The signal calculation module is used to perform frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal;

[0038] A signal processing module is used to perform iterative inversion training on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and obtain the shear wave velocity corresponding to the background noise signal based on the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation; wherein the number of layers of the preset initial model is n, and the minimum thin layer thickness is h, where n and h are both positive integers;

[0039] The drawing module is used for performing drawing processing according to the shear wave velocity and attenuation parameters corresponding to the background noise signal.

[0040] In an optional solution of the second aspect, the preprocessing module specifically includes:

[0041] A first preprocessing unit is configured to determine at least m acquisition point positions based on a short-period dense array, and classify and process background noise signals according to the at least m acquisition point positions; wherein m is a positive integer greater than or equal to 5;

[0042] A second preprocessing unit is used to perform signal preprocessing on the background noise signal after the classification process;

[0043] The third preprocessing unit is used to perform bandpass filtering on the background noise signal after signal preprocessing.

[0044] In another optional solution of the second aspect, the signal calculation module specifically includes:

[0045] a determination unit, configured to determine a target collection point position from at least m collection point positions; wherein the target collection point position is any one of the at least m collection point positions;

[0046] a screening unit, configured to screen out background noise signals corresponding to a first set of collection point positions that are within a preset distance from a target collection point position from the background noise signals after bandpass filtering; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point position;

[0047] A first calculation unit is configured to perform frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency;

[0048] The first processing unit is configured to perform fitting processing on a first frequency domain parameter of the target acquisition point position at a preset frequency to obtain a first Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

[0049] In yet another optional solution of the second aspect, the first computing unit is specifically configured to:

[0050] Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method;

[0051] Based on the signal segments corresponding to the target acquisition point positions and the signal segments corresponding to the first acquisition point position set, obtaining frequency domain parameters corresponding to each signal segment at the target acquisition point position at a preset frequency;

[0052] The frequency domain parameters corresponding to each signal segment are superimposed and calculated to obtain the first frequency domain parameters of the target acquisition point position at the preset frequency.

[0053] In another optional solution of the second aspect, the signal calculation module specifically further includes:

[0054] a dividing unit, configured to divide intervals within a preset distance from the target collection point position according to a preset angle, and determine a second collection point position set corresponding to each divided interval from the first collection point position set; wherein the second collection point position set includes at least s collection point positions; wherein s is a positive integer greater than or equal to 5;

[0055] A second calculation unit is configured to perform frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency;

[0056] The second processing unit is used to perform fitting processing on the second frequency domain parameters of the target acquisition point position at a preset frequency to obtain a second Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0057] In another optional solution of the second aspect, the signal processing module specifically includes:

[0058] a training unit, configured to perform an inverse calculation of the first Rayleigh wave phase velocity at the target acquisition point, and iteratively train a preset initial model based on the calculated first Rayleigh wave phase velocity at the target acquisition point until an iterative error of the preset initial model falls within a preset error range;

[0059] The first output unit of the model is used to input the first Rayleigh wave phase velocity at the target acquisition point into the trained preset initial model to obtain the first shear wave velocity at the target acquisition point;

[0060] The second output unit of the model is used to input the second Rayleigh wave phase velocity at the target acquisition point position into the trained preset initial model to obtain the second shear wave velocity at the target acquisition point position.

[0061] In another optional solution of the second aspect, the drawing module specifically includes:

[0062] A first mapping unit is configured to determine a position of at least one mapping acquisition point based on a short-period dense array, and perform mapping processing according to a first shear wave velocity at the at least one mapping acquisition point and an attenuation parameter corresponding to the first shear wave velocity;

[0063] The second drawing unit is configured to perform drawing processing according to the second shear wave velocity at at least one drawing acquisition point and an attenuation parameter corresponding to the second shear wave velocity.

[0064] In a third aspect, an embodiment of the present application provides an imaging device based on short-period dense array urban background noise, including a processor and a memory;

[0065] The processor is connected to the memory;

[0066] a memory for storing executable program code;

[0067] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the imaging method based on short-period dense array urban background noise provided by the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect.

[0068] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the imaging method based on short-period dense array urban background noise provided by the first aspect of the embodiment of the present application or any implementation method of the first aspect can be implemented.

