Node data random noise suppression method and device based on extrusion transformation and medium

Abstract

The invention provides a node data random noise suppression method and device based on extrusion transformation and a medium, and belongs to the field of oil and gas geophysical exploration. The method comprises the following steps: inputting passive source data and original seismic data; random noise is extracted from the passive source data, and the distribution condition of the random noise is counted; simulating the random noise according to the distribution condition of the random noise, and counting the distribution condition of the simulated random noise in the extrusion transform domain; and according to the distribution condition of the random noise in the extrusion transform domain, performing denoising processing on the original seismic data to obtain denoised seismic data. By adopting the method, a large amount of random noise in node data can be removed, and a basis is provided for subsequent seismic data processing.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas geophysical exploration, specifically relating to a method, device, and medium for suppressing random noise in nodal data based on compression transformation. Background Technology

[0002] Noise in seismic data can interfere with accurate seismic analysis and interpretation. Common seismic data denoising methods include mean filtering, median filtering, wavelet denoising, and singular value decomposition (SVD). Mean filtering is a simple method that reduces noise by calculating the average value of the neighborhood around a data point using a sliding window; it is suitable for noise with a zero mean, such as Gaussian noise. Median filtering is a nonlinear filtering method that sorts the neighborhood around a data point and selects the median value as the denoised value; it is effective at removing outliers, such as salt-and-pepper noise. Wavelet denoising utilizes the multi-scale analysis characteristics of wavelet transform to decompose the signal into sub-bands of different frequencies. Thresholding is then applied to each sub-band to gradually filter out low-amplitude noise, effectively reducing background noise. Singular value decomposition (SVD) is a linear algebraic technique that decomposes the original seismic data matrix into singular values, left singular vectors, and right singular vectors. By retaining larger singular values ​​for reconstruction, noise can be suppressed and the main signal of the data can be recovered.

[0003] Compression transform is a multi-scale analysis method capable of extracting detailed information and texture features from images. Current research also includes methods for denoising in the synchronous compression transform domain. For example, J. Kang et al. proposed an image denoising method based on compression transform and nonlocal mean filters, which removes noise by transforming the image to the compression domain and further reduces noise by combining nonlocal mean filters. B. Lin et al. proposed an image denoising method based on compression transform, utilizing a multi-scale filter bank based on wavelet transform to extract a contractible representation of the image, demonstrating good performance in noise detection and denoising while preserving image details and texture features. However, these methods assume that the noise information conforms to a certain analysis, which often does not reflect reality. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the prior art and to provide a method, apparatus and medium for suppressing random noise in nodal data based on compression transformation.

[0005] This invention is achieved through the following technical solution:

[0006] A first aspect of the present invention provides a method for suppressing random noise in nodal data based on squeezing transform, comprising:

[0007] S100, input passive source data and raw seismic data;

[0008] S200 extracts random noise from passive source data and statistically analyzes the distribution of random noise.

[0009] S300, simulates random noise based on the distribution of random noise, and statistically analyzes the distribution of the simulated random noise in the squeezing transform domain.

[0010] S400 denoises the original seismic data based on the distribution of random noise in the compression transform domain, resulting in denoised seismic data.

[0011] A further improvement of the present invention is that:

[0012] The distribution of random noise in S200 is statistically analyzed, and the specific operations include:

[0013] Assuming the random noise follows a Gaussian distribution with a mean of 0, the data points for a 5-hour window are denoted as v1, v2, v3, v4, and v5. These 5 samples are extracted sequentially according to the chronological order of the node data. The length of the hour window is 100 sampling points. Where v is the algebraic sum of random noise from the 5-hour window, v i Let represent the random noise of the i-th hour window;

[0014] If |v| < 0.001, then these 5 sets of data are considered to have a mean of 0, and the variance of random noise is statistically calculated according to formula (1):

[0015]

[0016] Where i represents the i-th time window; t represents the sampling point number of the noise signal captured by the time window;

[0017] Therefore, the statistically derived random noise follows the following distribution:

[0018]

[0019] Where s is the value of random noise.

[0020] A further improvement of the present invention is that:

[0021] In S300, random noise is simulated based on its distribution, and the distribution of the simulated random noise in the squeezing transform domain is statistically analyzed. Specific operations include:

[0022] S310, based on the length N of the original seismic data, N random numbers are randomly given, and random noise is simulated using formula (2);

[0023] S320 converts the simulated random noise into the squeeze transform domain;

[0024] S330, the distribution of random noise in the squeeze transform domain in statistical simulation.

