Urban environment noise source screening method and device, medium and equipment

Abstract

The invention discloses an urban environment noise source screening method and device, a medium and equipment, and the method comprises the steps: obtaining a plurality of background noise waveforms of a to-be-measured work area, and carrying out the slicing of the plurality of background noise waveforms according to a preset time window, and obtaining a plurality of sub-window waveforms; performing two-dimensional Fourier transform on each sub-window waveform to obtain data of a plurality of first frequency-wavenumber domains, and performing normalization processing on the data of each first frequency-wavenumber domain to obtain data of a plurality of second frequency-wavenumber domains; and performing multi-stage clustering processing on the data of the plurality of second frequency-wave number domains to obtain a plurality of surface wave windows with coherent characteristics and different modes. According to the method, the spatial coherence of array data is fully considered from the angle of a frequency-wave number domain, and multi-stage clustering processing is carried out on the data, so that refined representation and screening are carried out on a complex noise source in an urban environment, and a plurality of surface wave windows with coherent characteristics and different modes are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and particularly to a method, device, medium and equipment for screening urban environmental noise sources. Background Art

[0002] The earth's surface is subjected to vibrations generated by natural phenomena and human activities. The wave fields generated by these vibrations constitute the background noise signals in continuous seismic records. By analyzing these background noises, the geological structure underground in the city can be understood, providing a basis for urban construction planning. It can also monitor the time-lapse changes of underground media, such as monitoring the changes in groundwater and the dynamic changes of oil and gas reservoirs, which is of great significance for resource exploration and development.

[0003] The background noise generated by human activities is mainly concentrated in cities. The existing methods for processing the background noise data of cities in seismic records introduce a noise source screening strategy during the processing to screen coherent noise sources, and use the screened noise sources for underground space exploration and energy and mineral exploration. The existing noise source screening methods are mainly frequency-domain screening methods based on the statistical characteristics of the diffuse wave field. The seismic waveform sequence is divided into multiple non-overlapping short-time windows, and the frequency-domain data of all sub-windows are statistically analyzed. Three dimensionless physical quantities are calculated, the residuals between the three physical quantities and their target objects are quantified, and the P value is calculated. Based on the P value, the diffuse waveforms are screened.

[0004] Since the frequency-domain screening method based on the statistical characteristics of the diffuse wave field mainly relies on the waveforms recorded by a single station and cannot characterize the spatial coherence of the background noise field, and in the urban environment, due to the inherent coherence of the ballistic waves that dominate traffic noise, therefore, the frequency-domain screening method based on the statistical characteristics of the diffuse wave field is not applicable to the screening of noise sources in complex urban environments, and a noise source screening method applicable to urban environments is needed. Summary of the Invention

[0005] In view of this, the present invention provides a method, device, medium and equipment for screening urban environmental noise sources, mainly aiming to solve the problem that the existing noise source screening methods are not applicable to complex urban environments.

[0006] According to one aspect of the present application, a method for screening urban environmental noise sources is provided, and the method includes:

[0007] Obtain multi-channel background noise waveforms of the work area to be measured, and perform slicing processing on the multi-channel background noise waveforms according to a preset time window to obtain a plurality of sub-window waveforms;

[0008] Perform two-dimensional Fourier transform on each of the sub-window waveforms to obtain data in multiple first frequency-wavenumber domains, and perform normalization processing on the data in each of the first frequency-wavenumber domains respectively based on the median truncation method to obtain data in multiple second frequency-wavenumber domains;

[0009] Perform multi-stage clustering processing on the data in multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent characteristics and different modes.

[0010] Optionally, the formula adopted by the median truncation method:

[0011]

[0012] Wherein, d′ and d respectively represent the data in the frequency-wavenumber domain before and after median truncation normalization; μ and σ respectively represent the data median and standard deviation; n represents a constant.

