Train noise suppression method and device, electronic equipment and medium

Abstract

The invention belongs to the technical field of petroleum seismic exploration, and discloses a train noise suppression method and device, electronic equipment and a medium, and the method comprises the steps: firstly reading and analyzing seismic data, recognizing train noise in the seismic data, then carrying out the decomposition of the seismic data through variational mode decomposition, obtaining a mode component with a specific frequency band, and carrying out the recognition of the train noise in the seismic data; setting a difference threshold value, determining a train noise modal component, and finally filtering the train noise modal component to suppress the train noise. According to the invention, accurate identification and effective suppression of train noise are realized, and seismic data quality is improved. The device based on the train noise suppression method, the electronic equipment and the medium are convenient to store, train noise can be effectively suppressed, and the device and the electronic equipment are suitable for suppressing train noise in seismic data in urban areas.

Description

Technical Field

[0001] This invention belongs to the field of petroleum seismic exploration technology and relates to a method for suppressing train noise, specifically a method, device, electronic equipment and medium for suppressing train noise. Background Technology

[0002] Train noise is a significant source of random interference, generated by friction and collisions between the train and the track, particularly harmonic noise caused by the free-wheeling motion resulting from wheel-rail friction. The dominant frequency intensity of train noise increases periodically, and the signal exhibits instantaneous frequency variations. In seismic exploration, train noise significantly impacts the quality of acquired seismic data, especially in urban areas where it contains substantial amounts of train noise, hindering further processing. Currently, traditional seismic data processing techniques address this issue by suppressing random noise and removing train noise from the seismic data. However, this method, while removing train noise, also removes some valid seismic wave information, thus reducing the quality of the seismic data.

[0003] Therefore, there is an urgent need for a new technology that can accurately identify and effectively suppress train noise in order to improve the processing efficiency and reliability of seismic data. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a method, apparatus, electronic device, and medium for suppressing train noise, thereby achieving accurate identification and effective suppression of train noise and improving the quality of seismic data.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for suppressing train noise involves importing collected seismic data into a database, and then performing the following steps sequentially within the database:

[0007] S1. Read and analyze earthquake data, identify train noise.

[0008] Read the seismic data, perform spectral and time-frequency analysis on the seismic data to obtain the spectrum and time spectrum, identify the train noise based on the spectrum and time spectrum, and obtain the train noise frequency band;

[0009] S2. Using variational mode decomposition, the modal components of the seismic data are obtained.

[0010] Variational mode decomposition is used to decompose seismic data into modal components with specific frequency bands.

[0011] The specific frequency band includes the train noise frequency band;

[0012] S3. Set the difference threshold and determine the train noise modal components.

[0013] The amplitude of the modal components is subjected to Fourier transform to obtain the maximum amplitude of each modal component, and then linear fitting is performed to obtain the amplitude trend line.

[0014] Calculate the difference between the maximum amplitude of each modal component and the amplitude trend line, and standardize the difference to obtain the standardized difference value;

[0015] Based on the train noise, a difference threshold is set;

[0016] When the standardized difference value of a modal component is greater than or equal to the difference value threshold, the modal component is a train noise modal component.

[0017] S4. Perform filtering to suppress train noise.

[0018] The train noise mode components obtained in step S3 are filtered to suppress the train noise.

[0019] As a limitation of the present invention, in step S1, the Hilbert-Huang transform (HHT) is used to perform spectral analysis on the seismic data.

[0020] As a limitation of the present invention, in step S1, wavelet transform is used to perform time-frequency analysis on the seismic data.

[0021] As a further limitation of the present invention, the dominant frequency of the train noise increases periodically.

[0022] As a limitation of the present invention, in step S2, the calculation formula for the variational mode decomposition is:

[0023]

[0024] Among them, {u k} is the set of decomposed modal components, {w k} represents the set of center frequencies of the decomposed modal components, K is the number of modal components, δ(t) is the unit impulse function, t is the time variable, j is the imaginary unit, π is pi, ||| is the norm symbol, * is the convolution operation, and u k (t) is the kth modal component function of the decomposition, w k It is the center frequency of the k-th modal component in the decomposition. It is a negative exponential function;

[0025] The value of K is chosen such that the specific frequency band includes the train noise frequency band.