[0069] In an embodiment of the present application, when imaging is performed based on urban background noise, a background noise signal corresponding to a short-period dense array is first obtained, and the background noise signal is preprocessed; then, frequency domain correlation calculation is performed on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal; then, the preset initial model is iteratively trained based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained based on the trained preset initial model; then, drawing processing is performed based on the shear wave velocity and attenuation parameters corresponding to the background noise signal. By calculating the frequency-domain correlation of the power spectrum of a short-period dense array, we can obtain correlation coefficients at different locations, frequencies, and directions. Then, through least-squares fitting, we can obtain the corresponding Rayleigh wave phase velocity, attenuation coefficient, and anisotropic structure information of the medium. Subsequently, through inversion of the Rayleigh wave phase velocity and attenuation coefficient, we can obtain the underground shear wave velocity structure based on the urban background noise signal of the short-period dense array. This method is not only applicable to short-period dense arrays of various arrangements, but also effectively avoids the complex time-domain calculation of Green's functions and the low signal-to-noise ratio shortcomings of traditional short-period dense array calculation methods. It has important practical applications in a variety of technical fields, including short-period dense array technology, urban background noise technology, regional underground structure detection research, urban underground space surveys, urban disaster prevention and early warning, underground resource development, digital signal processing, and earth science. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 A schematic diagram of a flow chart of an imaging method for urban background noise based on a short-period dense array provided in an embodiment of the present application;

[0072] Figure 2 A schematic diagram of the effect of selecting a collection point location provided in an embodiment of the present application;

[0073] Figure 3 A schematic diagram of the structure of an imaging device for urban background noise based on a short-period dense array provided in an embodiment of the present application;

[0074] Figure 4 A schematic structural diagram of another imaging device based on short-period dense array urban background noise provided in an embodiment of the present application. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0076] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application. Different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, even though the embodiment may not be clearly described in the following text.

[0077] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the elements described without departing from the scope of the present application. Various examples may appropriately omit, replace, or add various processes or components. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. In addition, features described in some examples may be combined in other examples.

[0078] See also Figure 1 , Figure 1 A flow chart of an imaging method for urban background noise based on a short-period dense array provided in an embodiment of the present application is shown.

[0079] like Figure 1 As shown, the imaging method based on short-period dense array urban background noise may include at least the following steps:

[0080] Step 102: Acquire a background noise signal corresponding to the short-period dense array and pre-process the background noise signal.

[0081] The imaging method based on short-period dense array urban background noise in the embodiment of the present application can be, but is not limited to, applied to a control terminal that can collect short-period dense array urban background noise. The control terminal can not only be used to monitor the collected short-period dense array urban background noise, but also to process the collected short-period dense array urban background noise so as to conduct subsequent research on underground attenuation structure and anisotropy change measurement through imaging.

[0082] It is understandable that the short-period dense array in this field is usually composed of hundreds or thousands of seismic stations, which can achieve very large spatial coverage by taking advantage of the number of instruments. The stations can be flexibly deployed in local key areas with spacing of meters to obtain richer earthquake information. In the embodiment of the present application, the short-period dense array can be used to obtain background noise signals emitted by the city, and is not limited to this.

[0083] Specifically, when imaging based on urban background noise, the background noise signal collected by the short-period dense array can be first obtained and preprocessed to ensure the accuracy and effectiveness of the signal. The short-period dense array in the embodiments of the present application can include, but is not limited to, multiple acquisition devices. These multiple acquisition devices can be arranged according to preset rules, for example, in an m*n matrix, and each acquisition device can acquire background noise signals. In other words, the background noise signal corresponding to the short-period dense array can include the background noise signals collected by all acquisition devices.

[0084] As an option in the embodiment of the present application, preprocessing the background noise signal includes:

[0085] Determine at least m acquisition point locations based on a short-period dense array, and classify and process background noise signals based on the at least m acquisition point locations; where m is a positive integer greater than or equal to 5;

[0086] Performing signal preprocessing on the background noise signal after classification processing;

[0087] The background noise signal after signal preprocessing is subjected to bandpass filtering.

[0088] Specifically, at least five acquisition devices can be identified from the multiple acquisition devices included in the short-period dense array. Based on these at least five acquisition devices, background noise signals can be classified to classify the background noise signals corresponding to each acquisition device. The background noise signals corresponding to each acquisition device can be, but are not limited to, differentiated and represented based on the acquisition point locations corresponding to each acquisition device. Taking the short-period dense array as an example, comprising acquisition devices A, B, and C, the background noise signal corresponding to acquisition device A can be represented as a, and its corresponding acquisition point location can be, but are not limited to, (Xa, Ya); the background noise signal corresponding to acquisition device B can be represented as b, and its corresponding acquisition point location can be, but are not limited to, (Xb, Yb); and the background noise signal corresponding to acquisition device C can be represented as c, and its corresponding acquisition point location can be, but are not limited to, (Xc, Yc). It will be understood that the acquisition point locations here can be, but are not limited to, determined based on a planar rectangular coordinate system established by the short-period dense array. The coordinates of each acquisition device in this planar rectangular coordinate system are the acquisition point locations of each acquisition device. In order to ensure the effectiveness and speed of data calculation in the embodiment of the present application, the background noise signal corresponding to each acquisition point position can also be converted and processed so that the data format of the background noise signal is converted into SAC binary format data commonly used in seismological research.