[0013] Optionally, the performing multi-stage clustering processing on the data in multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent characteristics and different modes includes:

[0014] Perform the first-stage clustering processing on the data in the multiple second frequency-wavenumber domains, and determine the frequency band range of the waveforms with coherent characteristics according to the clustering result of the first stage;

[0015] Perform filtering processing on the data in the multiple second frequency-wavenumber domains based on the frequency band range, perform the second-stage clustering processing on the filtered frequency-wavenumber domain data, and obtain multiple surface wave windows with coherent characteristics according to the clustering result of the second stage;

[0016] Perform the third-stage clustering processing on the multiple surface waves with coherent modes, and obtain multiple surface wave windows with coherent characteristics and different modes according to the clustering result of the third stage.

[0017] Optionally, the method for screening urban environmental noise sources further includes:

[0018] Perform the first-stage clustering processing, the second-stage clustering processing, and the third-stage clustering processing based on different preset clustering models;

[0019] Each of the preset clustering models includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.

[0020] Optionally, after performing multi-stage clustering processing on the data in multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent characteristics and different modes, the method for screening urban environmental noise sources further includes:

[0021] Perform seismic interferometry on each surface wave window with coherent characteristics and different modes to obtain a virtual shot gather corresponding to each surface wave window;

[0022] Superimpose multiple virtual shot gathers to obtain superimposed virtual shot gather data;

[0023] Use the phase shift method to image the superimposed virtual shot gather data to obtain a dispersion energy map.

[0024] Optionally, before obtaining the multi-channel background noise waveforms of the work area to be measured, the screening method for urban environmental noise sources further includes:

[0025] Obtain the initial background noise data of different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format;

[0026] Successively perform mean removal processing and detrending processing on the noise data with the preset format to obtain the multi-channel background noise waveforms of the work area to be measured.

[0027] According to another aspect of the present application, there is provided a screening device for urban environmental noise sources, including:

[0028] A slicing module, configured to obtain the multi-channel background noise waveforms of the work area to be measured, and perform slicing processing on the multi-channel background noise waveforms according to a preset time window to obtain a plurality of sub-window waveforms;

[0029] A conversion module, configured to perform two-dimensional Fourier transform on each of the sub-window waveforms to obtain a plurality of first frequency-wavenumber domain data, and perform normalization processing on each of the first frequency-wavenumber domain data based on the median truncation method to obtain a plurality of second frequency-wavenumber domain data;

[0030] A clustering module, configured to perform multi-stage clustering processing on a plurality of second frequency-wavenumber domain data to obtain a plurality of surface wave windows with coherent characteristics and different modes.

[0031] Optionally, the formula adopted by the median truncation method:

[0032]

[0033] Wherein, d′ and d respectively represent the frequency-wavenumber domain data before and after median truncation normalization; μ and σ respectively represent the data median and standard deviation; n represents a constant.

[0034] Optionally, the clustering module is further configured to:

[0035] Perform the first-stage clustering processing on the plurality of second frequency-wavenumber domain data, and determine the frequency band range of the waveforms with coherent characteristics according to the clustering results of the first stage;

[0036] Filter the data in the multiple second frequency - wavenumber domains based on the frequency band range, perform a second - stage clustering process on the filtered frequency - wavenumber domain data, and obtain multiple surface wave windows with coherent characteristics according to the results of the second - stage clustering.

[0037] Perform a third - stage clustering process on the multiple surface waves with coherent modes, and obtain multiple surface wave windows with coherent characteristics and different modes according to the results of the third - stage clustering.

[0038] Optionally, the clustering module is further configured to:

[0039] Perform the first - stage clustering process, the second - stage clustering process, and the third - stage clustering process based on different preset clustering models.

[0040] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.

[0041] Optionally, the screening device for urban environmental noise sources further includes:

[0042] A dispersion energy map generation module, configured to perform seismic interference on each surface wave window with coherent characteristics and different modes to obtain a virtual shot gather corresponding to each surface wave window; superimpose the multiple virtual shot gathers to obtain superimposed virtual shot gather data; and use the phase - shift method to image the superimposed virtual shot gather data to obtain a dispersion energy map.

[0043] Optionally, the screening device for urban environmental noise sources further includes:

[0044] An original data acquisition module, configured to acquire initial background noise data at different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format.

[0045] A pre - processing module, configured to perform mean - removal processing and detrending processing on the noise data with the preset format in sequence to obtain multi - trace background noise waveforms in the work area to be measured.