[0026] As a further limitation of the present invention, in step S3, the linear fitting is to establish a binomial formula for the index of the modal components and the maximum amplitude.

[0027] As a limitation of the present invention, in step S4, the filtering process is to use an LMS adaptive filter to filter the train noise.

[0028] The present invention also provides a train noise suppression device, comprising:

[0029] The acquisition module is used to acquire earthquake data;

[0030] The read module is used to read earthquake data;

[0031] The identification module, based on any one of the train noise suppression methods described above, identifies train noise in seismic data;

[0032] The decomposition module, based on any one of the train noise suppression methods described above, decomposes seismic data to obtain modal components with specific frequency bands;

[0033] The module determines the train noise modal components based on any one of the train noise suppression methods described above, by setting a difference threshold.

[0034] The suppression module, based on any one of the train noise suppression methods described above, suppresses train noise.

[0035] The present invention also provides an electronic device, the electronic device comprising:

[0036] Memory, which stores executable instructions;

[0037] A processor that executes the executable instructions in the memory to implement the train noise suppression method described in any of the above technical solutions.

[0038] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the train noise suppression method described in any of the above technical solutions.

[0039] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:

[0040] The present invention discloses a train noise suppression method. First, by performing Hilbert-Huang Transform (HHT) and wavelet transform on seismic data, the spectral and time-frequency spectra are obtained. Based on the spectral and time-frequency spectra, train noise in the seismic data can be accurately identified. The dominant frequency of this train noise conforms to the characteristics of train noise, exhibiting a periodic increase. Then, variational mode decomposition (VMD) is used to decompose the seismic data to obtain modal components with specific frequency bands. Seismic data containing train noise signals are concentrated and distributed into several modal components. Next, an index of the modal components and a binomial formula for the maximum amplitude are established to obtain an amplitude trend line. Further, standardized difference values ​​are obtained. Based on the characteristics of train noise, a difference value threshold is set, and modal components with standardized difference values ​​exceeding the difference value threshold are identified as train noise modal components. Finally, filtering is performed to suppress the train noise.

[0041] The train noise suppression method of this invention can accurately identify and effectively suppress train noise in seismic data. This not only improves the signal-to-noise ratio of seismic data and reduces the negative impact of train noise on seismic data, but also ensures the integrity of effective seismic signals, avoids the loss of valuable signals that may occur in traditional methods, improves the quality of seismic data, and enhances the practicality and reliability of seismic data processing.

[0042] This invention is applicable to train noise suppression in seismic data of urban areas, and is used to solve the problem of train noise in seismic exploration in urban areas. Attached Figure Description

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 This is a flowchart of the train noise suppression method in Embodiment 1 of the present invention;

[0045] Figure 2 This is a schematic diagram of the seismic data imported in Embodiment 1 of the present invention. The solid lines in the diagram represent single shots without train noise suppression.

[0046] Figure 3 The spectrum obtained in Embodiment 1 of the present invention;

[0047] Figure 4 This is the time-frequency spectrum obtained in Embodiment 1 of the present invention;

[0048] Figure 5 This is the modal component map with a specific frequency band obtained in Embodiment 1 of the present invention;

[0049] Figure 6 for Figure 5 The spectrum diagram corresponding to the modal components in the image;

[0050] Figure 7This is a schematic diagram of the seismic data obtained in Embodiment 1 of the present invention to achieve train noise suppression. The solid line box in the diagram represents a single shot that has achieved train noise suppression. Detailed Implementation

[0051] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are only used to explain the present invention and do not limit the present invention.

[0052] Example 1: A method for suppressing train noise

[0053] This embodiment describes a method for suppressing train noise. It uses seismic data collected during urban seismic exploration as an example to illustrate train noise suppression. Figure 1 The acquired seismic data is imported into a database and stored to obtain seismic data in SEGY format, such as... Figure 2 As shown, the seismic data includes seismic traces and trace head information. The trace head information does not include observation system information. The following steps are performed sequentially in the database:

[0054] S1. Read and analyze earthquake data, identify train noise.