[0089] Furthermore, after classifying the background noise signal corresponding to the short-period dense array, since the background noise signal always has a non-zero mean or a long-period linear trend, which will affect the analysis of the signal, the processed background noise signal can be subjected to signal trend removal processing or signal mean removal processing.

[0090] Furthermore, after preprocessing the background noise signal corresponding to the short-period dense array, in order to remove the influence of the interference signal, the processed background noise signal can also be processed to remove the instrument response. It is understood that the background noise signal corresponding to the short-period dense array can be represented by, but is not limited to, the following formula:

[0091] u(t)=s(t)*a(t)*i(t)

[0092] In the above formula, s(t) represents the source term, g(t) represents the path term, i(t) represents the instrument response term, and * represents a convolution calculation. In other words, the background noise signal corresponding to a short-period, dense array can be represented by the convolution of the source term, the path term, and the instrument response term. In the study of urban background noise from short-period, dense arrays, the signal related to the instrument response term in the acquired background noise signal is an interference term and needs to be removed, retaining only the source term and path term related to the shallow surface structure.

[0093] Furthermore, after removing instrument response from the background noise signal corresponding to the short-period dense array, bandpass filtering can be performed on the background noise signal to improve its signal-to-noise ratio. This can result in a signal within a fixed frequency band, thereby improving the signal-to-noise ratio. It is understood that, in signal acquisition, a higher signal sampling rate is generally used to facilitate subsequent application research of other methods. However, excessively high sampling rates reduce the efficiency of urban background noise calculations. Based on this, the bandpass filtered background noise signal corresponding to the short-period dense array can also be downsampled to a 20 Hz sampling rate, but this is not limited to this.

[0094] Step 104: Perform frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal.

[0095] Specifically, after preprocessing the background noise signal, a target collection point location can be determined from the multiple collection point locations included in the background noise signal. The target collection point location can be any one of the multiple collection point locations included in the background noise signal. A first collection point location set comprising at least five collection point locations can be determined within a preset distance from the target collection point location, along with the background noise signal corresponding to the first collection point location set. The interval within the preset distance from the target collection point location can be, but is not limited to, a circular interval with the target collection point location as the center and the preset distance as the radius. The set of all collection point locations within the circular interval, excluding the target collection point location, can be used as the first collection point location set.

[0096] Furthermore, after determining the first set of collection point locations, a frequency domain correlation calculation can be performed based on the background noise signals corresponding to the target collection point locations and the background noise signals corresponding to the first set of collection point locations to obtain first frequency domain parameters of the target collection point locations at a preset frequency. The recording time of the background noise signals corresponding to the first set of collection point locations must be consistent with the recording time of the background noise signals corresponding to the target collection point locations.

[0097] Here, the target acquisition point position can be expressed as (x0, y0) and the preset distance can be expressed as r as an example. The first frequency domain parameter of the target acquisition point position at the preset frequency ω0 can be expressed by, but is not limited to, the following formula:

[0098]

[0099] in,

[0100] Sx1x2(ω0,θ)=E[X1(t,ω0),X2(t,ω0)]

[0101] Sx1(ω0)=E[|X1(t,ω0)| 2 ]

[0102] Sx2(ω0)=E[|X2(t,ω0)| 2 ]

[0103] In the above formula, Sx1x2(ω0, θ) can be expressed as the cross-power spectrum of any two acquisition point positions in the first acquisition point position set at the preset frequency ω0, Sx1(ω0) can be expressed as the auto-power spectrum of the acquisition point position with coordinates corresponding to x1 in the first acquisition point position set at the preset frequency ω0, Sx2(ω0) can be expressed as the auto-power spectrum of the acquisition point position with coordinates corresponding to x2 in the first acquisition point position set at the preset frequency ω0, X1(t, ω0) can be expressed as the background noise signal of the acquisition point position with coordinates corresponding to x1 in the first acquisition point position set at the preset frequency ω0, X2(t, ω0) can be expressed as the background noise signal of the acquisition point position with coordinates corresponding to x2 in the first acquisition point position set at the preset frequency ω0, and ρ(ω0, r) can be expressed as the frequency domain related parameter of the target acquisition point position at the preset frequency ω0.

[0104] As another optional embodiment of the present application, frequency domain correlation calculation is performed based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain the first frequency domain parameter of the target acquisition point position at a preset frequency, including:

[0105] Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method;

[0106] Based on the signal segments corresponding to the target acquisition point positions and the signal segments corresponding to the first acquisition point position set, obtaining frequency domain parameters corresponding to each signal segment at the target acquisition point position at a preset frequency;

[0107] The frequency domain parameters corresponding to each signal segment are superimposed and calculated to obtain the first frequency domain parameters of the target acquisition point position at the preset frequency.