[0025] A further improvement of the present invention is that:

[0026] S320 transforms the simulated random noise into the squeeze transform domain. Specific operations include:

[0027] The simulated random noise is converted to the squeezing transform domain using formula (3).

[0028] v s =ST(v) (3)

[0029] Where v is random noise data, v s It is the data after random noise compression transformation, and ST is the compression transformation operator.

[0030] A further improvement of the present invention is that:

[0031] The distribution of random noise in the squeeze transform domain in the statistical simulation of S330 includes the following specific operations:

[0032] Assuming the data after random noise squeezing transformation follows a Gaussian distribution, the new distribution is denoted as:

[0033]

[0034] in, i and j are the index numbers of pixels in the image, M and N are the total number of samples in the vertical and horizontal directions of the image, respectively, v s It is data after random noise compression and transformation.

[0035] A further improvement of the present invention is that:

[0036] In S400, the original seismic data is denoised based on the distribution of random noise in the compression transform domain, resulting in denoised seismic data. Specific operations include:

[0037] Step 410: Convert the original seismic data to the compression transform domain to obtain the compression transformed seismic data;

[0038] Step 420: The probability of each pixel value belonging to random noise is calculated using formula (4) on the seismic data after compression transformation. When the probability is ≥60%, the value of the corresponding synchronous compression transformation domain is set to 0. After all pixel operations are completed, the time domain is returned to obtain the denoised seismic data.

[0039] A second aspect of the present invention provides a nodal data random noise suppression device based on squeezing transform, comprising:

[0040] The input unit is used to input passive source data and raw seismic data;

[0041] The first statistical unit is used to extract random noise from passive source data and to statistically analyze the distribution of random noise.

[0042] The second statistical unit is used to simulate random noise based on the distribution of random noise, and to statistically analyze the distribution of the simulated random noise in the squeezing transform domain.

[0043] The noise suppression unit is used to denoise the original seismic data based on the distribution of random noise in the compression transform domain, and obtain denoised seismic data.

[0044] A further improvement of the present invention is that:

[0045] The second statistical unit includes:

[0046] The random noise simulation sub-unit is used to generate N random numbers based on the length N of the original seismic data to simulate random noise.

[0047] The transformation sub-unit is used to transform the simulated random noise into the squeeze transform domain;

[0048] The statistical sub-unit is used to statistically simulate the distribution of random noise in the squeeze transform domain.

[0049] A further improvement of the present invention is that:

[0050] The transformation sub-unit is used to transform the simulated random noise into the squeeze transform domain, specifically by performing the following operations:

[0051] The simulated random noise is transformed into the squeeze transform domain using the following formula:

[0052] v s =ST(v)

[0053] Where v is random noise data, v s It is the data after random noise compression transformation, and ST is the compression transformation operator.

[0054] A third aspect of the present invention provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the described method for suppressing random noise in nodal data based on squeeze transform.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] This invention first extracts random noise from passive source data and statistically analyzes its distribution. Then, it simulates random noise based on this distribution and statistically analyzes the distribution of the simulated random noise in the compression transform domain. Finally, based on the distribution of random noise in the compression transform domain, it denoises the original seismic data to obtain denoised seismic data. This method can significantly remove random noise from nodal data, providing a foundation for subsequent seismic data processing. Attached Figure Description

[0057] Figure 1 This is a flowchart of a method for suppressing random noise in nodal data based on squeezing transformation in an embodiment of the present invention;

[0058] Figure 2 For simulated random noise;

[0059] Figure 3 This is the result after synchronous squeezing transformation of random noise;

[0060] Figure 4 This is the raw earthquake data;

[0061] Figure 5 This refers to data transformed from raw seismic data to the compression transform domain.

[0062] Figure 6 This is the denoised earthquake data. Detailed Implementation

[0063] The present invention will now be described in further detail with reference to the accompanying drawings:

[0064]

Example 1

[0065] This invention provides a method for suppressing random noise in nodal data based on squeezing transform, such as... Figure 1 As shown, the specific steps include:

[0066] S100, input passive source data and raw seismic data;

[0067] S200 extracts random noise from passive source data and statistically analyzes the distribution of random noise.