[0055] Seismic data is read, and Hilbert-Huang Transform (HHT) is used to perform spectral analysis on the seismic data to obtain the spectrum, such as... Figure 3 As shown, wavelet transform is used to perform time-frequency analysis on seismic data to obtain the time spectrum. Figure 4 As shown, train noise is identified based on the frequency spectrum and time spectrum. Figure 3 and Figure 4 It can be seen that the train noise is a series of harmonic noises. The main frequencies of the train noise are identified as 6Hz, 12Hz, and 24Hz, and these three frequencies increase periodically. The train noise signal presents an instantaneous frequency and has instantaneous variability.

[0056] S2. Obtain the modal components of the seismic data using variational mode decomposition (VMD).

[0057] After inputting seismic data, the variational mode decomposition (VMD) algorithm is used to decompose the data into multiple modal components. Using variational mode decomposition, the following formula is used:

[0058]

[0059] In this process, several key parameters are used to optimize and constrain the calculation results to ensure the accuracy and effectiveness of the decomposition:

[0060] The regularization parameter α controls the bandwidth of the modal components. A smaller α value may result in a wider bandwidth for the modal components, leading to a coarser decomposition result; a larger α value will make the modal components more detailed, but may result in over-decomposition. Therefore, a value between 2000 and 5000 is usually chosen to balance the smoothness and fineness of the decomposition. In this embodiment, the α value is set to 3000.

[0061] The termination tolerance `tol` determines the convergence condition of the optimization process. A smaller tolerance value will cause the algorithm to run more iterations to achieve an accurate decomposition result, but may increase the computation time. Generally, 1e is chosen. -6 to 1e -4 The values ​​between these ranges can achieve a good balance between accuracy and computational efficiency; in this example, the tol value is set to 1e. -5 .

[0062] The number of modes, K, determines how many modal components the signal is decomposed into. A small K value may be insufficient to capture all the important frequency components in the signal, while an excessively large K value may lead to over-decomposition, extracting unnecessary noise or spurious modes. Typically, the choice of K is based on the signal complexity and spectral analysis results. In this embodiment, K is set to 10, decomposing the seismic signal into 10 modal components, such as... Figure 5-6 As shown, the main frequency signals of train noise, 6Hz, 12Hz, and 24Hz, are decomposed into several modal components.

[0063] S3. Set the difference threshold and determine the train noise modal components.

[0064] For the amplitude of each modal component obtained in step S2, perform a Fourier transform to obtain the maximum amplitude of each modal component, perform linear fitting, establish the index of the modal component and the binomial formula of the maximum amplitude, and obtain the amplitude trend line.

[0065] Calculate the difference between the maximum amplitude of each modal component and the amplitude trend line, standardize the difference to obtain the standardized difference value, which is between [0,1].

[0066] Based on the main frequency and harmonic spectrum of the train noise identified in step S1, the difference threshold is set to 0.9.

[0067] When the standardized difference value of a modal component is greater than or equal to the difference value threshold, the modal component is a train noise modal component.

[0068] S4. Perform filtering to suppress train noise.

[0069] The train noise mode components obtained in step S3 are filtered using an LMS adaptive filter to suppress the train noise. Figure 7 As shown.

[0070] Results Analysis

[0071] The seismic data obtained by this invention to suppress train noise, such as Figure 7 As shown, compared with the original seismic data, such as Figure 2 As shown in the comparison, the present invention can improve the accuracy of identifying train noise in seismic data and effectively suppress it. This not only improves the signal-to-noise ratio of seismic data and reduces the negative impact of train noise on seismic data, but also ensures the integrity of effective seismic signals, avoids the loss of valuable signals that may occur in traditional methods, improves the quality of seismic data, and enhances the practicality and reliability of seismic data processing.