[0108] Specifically, in order to ensure the calculation accuracy of long-term background noise signals, a signal segmentation method can be used to collect the background noise signals corresponding to the target acquisition point position and the background noise signals corresponding to the first acquisition point position set are cut into at least two signal segments according to a preset cutting method. A cosine window is applied to each time signal record to prevent spectrum leakage, and the signal-to-noise ratio and calculation accuracy are improved by superimposing the frequency domain parameters corresponding to each signal segment. This not only reduces the complexity of the overall calculation process, but also facilitates the rapid storage, processing and analysis of data.

[0109] It is understandable that the calculation method of the frequency domain parameters corresponding to each signal segment here can refer to the above steps, and will not be described in detail here.

[0110] Furthermore, after the frequency domain related parameters of the target acquisition point position are calculated, the frequency domain related parameters of the target acquisition point position can be fitted by, but not limited to, applying a nonlinear least squares fitting algorithm to make the frequency domain related parameters of the target acquisition point position between 0 and 2r.

[0111] Then, the frequency domain related parameters of the target acquisition point position after fitting mentioned above can be brought back into the first frequency domain parameter calculation formula mentioned above, and the difference between the measured fundamental Bezier curve observation value Re[γ(fr)] and the fitted J0 is calculated using the least squares method. The C in J0 when the residual between the observation value and the fitted value is the smallest is the first Rayleigh wave phase velocity C of the target acquisition point position at this frequency. R and the first Rayleigh wave phase velocity C R The corresponding attenuation parameter α.

[0112]

[0113]

[0114] It can be understood that by introducing the attenuation factor that varies with frequency, the change in signal attenuation is taken into account in the fitting process, so that the attenuation information of the signal is obtained while calculating the signal wave velocity.

[0115] It should be noted that, based on the above steps, the first Rayleigh wave phase velocity of each acquisition point position among the multiple acquisition point positions included in the background noise signal and the attenuation parameter corresponding to the first Rayleigh wave phase velocity can be obtained.

[0116] As another optional embodiment of the present application, after fitting the first frequency domain parameters at the target acquisition point position at a preset frequency to obtain the first Rayleigh wave phase velocity at the target acquisition point position and the attenuation parameter corresponding to the first Rayleigh wave phase velocity, the method further includes:

[0117] Dividing intervals within a preset distance from the target collection point location according to a preset angle, and determining a second collection point location set corresponding to each divided interval from the first collection point location set; wherein the second collection point location set includes at least s collection point locations; wherein s is a positive integer greater than or equal to 5;

[0118] Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency;

[0119] The second frequency domain parameters at the target acquisition point position at the preset frequency are fitted to obtain the second Rayleigh wave phase velocity at the target acquisition point position and the attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0120] The embodiment of the present application can also determine the anisotropy information corresponding to the target acquisition point position according to the target acquisition point position. Specifically, when the acquisition points recorded by the short-period dense array are dense enough, after determining the target acquisition point position, the anisotropy information corresponding to the target acquisition point position can be determined according to the preset angle. An interval within a preset distance from the target collection point location is evenly divided along i directions to obtain multiple divided intervals corresponding to the target collection point location. Each divided interval can include at least one collection point location, and the at least one collection point location belongs to any one collection point location in the first collection point location set.

[0121] Furthermore, frequency domain correlation calculation can be performed based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain the target acquisition point position at the preset frequency ω0. The second frequency domain parameter of the direction can be expressed by, but not limited to, the following formula:

[0122]

[0123]

[0124] in,

[0125] Furthermore, after calculating the second frequency domain parameter of the target acquisition point position, a nonlinear least squares fitting algorithm can be applied to fit the second frequency domain parameter of the target acquisition point position, but is not limited to fitting the second frequency domain parameter of the target acquisition point position, so that the second frequency domain parameter of the target acquisition point position is between 0 and 2r.

[0126] Next, the second frequency domain parameters of the target acquisition point position after the fitting processing mentioned above can be re-introduced into the second frequency domain parameter calculation formula mentioned above, and the least squares method is used to calculate the difference between the measured fundamental Bezier curve observation value Re[γ(f, r)] and the fitted J0. When the residual between the observed value and the fitted value is the smallest, C in J0 is the second Rayleigh wave phase velocity at the target acquisition point position at this frequency and the attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0127] See here Figure 2 The diagram shows a schematic diagram of the selection effect of a collection point location provided by an embodiment of the present application. Figure 2 As shown, the arrangement of the short-period dense array can be a 5*29 matrix, in which the target acquisition point can be determined as (x0, y0), and the preset distance can be r. Then the first acquisition point position set can include all acquisition point positions within a circle with an origin of (x0, y0) and a radius of r, and the second acquisition point position set can include all acquisition point positions within an interval uniformly divided by the circle with an origin of (x0, y0) and a radius of r, and the angle of the uniform division is

[0128] Step 106: Perform iterative inversion training on the preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and obtain the shear wave velocity corresponding to the background noise signal based on the minimum fitting error between the dispersion curve of the preset initial model in the iterative inversion calculation and the observed value.