[0046] According to another aspect of the present application, there is provided a computer device, including: a processor, a memory, a communication interface, and a communication bus, where the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0047] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the above - mentioned screening method for urban environmental noise sources.

[0048] With the above technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:

[0049] A method, device, equipment and medium for screening urban environmental noise sources provided by the present application perform two-dimensional Fourier transform on the waveforms of sub-windows to obtain data in the frequency-wavenumber domain, perform normalization processing on the data in the frequency-wavenumber domain by the median truncation method, and perform multi-stage clustering processing on the normalized frequency-wavenumber data to obtain multiple surface wave windows with coherent characteristics and different modes. From the perspective of the frequency-wavenumber domain, the spatial coherence of array data is fully considered, and multi-stage clustering processing is performed on the data, so as to perform refined characterization and screening on complex noise sources in the urban environment, obtain multiple surface wave windows with coherent characteristics and different modes, and improve the quality of passive source surface wave imaging.

[0050] The above description is only an overview of the technical solutions of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0052] Figure 1 Shows a flowchart of a method for screening urban environmental noise sources provided by an embodiment of the present application;

[0053] Figure 2 Shows the multi-channel background noise waveforms of a method for screening urban environmental noise sources provided by an embodiment of the present application;

[0054] Figure 3 Shows another flowchart of a method for screening urban environmental noise sources provided by an embodiment of the present application;

[0055] Figure 4 Shows a structural block diagram of another device for screening urban environmental noise sources provided by an embodiment of the present application;

[0056] Figure 5 Shows a structural schematic diagram of a computer device provided by an embodiment of the present invention.

[0057] Among them,

[0058] Figure 4Chinese: 402 - Slicing module; 404 - Conversion module; 406 - Clustering module;

[0059] Figure 5 Chinese: 502 - Processor; 504 - Communication interface; 506 - Memory; 508 - Communication bus; 510 - Program. Detailed implementation manner

[0060] In the following, the present invention will be described in detail with reference to the accompanying drawings and in combination with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0061] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in combination with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manner, structure, features and their effects of the application according to the present invention. In the following description, different "one embodiment" or "embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0062] Aiming at the problem that the existing noise source screening is not applicable to the urban environment at present, the embodiment of the present application provides a method for screening urban environmental noise sources, as Figure 1 shown, the method includes:

[0063] 102: Obtain multi-channel background noise waveforms of the work area to be measured, perform slicing processing on the multi-channel background noise waveforms according to a preset time window, and obtain a plurality of sub-window waveforms;

[0064] 104: Perform two-dimensional Fourier transform on each sub-window waveform to obtain a plurality of first frequency-wavenumber domain data, and perform normalization processing on each first frequency-wavenumber domain data based on the median truncation method to obtain a plurality of second frequency-wavenumber domain data;

[0065] 106: Perform multi-stage clustering processing on the plurality of second frequency-wavenumber domain data to obtain a plurality of surface wave windows with coherent features and different modes.

[0066] Specifically, obtain multi-channel initial background noise data of different measurement points in the work area to be measured. Since the equipment is different, the formats of the initial background noise data are also different, such as data formats like.sac and.mat. Convert the initial background noise data into noise data with a preset format, and perform preprocessing on the multi-channel noise data with the preset format, such as mean removal processing and detrending processing, etc., to obtain multi-channel background noise waveforms of the work area to be measured, as Figure 2 shown. Then slice the continuous multi-channel background noise waveforms according to a preset time window to obtain several short-time sub-window waveforms.

[0067] In order to capture the spatial coherence characteristics of the background noise field, a two-dimensional Fourier transform (frequency-wavenumber transform) is performed on the sliced sub-window waveforms to obtain the data in the frequency-wavenumber domain. By converting the signal to the frequency-wavenumber domain, the two-dimensional Fourier transform can simultaneously analyze the temporal and spatial characteristics of the signal, thereby capturing the spatial coherence characteristics of the background noise field, highlighting the propagation law of waves in space, enhancing the understanding of the wave field, and at the same time improving the detection ability of the array for weak signals or complex wave fields. Therefore, performing a two-dimensional Fourier transform on the sub-window waveforms can highlight the coherent characteristics of the signal in space and further improve the accuracy of noise source extraction.