[0068] S300, simulates random noise based on the distribution of random noise, and statistically analyzes the distribution of the simulated random noise in the squeezing transform domain.

[0069] S400 denoises the original seismic data based on the distribution of random noise in the compression transform domain, resulting in denoised seismic data.

[0070]

Example 2

[0071] S100, Input passive source data and raw seismic data

[0072] It should be understood that the passive source data refers to seismic waves generated by natural or human activities. During seismic exploration, seismic waves encounter various geological conditions and geophysical features during propagation, resulting in random noise. Furthermore, seismic signals contain rich feature information; in this embodiment, the acquired raw seismic data not only contains the required seismic signals but also some noise signals.

[0073]

Example 3

[0074] S200 extracts random noise from passive source data and statistically analyzes the distribution of the random noise.

[0075] The key characteristic of node-based data acquisition is its continuous nature, meaning that nodes are constantly collecting environmental noise before blasting. This environmental noise is complex, including random noise and regular interference noise such as the noise from factory lathes. These noises are related to, yet distinct from, the noise generated after blasting. This invention only addresses random noise, which, theoretically, follows the same distribution before and after blasting. Therefore, to utilize the statistical properties of random noise, it is necessary to extract it from passive source data and statistically analyze its distribution. Passive source data refers to seismic waves generated by natural or human activities.

[0076] The distribution of statistical random noise includes the following specific operations:

[0077] Assuming the random noise follows a Gaussian distribution with a mean of 0, its variance is generally stable, so we only need to calculate the variance. Specifically:

[0078] Five hourly data windows were established, denoted as v1, v2, v3, v4, and v5. These five data samples were extracted sequentially according to the chronological order of the node data. The length of each hourly window was 100 sampling points. Where v is the algebraic sum of random noise captured by the 5-hour window, v i Let represent the random noise of the i-th hour window;

[0079] If |v| < 0.001, then these 5 sets of data are considered to have a mean of 0, and the variance of random noise is statistically calculated according to formula (1):

[0080]

[0081] Where i represents the i-th time window; t represents the sampling point number of the noise signal captured by the time window;

[0082] Therefore, the statistically derived random noise follows the following distribution:

[0083]

[0084] Where s is the value of random noise.

[0085] Given any value, its probability of occurrence can be calculated because the value of formula (2) is always ≤1.

[0086]

Example 4

[0087] S300 simulates random noise based on its distribution and statistically analyzes the distribution of the simulated random noise in the compression transform domain. Specific operations include:

[0088] According to formula (2), random noise can be randomly simulated. If the length of the signal to be denoised (original seismic data) is N, then N random numbers can be randomly generated using formula (2) to simulate random noise, such as... Figure 2 As shown, the simulated random noise is transformed into the squash transform domain, and the squash transform operator is denoted as ST.

[0089] v s =ST(v) (3)

[0090] Where v is random noise data, v s It is data after random noise compression transformation;

[0091] At this point, the one-dimensional signal is transformed into a two-dimensional signal, that is, in the time-frequency domain, or in the squeeze transform domain, such as... Figure 3 It is the result of simulated random noise squeezing transformation, from Figure 3 As can be seen above, the second distribution of random noise does not exhibit a frequency range bias; random noise is distributed throughout the entire frequency domain. Observing the image, it can be seen that random noise (in the synchronous squeeze transform domain) follows a certain distribution in any frequency band, and the values ​​appear periodically.

[0092] The distribution characteristics of the two-dimensional image in the synchronous squeeze transform domain are statistically analyzed again, still assuming that it follows a Gaussian distribution. The statistical method is similar to that of formulas (1) and (2), and will not be repeated here. The new distribution is denoted as:

[0093]

[0094] in, i and j are the index numbers of pixels in the image, M and N are the total number of samples in the vertical and horizontal directions of the image, respectively, v s It is data after random noise compression and transformation.

[0095]

Example 5

[0096] S400, based on the distribution of random noise in the compression transform domain, denoises the original seismic data to obtain denoised seismic data. Specific operations include:

[0097] S410 transforms the original seismic data into the compression transform domain to obtain the compression-transformed seismic data.

[0098] S420, the probability of each pixel value belonging to random noise is calculated using formula (4) on the seismic data after compression transformation. When the probability is ≥60%, the value of the corresponding synchronous compression transformation domain is set to 0. After all pixel operations are completed, the time domain is returned to obtain the denoised seismic data.