[0129] Specifically, a thin-layer inversion method can be used to invert and calculate the first Rayleigh wave phase velocity at the target acquisition point, and the preset initial model can be iteratively trained based on the calculated first Rayleigh wave phase velocity at the target acquisition point. The preset initial model is a homogeneous layer, and the thickness of each finite element or layer in the model must be less than the wavelength to ensure accuracy. In the embodiment of the present application, the number of layers n and the minimum thin layer thickness h of the preset initial model can be given. The total model thickness must be large enough so that the preset initial model can be approximated as a free interface, and the Poisson's ratio and medium density of the preset initial model can be estimated based on actual geological conditions. It is understood that during the iterative training of the preset initial model, the acceptable error range of the preset initial model during convergence can be limited until the iterative error of the preset initial model falls within the preset error range.

[0130] After obtaining the trained preset initial model, the first Rayleigh wave phase velocity at the target acquisition point position can be input into the trained preset initial model to obtain the average shear wave velocity at the target acquisition point position; and the second Rayleigh wave phase velocity at the target acquisition point position can also be input into the trained preset initial model to obtain the average anisotropic Rayleigh wave phase velocity at the target acquisition point position.

[0131] It is understandable that after obtaining the trained preset initial model, the average shear wave velocity, and the average anisotropic Rayleigh wave phase velocity, the quality factor of the preset initial model at different times can also be calculated. The calculation method can be, but is not limited to, expressed by the following formula:

[0132]

[0133] Among them, α i It can be expressed as the average shear wave velocity of the i-th layer of the preset initial model, β i It can be expressed as the average anisotropic Rayleigh wave phase velocity of the i-th layer of the preset initial model, Q μ It can be expressed as the quality factor of the i-th layer of the preset initial model.

[0134] It should be noted that, based on the above-mentioned steps, the average Rayleigh wave phase velocity and the average anisotropic Rayleigh wave phase velocity of each acquisition point position among the multiple acquisition point positions included in the background noise signal can be obtained.

[0135] Step 108: Perform mapping processing according to the shear wave velocity and attenuation parameters corresponding to the background noise signal.

[0136] Specifically, when mapping the average Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal, one can first determine a mapping acquisition point location within the short-period dense array, and then generate a one-dimensional image based on the average Rayleigh wave phase velocity and attenuation parameters corresponding to the one mapping acquisition point location. The one-dimensional image can represent the subsurface shear wave velocity at the specific acquisition point location of the short-period dense array. Alternatively, at least two consecutive mapping acquisition point locations can be determined within the short-period dense array, and then generate a two-dimensional image based on the average Rayleigh wave phase velocity and attenuation parameters corresponding to the at least two consecutive mapping acquisition point locations. The two-dimensional image can represent the average shear wave velocity at different thicknesses of the shallow subsurface medium along a designated observation line or surface of the short-period dense array.

[0137] When plotting the average anisotropic Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal, one can first determine a mapping acquisition point location within the short-period dense array, and then generate a one-dimensional image based on the average anisotropic Rayleigh wave phase velocity and attenuation parameters corresponding to the one mapping acquisition point location. The one-dimensional image can represent the shear wave velocity varying in different directions at a specific acquisition point location within the short-period dense array. Alternatively, at least two consecutive mapping acquisition point locations can be determined within the short-period dense array, and then generate a two-dimensional image based on the average anisotropic Rayleigh wave phase velocity and attenuation parameters corresponding to the at least two consecutive mapping acquisition point locations. The two-dimensional image can represent the average anisotropic Rayleigh wave phase velocity at different thicknesses of the shallow subsurface medium within a designated observation line or surface of the short-period dense array.

[0138] See also Figure 3 , Figure 3 A schematic structural diagram of an imaging device for urban background noise based on a short-period dense array provided in an embodiment of the present application is shown.

[0139] like Figure 3 As shown, the imaging device based on short-period dense array urban background noise may include at least a pre-processing module 301, a signal calculation module 302, a signal processing module 303 and a mapping module 304, wherein:

[0140] The preprocessing module 301 is used to obtain the background noise signal corresponding to the short-period dense array and preprocess the background noise signal;

[0141] The signal calculation module 302 is used to perform frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameter corresponding to the background noise signal;

[0142] The signal processing module 303 is configured to perform iterative inversion training on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and obtain the shear wave velocity corresponding to the background noise signal based on the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation; wherein the number of layers in the preset initial model is n, and the minimum thin layer thickness is h, where n and h are both positive integers;

[0143] The drawing module 304 is configured to perform drawing processing according to the shear wave velocity and attenuation parameters corresponding to the background noise signal.