[0068] Since there may be outliers in the original frequency-wavenumber domain data, which can mask the weaker coherent signals, the median truncation method is used to normalize the frequency-wavenumber domain data to avoid the influence of outliers. The normalized frequency-wavenumber domain data is subjected to multi-stage clustering processing. Through multi-stage clustering processing, the clustering results of complex noise sources in the urban environment are continuously refined to screen out noise segments with coherent characteristics and different modes.

[0069] The present application provides a method for screening urban environmental noise sources. Compared with the prior art, a two-dimensional Fourier transform is performed on the waveforms of the sub-windows to obtain the data in the frequency-wavenumber domain. The frequency-wavenumber domain data is normalized by the median truncation method, and the normalized frequency-wavenumber domain data is subjected to multi-stage clustering processing to obtain multiple surface wave windows with different modes and coherent characteristics. From the perspective of the frequency-wavenumber domain, the spatial coherence of the array data is fully considered, and the data is subjected to multi-stage clustering processing to finely characterize and screen complex noise sources in the urban environment, obtain multiple surface wave windows with coherent characteristics and different modes, and improve the quality of passive source surface wave imaging.

[0070] In one embodiment, the formula adopted by the median truncation method is:

[0071]

[0072] where d′ and d respectively represent the frequency-wavenumber domain data before and after median truncation normalization; μ and σ respectively represent the data median and standard deviation; n represents a constant.

[0073] Specifically, there may be outliers in the obtained original frequency-wavenumber domain data, which can mask the weaker coherent signals. Therefore, in order to highlight the effective information in the data, the median truncation algorithm is used to perform median truncation on the original frequency-wavenumber domain data, which can make the data distribution within a reasonable range, avoid the influence of outliers, and further highlight the useful information.

[0074] In another embodiment of the present invention, for further limitation and illustration, as Figure 3 shown, multi-stage clustering processing is performed on multiple second frequency-wavenumber domain data to obtain multiple surface wave windows with coherent characteristics and different modes, including:

[0075] 302: Perform first-stage clustering processing on multiple second frequency-wavenumber domain data, and determine the frequency band range of waveforms with coherent characteristics according to the clustering results of the first stage;

[0076] 304: Perform filtering processing on multiple second frequency-wavenumber domain data based on the frequency band range, perform second-stage clustering processing on the filtered frequency-wavenumber domain data, and obtain multiple surface wave windows with coherent characteristics according to the clustering results of the second stage;

[0077] 306: Perform third-stage clustering processing on multiple surface wave windows with coherent mode characteristics, and obtain multiple surface wave windows with coherent characteristics and different modes according to the clustering results of the third stage.

[0078] Specifically, the multi-stage deep clustering processing takes the deep embedded clustering algorithm as the core, and its core process is divided into three steps: (a) The first-stage clustering, which is also the primary clustering stage: Characterize the noise sources in the wide frequency band range and identify the time distribution patterns of typical noise sources. In this stage, by distinguishing the effective frequency-wavenumber domain windows of surface waves, separating the surface wave windows with coherent characteristics and the noise windows with non-coherent characteristics, and determining the frequency band range of waveforms with coherent characteristics. (b) The second-stage clustering, which is also the secondary clustering stage: First, perform band-pass filtering on the frequency-wavenumber domain data through the frequency band range of waveforms with coherent characteristics determined in the primary clustering stage, perform second-stage clustering processing on the filtered data, and use the deep embedded clustering technology to extract coherent signals, and screen out the noise segments suitable for background noise imaging in the urban environment for specific frequency-wavenumber domain windows. (c) The third-stage clustering, which is also the mode-specific clustering stage: Further subdivide the coherent signals obtained by the secondary clustering, and further subdivide according to the mode of the signals, such as the fundamental mode, higher-order mode, etc. The multi-stage deep clustering processing can enhance the understanding of noise sources in complex environments, help understand the generation mechanism of noise sources, and can be used across scenarios. At the same time, by using the multi-stage deep clustering algorithm, through continuous refinement of the clustering results of noise sources, noise segments containing different modes can be screened out.