[0072] Example 2: A train noise suppression device

[0073] This embodiment is a train noise suppression device, comprising:

[0074] The acquisition module is used to acquire earthquake data;

[0075] The read module is used to read earthquake data;

[0076] The identification module, based on the train noise suppression method in Example 1, identifies the main frequency and harmonic spectrum of train noise in seismic data;

[0077] The decomposition module, based on the train noise suppression method in Example 1, decomposes the seismic data to obtain modal components with specific frequency bands;

[0078] The module determines the train noise modal components by setting a difference threshold based on the train noise suppression method in Example 1.

[0079] The suppression module suppresses train noise based on the train noise suppression method in Example 1.

[0080] Example 3: An electronic device

[0081] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that executes the executable instructions in the memory to implement the train noise suppression method in Embodiment 1.

[0082] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0083] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment disclosed in this application, the processor is used to execute computer-readable instructions stored in the memory.

[0084] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0085] Example 4: A computer-readable storage medium

[0086] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the train noise suppression method in Embodiment 1.

[0087] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.

[0088] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0089] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method of suppressing train noise, characterized by, It is to import the collected seismic data into a database, and sequentially perform the following steps in the database: S1. reading and analyzing seismic data, identifying train noise Reading seismic data, performing spectral analysis and time-frequency analysis on the seismic data, obtaining frequency spectrum and time-frequency spectrum, identifying train noise according to the frequency spectrum and time-frequency spectrum, and obtaining train noise frequency band; S2. Obtain the modal component of the seismic data by using variational mode decomposition Using variational mode decomposition, the seismic data is decomposed into modal components with specific frequency bands; The specific frequency band contains the train noise frequency band; S3. Set the difference value threshold to determine the train noise modal component Performing Fourier transform on the amplitude of the modal component to obtain the amplitude maximum value of each modal component, performing linear fitting to obtain the amplitude trend line; Calculate the difference value of the amplitude maximum value of each modal component and the amplitude trend line, and obtain the standardized difference value by standardizing the difference value; Based on the train noise, set the difference value threshold; When the standardized difference value of the modal component is greater than or equal to the difference value threshold, the modal component is a train noise modal component; S4. Filtering processing to suppress train noise The train noise modal component obtained in step S3 is filtered to complete the suppression of train noise.

2. The train noise suppression method of claim 1, wherein, In step S1, Hilbert-Huang transform is used to perform spectral analysis on the seismic data.

3. The train noise suppression method according to claim 1 or 2, characterized by, In step S1, wavelet transform is used to perform time-frequency analysis on the seismic data.

4. The train noise suppression method of claim 3, wherein, The main frequency of the train noise is periodic and increasing.

5. The train noise suppression method according to claim 1 or 4, characterized by, In step S2, the calculation formula of the variational mode decomposition is: where {u k} is the set of decomposed modal components, {w k} is the set of center frequencies of the decomposed modal components, K is the number of modal components, δ(t) is the unit impulse function, t is the time variable, j is the imaginary unit, π is the circular constant, ‖‖ is the norm symbol, * is the convolution operation, u k (t) is the kth decomposed modal component function, w k is the center frequency of the kth decomposed modal component, is the negative exponential function; The value of K makes the specific frequency band contain the train noise frequency band.

6. The train noise suppression method of claim 5, wherein, In step S3, the linear fitting is a binomial formula of the index and amplitude maximum value of the modal component.

7. The train noise suppression method according to claim 1 or 6, characterized by, In step S4, the filtering processing is to use an LMS adaptive filter to filter the train noise.

8. A train noise suppression apparatus, characterized by comprising: It includes: An acquisition module for acquiring seismic data; A reading module for reading seismic data; An identification module for identifying train noise in seismic data based on the train noise suppression method of any one of claims 1-7; A decomposition module for decomposing seismic data to obtain modal components with specific frequency bands based on the train noise suppression method of any one of claims 1-7; A determination module for setting a difference value threshold to determine a train noise modal component based on the train noise suppression method of any one of claims 1-7; A suppression module for suppressing train noise based on the train noise suppression method of any one of claims 1-7.

9. An electronic device, comprising: The electronic device includes: A memory storing executable instructions; A processor running the executable instructions in the memory to implement the train noise suppression method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program which is executed by a processor to implement the train noise suppression method of any one of claims 1-7.

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