[0144] In some possible embodiments, the preprocessing module specifically includes:

[0145] A first preprocessing unit is configured to determine at least m acquisition point positions based on a short-period dense array, and classify and process background noise signals according to the at least m acquisition point positions; wherein m is a positive integer greater than or equal to 5;

[0146] A second preprocessing unit is used to perform signal preprocessing on the background noise signal after the classification process;

[0147] The third preprocessing unit is used to perform bandpass filtering on the background noise signal after signal preprocessing.

[0148] In some possible embodiments, the signal calculation module specifically includes:

[0149] a determination unit, configured to determine a target collection point position from at least m collection point positions; wherein the target collection point position is any one of the at least m collection point positions;

[0150] a screening unit, configured to screen out background noise signals corresponding to a first set of collection point positions that are within a preset distance from a target collection point position from the filtered background noise signals; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point position;

[0151] A first calculation unit is configured to perform frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency;

[0152] The first processing unit is configured to perform fitting processing on a first frequency domain parameter of the target acquisition point position at a preset frequency to obtain a first Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

[0153] In some possible embodiments, the first computing unit is specifically configured to:

[0154] Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method;

[0155] Based on the signal segments corresponding to the target acquisition point positions and the signal segments corresponding to the first acquisition point position set, obtaining frequency domain parameters corresponding to each signal segment at the target acquisition point position at a preset frequency;

[0156] The frequency domain parameters corresponding to each signal segment are superimposed and calculated to obtain the first frequency domain parameters of the target acquisition point position at the preset frequency.

[0157] In some possible embodiments, the signal calculation module specifically further includes:

[0158] a dividing unit, configured to divide intervals within a preset distance from the target collection point position according to a preset angle, and determine a second collection point position set corresponding to each divided interval from the first collection point position set; wherein the second collection point position set includes at least s collection point positions; wherein s is a positive integer greater than or equal to 5;

[0159] A second calculation unit is configured to perform frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency;

[0160] The second processing unit is used to perform fitting processing on the second frequency domain parameters of the target acquisition point position at a preset frequency to obtain a second Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0161] In some possible embodiments, the signal processing module specifically includes:

[0162] a training unit, configured to perform an inverse calculation of the first Rayleigh wave phase velocity at the target acquisition point, and iteratively train a preset initial model based on the calculated first Rayleigh wave phase velocity at the target acquisition point until an iterative error of the preset initial model falls within a preset error range;

[0163] The first output unit of the model is used to input the first Rayleigh wave phase velocity at the target acquisition point into the trained preset initial model to obtain the first shear wave velocity at the target acquisition point;

[0164] The second output unit of the model is used to input the second Rayleigh wave phase velocity at the target acquisition point position into the trained preset initial model to obtain the second shear wave velocity at the target acquisition point position.

[0165] In some possible embodiments, the drawing module specifically includes:

[0166] A first mapping unit is configured to determine a position of at least one mapping acquisition point based on a short-period dense array, and perform mapping processing according to a first shear wave velocity at the at least one mapping acquisition point and an attenuation parameter corresponding to the first shear wave velocity;

[0167] The second drawing unit is configured to perform drawing processing according to the second shear wave velocity at at least one drawing acquisition point and an attenuation parameter corresponding to the second shear wave velocity.

[0168] Those skilled in the art will clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently perform or cooperate with other components to perform specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0169] See also Figure 4 , Figure 4 FIG. 1 shows a schematic diagram of the structure of another imaging device based on short-period dense array urban background noise provided by an embodiment of the present application. Figure 4 As shown, the imaging device 400 based on short-period dense array urban background noise may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 and at least one communication bus 402 .

[0170] The communication bus 402 may be used to implement connection and communication among the above components.

[0171] The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0172] The network interface 404 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, and the like.

[0173] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect the various parts of the entire electronic device 400, and performs various functions and processes data of the routing device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 401 can integrate one or a combination of CPU, GPU, and modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor 401, but may be implemented separately through a chip.

[0174] Among them, the memory 405 may include RAM and may also include ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be optionally at least one storage device located away from the aforementioned processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an imaging application based on short-period dense array urban background noise.

[0175] Specifically, the processor 401 may be configured to call an imaging application based on short-period dense array urban background noise stored in the memory 405 and perform the following operations:

[0176] Obtain the background noise signal corresponding to the short-period dense array and pre-process the background noise signal;

[0177] Perform frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal;

[0178] The preset initial model is iteratively inverted based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained based on the minimum fitting error between the dispersion curve of the preset initial model in the iterative inversion calculation and the observed value. The number of layers in the preset initial model is n, and the minimum thin layer thickness is h, where n and h are both positive integers.

[0179] The plot is processed based on the shear wave velocity and attenuation parameters corresponding to the background noise signal.