[0079] In one embodiment, the first-stage clustering processing, the second-stage clustering processing, and the third-stage clustering processing are performed based on different preset clustering models;

[0080] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.

[0081] Specifically, training datasets at different stages are obtained, and the clustering models corresponding to different stages are trained respectively to obtain multiple trained clustering models. The data corresponding to each stage is respectively input into the trained clustering model corresponding to this stage to obtain the output data corresponding to this stage. Through multiple clusterings, the noise sources in the urban environment are further segmented, and finally surface wave windows with coherent features and different modes are obtained.

[0082] In one embodiment, after performing multi-stage clustering processing on multiple second frequency-wave number domain data to obtain multiple surface wave windows with coherent features and different modes, the method for screening urban environmental noise sources further includes:

[0083] Performing seismic interference on each surface wave window with coherent features and different modes to obtain a virtual shot gather corresponding to each surface wave window;

[0084] Superposing multiple virtual shot gathers to obtain superposed virtual shot gather data;

[0085] Using the phase shift method to image the superposed virtual shot gather data to obtain a dispersion energy map.

[0086] Specifically, for the surface wave windows with coherent features and different modes screened by different clustering stages, dispersion energy imaging is performed. For example, an interference-based imaging method is used: performing seismic interference on the surface wave windows with coherent features and different modes to obtain a virtual shot gather corresponding to the noise segment. For example, any one of the methods of cross-correlation, cross-coherence, or deconvolution is used to perform seismic interference to obtain a virtual shot gather; then the virtual shot gathers are superposed, for example, using a signal-to-noise ratio weighted superposition method or a phase weighted superposition method for superposition; finally, using the phase shift method to image the superposed virtual shot gather data to obtain a high-quality dispersion energy map, which can reduce the interference of artifacts to a certain extent and highlight the information of higher-order surface waves at the same time.

[0087] Furthermore, as an implementation of the above Figure 1 shown method, an embodiment of the present invention provides a device for screening urban environmental noise sources, as Figure 4 shown, the device includes:

[0088] A slicing module 402, configured to obtain multi-channel background noise waveforms of the work area to be measured, and perform slicing processing on the multi-channel background noise waveforms according to a preset time window to obtain multiple sub-window waveforms;

[0089] A conversion module 404 is configured to perform two-dimensional Fourier transform on the waveform of each sub-window to obtain data in multiple first frequency-wavenumber domains, and perform normalization processing on the data in each first frequency-wavenumber domain respectively based on a median truncation method to obtain data in multiple second frequency-wavenumber domains;

[0090] A clustering module 406 is configured to perform multi-stage clustering processing on the data in multiple second frequency-wavenumber domains to obtain surface wave windows with coherent features and different modes.

[0091] The present application provides a screening device for urban environmental noise sources. Compared with the prior art, it performs two-dimensional Fourier transform on the waveform of the sub-window to obtain data in the frequency-wavenumber domain, performs normalization processing on the data in the frequency-wavenumber domain by the median truncation method, and performs multi-stage clustering processing on the normalized data in the frequency-wavenumber domain to obtain multiple surface wave windows with coherent features and different modes. From the perspective of the frequency-wavenumber domain, it fully considers the spatial coherence of the array data, and performs multi-stage clustering processing on the data to finely characterize and screen complex noise sources in the urban environment, obtain multiple surface wave windows with coherent features and different modes, and improve the quality of passive source surface wave imaging.

[0092] In one embodiment, the formula adopted by the median truncation method is:

[0093]

[0094] where d′ and d respectively represent the data in the frequency-wavenumber domain before and after median truncation normalization; μ and σ respectively represent the median and standard deviation of the data; and n represents a constant.

[0095] In one embodiment, the clustering module is further configured to:

[0096] Perform first-stage clustering processing on the data in multiple second frequency-wavenumber domains, and determine the frequency band range of the waveform with coherent features according to the clustering result of the first stage;

[0097] Perform filtering processing on the data in multiple second frequency-wavenumber domains based on the frequency band range, perform second-stage clustering processing on the filtered data in the frequency-wavenumber domain, and obtain multiple surface wave windows with coherent features according to the clustering result of the second stage;

[0098] Perform third-stage clustering processing on multiple surface wave windows with coherent features, and obtain multiple surface wave windows with coherent features and different modes according to the clustering result of the third stage.

[0099] In one embodiment, the clustering module is further configured to:

[0100] Perform clustering processing in the first stage, the second stage, and the third stage based on different preset clustering models;

[0101] Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.

[0102] In one embodiment, the screening device for urban environmental noise sources further includes:

[0103] A dispersion energy map generation module, configured to perform seismic interference on each surface wave window with coherent features and different modes to obtain a virtual shot gather corresponding to each surface wave window; superimpose multiple virtual shot gathers to obtain superimposed virtual shot gather data; and use the phase shift method to image the superimposed virtual shot gather data to obtain a dispersion energy map.

[0104] In one embodiment, the screening device for urban environmental noise sources further includes:

[0105] A raw data acquisition module, configured to acquire initial background noise data of different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format;

[0106] A preprocessing module, configured to perform mean removal processing and detrending processing on the noise data with a preset format in sequence to obtain multi-channel background noise waveforms of the work area to be measured.

[0107] According to an embodiment of the present invention, there is provided a storage medium storing at least one executable instruction, and the computer executable instruction can execute the screening method for urban environmental noise sources in any of the above method embodiments.

[0108] Figure 5 The structural schematic diagram of a computer device provided according to an embodiment of the present invention is shown. The specific implementation of the computer device in the specific embodiments of the present invention is not limited.

[0109] As Figure 5 shown, the computer device may include: a processor 502, a communication interface 504, a memory 506, and a communication bus 508.

[0110] Among them: the processor 502, the communication interface 504, and the memory 506 communicate with each other through the communication bus 508.

[0111] The communication interface 504 is used to communicate with network elements of other devices such as clients or other servers.

[0112] A processor 502 is configured to execute a program 510, and specifically can execute relevant steps in the embodiments of the method for screening urban environmental noise sources described above.

[0113] Specifically, the program 510 may include program code, and the program code includes computer operation instructions.

[0114] The processor 502 may be a central processing unit (CPU), or a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computer device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0115] A memory 506 is configured to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0116] The program 510 is specifically configured to cause the processor 502 to perform the following operations:

[0117] Obtain multi-channel background noise waveforms of a work area to be measured, slice the multi-channel background noise waveforms according to a preset time window to obtain a plurality of sub-window waveforms;

[0118] Perform two-dimensional Fourier transform on each sub-window waveform to obtain a plurality of data in the first frequency-wavenumber domain, and perform normalization processing on each data in the first frequency-wavenumber domain based on the median truncation method to obtain a plurality of data in the second frequency-wavenumber domain;

[0119] Perform multi-stage clustering processing on the plurality of data in the second frequency-wavenumber domain to obtain a plurality of surface wave windows with coherent characteristics and different modes.

[0120] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. In one embodiment, they can be implemented by program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. Thus, the present invention is not limited to any specific combination of hardware and software.

[0121] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions within the essence and protection scope of the present application, and such modifications or equivalent substitutions should also be regarded as falling within the protection scope of the present application.

Claims

1. A screening method for urban environmental noise sources, characterized in that Including: Obtain multi-channel background noise waveforms of the work area to be measured, and slice the multi-channel background noise waveforms according to a preset time window to obtain a plurality of sub-window waveforms; Perform two-dimensional Fourier transform on each of the sub-window waveforms to obtain a plurality of data in the first frequency-wavenumber domain, and perform normalization processing on the data in each of the first frequency-wavenumber domains respectively based on the median truncation method to obtain a plurality of data in the second frequency-wavenumber domain; Perform multi-stage clustering processing on the plurality of data in the second frequency-wavenumber domain to obtain a plurality of surface wave windows with coherent characteristics and different modes.

2. The screening method for urban environmental noise sources according to claim 1, characterized in that, The formula adopted by the median truncation method is: Where d′ and d respectively represent the data in the frequency-wavenumber domain before and after median truncation normalization; μ and σ respectively represent the data median and standard deviation; n represents a constant.