[0180] In some possible embodiments, preprocessing the background noise signal includes:

[0181] Determine at least m acquisition point locations based on a short-period dense array, and classify and process background noise signals based on the at least m acquisition point locations; where m is a positive integer greater than or equal to 5;

[0182] Performing signal preprocessing on the background noise signal after classification processing;

[0183] The background noise signal after signal preprocessing is subjected to bandpass filtering.

[0184] In some possible embodiments, frequency domain correlation calculation is performed on the processed background noise signal to obtain Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal, including:

[0185] Determine a target collection point position from at least m collection point positions; wherein the target collection point position is any one of the at least m collection point positions;

[0186] Screening out background noise signals corresponding to a first set of collection point positions that are within a preset distance from a target collection point position from the filtered background noise signals; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point position;

[0187] Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency;

[0188] A first frequency domain parameter at a target acquisition point position at a preset frequency is fitted to obtain a first Rayleigh wave phase velocity at the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

[0189] In some possible embodiments, performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain the first frequency domain parameter of the target acquisition point position at a preset frequency includes:

[0190] Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method;

[0191] Based on the signal segments corresponding to the target acquisition point positions and the signal segments corresponding to the first acquisition point position set, obtaining frequency domain parameters corresponding to each signal segment at the target acquisition point position at a preset frequency;

[0192] The frequency domain parameters corresponding to each signal segment are superimposed and calculated to obtain the first frequency domain parameters of the target acquisition point position at the preset frequency.

[0193] In some possible embodiments, after fitting the first frequency domain parameters at the target acquisition point at a preset frequency to obtain the first Rayleigh wave phase velocity at the target acquisition point and the attenuation parameter corresponding to the first Rayleigh wave phase velocity, the method further includes:

[0194] Dividing intervals within a preset distance from the target collection point location according to a preset angle, and determining a second collection point location set corresponding to each divided interval from the first collection point location set; wherein the second collection point location set includes at least s collection point locations; wherein s is a positive integer greater than or equal to 5;

[0195] Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency;

[0196] The second frequency domain parameters at the target acquisition point position at the preset frequency are fitted to obtain the second Rayleigh wave phase velocity at the target acquisition point position and the attenuation parameter corresponding to the second Rayleigh wave phase velocity.

[0197] In some possible embodiments, iterative inversion training is performed on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained based on the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation, including:

[0198] Performing an inversion calculation on the first Rayleigh wave phase velocity at the target acquisition point, and iteratively training a preset initial model based on the calculated first Rayleigh wave phase velocity at the target acquisition point until the iterative error of the preset initial model is within a preset error range;

[0199] Input the first Rayleigh wave phase velocity at the target acquisition point into the trained preset initial model to obtain the first shear wave velocity at the target acquisition point;

[0200] The second Rayleigh wave phase velocity at the target acquisition point is input into the trained preset initial model to obtain the second shear wave velocity at the target acquisition point.

[0201] In some possible embodiments, performing mapping processing according to the shear wave velocity and attenuation parameter corresponding to the background noise signal includes:

[0202] Determining at least one mapping acquisition point position based on a short-period dense array, and performing mapping processing according to a first shear wave velocity at the at least one mapping acquisition point position and an attenuation parameter corresponding to the first shear wave velocity;

[0203] The mapping process is performed according to the second shear wave velocity at the at least one mapping acquisition point and the attenuation parameter corresponding to the second shear wave velocity.

[0204] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0205] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0206] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0207] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0208] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0209] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0210] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0211] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0212] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An imaging method for urban background noise based on a short-period dense array, characterized by: include: Acquiring a background noise signal corresponding to a short-period dense array, and preprocessing the background noise signal; Performing frequency domain correlation calculation on the processed background noise signal to obtain Rayleigh wave phase velocity and attenuation parameters corresponding to the background noise signal; Iterative inversion training is performed on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and the shear wave velocity corresponding to the background noise signal is obtained based on the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation; wherein the number of layers of the preset initial model is n, the minimum thin layer thickness is h, and both n and h are positive integers; Performing drawing processing according to the shear wave velocity corresponding to the background noise signal and the attenuation parameter; The performing of frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameter corresponding to the background noise signal includes: Determine at least m acquisition point positions based on the short-period dense array, and determine a target acquisition point position from the at least m acquisition point positions; wherein the target acquisition point position is any one of the at least m acquisition point positions, and m is a positive integer greater than or equal to 5; Screening out background noise signals corresponding to a first set of collection point positions that are within a preset distance from the target collection point position from the background noise signals after bandpass filtering; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point position; Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency; A fitting process is performed on the first frequency domain parameters of the target acquisition point position at a preset frequency to obtain a first Rayleigh wave phase velocity of the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

2. The method according to claim 1, characterized in that The preprocessing of the background noise signal includes: Classify and process the background noise signal according to the positions of the at least m acquisition points; Performing signal preprocessing on the background noise signal after the classification process; The background noise signal after signal preprocessing is subjected to bandpass filtering.