3. The screening method for urban environmental noise sources according to claim 1, characterized in that The performing multi-stage clustering processing on the plurality of data in the second frequency-wavenumber domain to obtain a plurality of surface wave windows with coherent characteristics and different modes includes: Perform the first-stage clustering processing on the plurality of data in the second frequency-wavenumber domain, and determine the frequency band range of the waveforms with coherent characteristics according to the clustering result of the first stage; Perform filtering processing on the plurality of data in the second frequency-wavenumber domain based on the frequency band range, perform the second-stage clustering processing on the filtered data in the frequency-wavenumber domain, and obtain a plurality of surface wave windows with coherent characteristics according to the clustering result of the second stage; Perform the third-stage clustering processing on the plurality of surface waves with coherent modes, and obtain a plurality of surface wave windows with coherent characteristics and different modes according to the clustering result of the third stage.

4. The screening method for urban environmental noise sources according to claim 3, characterized in that, The method for screening urban environmental noise sources further includes: Perform the first-stage clustering processing, the second-stage clustering processing, and the third-stage clustering processing based on different preset clustering models; Each preset clustering model includes a feature extraction module, an initial clustering module, a deep clustering optimization module, a cluster selection module, and an output module connected in sequence.

5. The screening method for urban environmental noise sources according to any one of claims 1-4, characterized in that, After performing multi-stage clustering processing on the plurality of data in the second frequency-wavenumber domain to obtain a plurality of surface wave windows with coherent characteristics and different modes, the method for screening urban environmental noise sources further includes: Perform seismic interference on each surface wave window with coherent characteristics and different modes to obtain a virtual shot gather corresponding to each surface wave window; Superimpose the plurality of virtual shot gathers to obtain superimposed virtual shot gather data; Use the phase shift method to image the superimposed virtual shot gather data to obtain a dispersion energy map.

6. The screening method for urban environmental noise sources according to any one of claims 1-4, characterized in that Before obtaining the multi-channel background noise waveforms of the work area to be measured, the method for screening urban environmental noise sources further includes: Obtain the initial background noise data of different measurement points in the work area to be measured, and convert the initial background noise data into noise data with a preset format; Perform mean removal processing and detrending processing on the noise data with the preset format in sequence to obtain multi-channel background noise waveforms of the work area to be measured.

7. A screening device for urban environmental noise sources, characterized in that, Including: A slicing module, configured to obtain multi-channel background noise waveforms of the work area to be measured, and slice the multi-channel background noise waveforms according to a preset time window to obtain a plurality of sub-window waveforms; A conversion module for performing two-dimensional Fourier transform on each of the sub-window waveforms to obtain data in multiple first frequency-wavenumber domains, and performing normalization processing on the data in each of the first frequency-wavenumber domains based on a median truncation method to obtain data in multiple second frequency-wavenumber domains; A clustering module for performing multi-stage clustering processing on the data in multiple second frequency-wavenumber domains to obtain multiple surface wave windows with coherent features and different modes.

8. The screening device for urban environmental noise sources according to claim 7, wherein, The clustering module is further configured to: Perform first-stage clustering processing on the data in the multiple second frequency-wavenumber domains, and determine the frequency band range of the waveforms with coherent features according to the clustering result of the first stage; Perform filtering processing on the data in the multiple second frequency-wavenumber domains based on the frequency band range, perform second-stage clustering processing on the filtered frequency-wavenumber domain data, and obtain multiple surface wave windows with coherent features according to the clustering result of the second stage; Perform third-stage clustering processing on the multiple surface waves with coherent modes, and obtain multiple surface wave windows with coherent features and different modes according to the clustering result of the third stage.

9. A storage medium storing at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the method for screening urban environmental noise sources according to any one of claims 1-6.

10. A computer device, comprising: A processor, a memory, a communication interface, and a communication bus, the processor, the memory, and the communication interface completing communication with each other through the communication bus; The memory is used for storing at least one executable instruction, the executable instruction causing the processor to perform operations corresponding to the method for screening urban environmental noise sources according to any one of claims 1-6.

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