3. The method according to claim 1, characterized in that The performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency includes: Cutting the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set into at least two signal segments according to a preset cutting method; Based on the signal segment corresponding to the target acquisition point position and the signal segment corresponding to the first acquisition point position set, obtaining a frequency domain parameter corresponding to each of the signal segments at the target acquisition point position at a preset frequency; The frequency domain parameters corresponding to each of the signal segments are superimposed and calculated to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency.

4. The method according to claim 1, wherein After fitting the first frequency domain parameters of the target acquisition point at the preset frequency to obtain the first Rayleigh wave phase velocity of the target acquisition point and the attenuation parameter corresponding to the first Rayleigh wave phase velocity, the method further includes: Dividing intervals within a preset distance from the target collection point location according to a preset angle, and determining a second collection point location set corresponding to each divided interval from the first collection point location set; wherein the second collection point location set includes at least s collection point locations; wherein s is a positive integer greater than or equal to 5; Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the second acquisition point position set to obtain a second frequency domain parameter of the target acquisition point position at a preset frequency; A fitting process is performed on the second frequency domain parameters of the target acquisition point position at a preset frequency to obtain a second Rayleigh wave phase velocity of the target acquisition point position and an attenuation parameter corresponding to the second Rayleigh wave phase velocity.

5. The method according to claim 4, characterized in that The method of iteratively inverting a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and obtaining the shear wave velocity corresponding to the background noise signal according to the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation, comprises: Performing an inversion calculation on the first Rayleigh wave phase velocity at the target acquisition point, and iteratively training a preset initial model based on the calculated first Rayleigh wave phase velocity at the target acquisition point until an iterative error of the preset initial model is within a preset error range; Inputting the first Rayleigh wave phase velocity at the target acquisition point into the trained preset initial model to obtain the first shear wave velocity at the target acquisition point; The second Rayleigh wave phase velocity at the target acquisition point is input into the trained preset initial model to obtain the second shear wave velocity at the target acquisition point.

6. The method according to claim 5, characterized in that The drawing process according to the shear wave velocity corresponding to the background noise signal and the attenuation parameter includes: Determining at least one mapping acquisition point position based on the short-period dense array, and performing mapping processing according to a first shear wave velocity at the at least one mapping acquisition point position and an attenuation parameter corresponding to the first shear wave velocity; Mapping processing is performed according to the second shear wave velocity at the at least one mapping acquisition point and an attenuation parameter corresponding to the second shear wave velocity.

7. An imaging device based on short-period dense array urban background noise, characterized in that: include: A preprocessing module is used to obtain a background noise signal corresponding to a short-period dense array and preprocess the background noise signal; a signal calculation module, configured to perform frequency domain correlation calculation on the processed background noise signal to obtain a Rayleigh wave phase velocity and an attenuation parameter corresponding to the background noise signal; a signal processing module, configured to perform iterative inversion training on a preset initial model based on the Rayleigh wave phase velocity corresponding to the background noise signal, and obtain the shear wave velocity corresponding to the background noise signal based on the minimum fitting error between the dispersion curve of the preset initial model and the observed value in the iterative inversion calculation; wherein the number of layers of the preset initial model is n, the minimum thin layer thickness is h, and both n and h are positive integers; a drawing module, configured to perform drawing processing according to the shear wave velocity corresponding to the background noise signal and the attenuation parameter; The performing of frequency domain correlation calculation on the processed background noise signal to obtain the Rayleigh wave phase velocity and attenuation parameter corresponding to the background noise signal includes: Determine at least m acquisition point positions based on the short-period dense array, and determine a target acquisition point position from the at least m acquisition point positions; wherein the target acquisition point position is any one of the at least m acquisition point positions, and m is a positive integer greater than or equal to 5; Screening out background noise signals corresponding to a first set of collection point positions that are within a preset distance from the target collection point position from the background noise signals after bandpass filtering; wherein the first set of collection point positions includes at least m collection point positions, and the recording time of the background noise signals corresponding to the first set of collection point positions is consistent with the recording time of the background noise signals corresponding to the target collection point position; Performing frequency domain correlation calculation based on the background noise signal corresponding to the target acquisition point position and the background noise signal corresponding to the first acquisition point position set to obtain a first frequency domain parameter of the target acquisition point position at a preset frequency; A fitting process is performed on the first frequency domain parameters of the target acquisition point position at a preset frequency to obtain a first Rayleigh wave phase velocity of the target acquisition point position and an attenuation parameter corresponding to the first Rayleigh wave phase velocity.

8. An imaging device based on short-period dense array urban background noise, characterized in that: including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 6.

Images