[0099] like Figure 4 The original earthquake data, Figure 5 It is the result of the synchronous compression transformation corresponding to the original seismic data. Figure 3 The result is calculated according to formula (4) to determine the probability that each pixel value belongs to random noise. When the probability is ≥60%, the corresponding value in the synchronous squeezing transform domain is set to 0. After all pixel operations are completed, the result is returned to the time domain, and the denoised result is obtained as shown in the figure. Figure 6 As shown.

[0100]

Example 6

[0101] This invention provides a device for suppressing random noise in nodal data based on squeezing transform, comprising:

[0102] The input unit is used to input passive source data and raw seismic data;

[0103] The first statistical unit is used to extract random noise from passive source data and to statistically analyze the distribution of random noise.

[0104] The second statistical unit is used to simulate random noise based on the distribution of random noise, and to statistically analyze the distribution of the simulated random noise in the squeezing transform domain.

[0105] The noise suppression unit is used to denoise the original seismic data based on the distribution of random noise in the compression transform domain, and obtain denoised seismic data.

[0106] in,

[0107] The second statistical unit includes:

[0108] The random noise simulation sub-unit is used to generate N random numbers based on the length N of the original seismic data to simulate random noise.

[0109] The transformation sub-unit is used to transform the simulated random noise into the squeeze transform domain;

[0110] The statistical sub-unit is used to statistically simulate the distribution of random noise in the squeeze transform domain.

[0111]

Example 7

[0112] This invention provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the described method for suppressing random noise in nodal data based on squeezing transformation.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the foregoing description is only a preferred option and is not restrictive.

Claims

1. A method for suppressing random noise in nodal data based on squeezing transform, characterized in that, include: S100, input passive source data and raw seismic data; S200 extracts random noise from passive source data and statistically analyzes the distribution of random noise. S300, simulates random noise based on the distribution of random noise, and statistically analyzes the distribution of the simulated random noise in the squeezing transform domain. S400 denoises the original seismic data based on the distribution of random noise in the compression transform domain, resulting in denoised seismic data.

2. The method according to claim 1, characterized in that, The distribution of random noise in S200 is statistically analyzed, and the specific operations include: Assuming the random noise follows a Gaussian distribution with a mean of 0, the data points for a 5-hour window are denoted as v1, v2, v3, v4, and v5. These 5 samples are extracted sequentially according to the chronological order of the node data. The length of the hour window is 100 sampling points. Where v is the algebraic sum of random noise from the 5-hour window, v i Let represent the random noise of the i-th hour window; If |v| < 0.001, then these 5 sets of data are considered to have a mean of 0, and the variance of random noise is statistically calculated according to formula (1): Where i represents the i-th time window; t represents the sampling point number of the noise signal captured by the time window; Therefore, the statistically derived random noise follows the following distribution: Where s is the value of random noise.

3. The method according to claim 2, characterized in that, In S300, random noise is simulated based on its distribution, and the distribution of the simulated random noise in the squeezing transform domain is statistically analyzed. Specific operations include: S310, based on the length N of the original seismic data, N random numbers are randomly given, and random noise is simulated using formula (2); S320 converts the simulated random noise into the squeeze transform domain; S330, the distribution of random noise in the squeeze transform domain in statistical simulation.

4. The method according to claim 3, characterized in that, S320 transforms the simulated random noise into the squeeze transform domain. Specific operations include: The simulated random noise is converted to the squeezing transform domain using formula (3). in s =ST(v) (3) Where v is random noise data, v s It is the data after random noise compression transformation, and ST is the compression transformation operator.

5. The method according to claim 4, characterized in that, The distribution of random noise in the squeeze transform domain in the statistical simulation of S330 includes the following specific operations: Assuming the data after random noise squeezing transformation follows a Gaussian distribution, the new distribution is denoted as: in, i and j are the index numbers of pixels in the image, M and N are the total number of samples in the vertical and horizontal directions of the image, respectively, v s It is data after random noise compression and transformation.

6. The method according to claim 5, characterized in that, In S400, the original seismic data is denoised based on the distribution of random noise in the compression transform domain, resulting in denoised seismic data. Specific operations include: Step 410: Convert the original seismic data to the compression transform domain to obtain the compression transformed seismic data; Step 420: The probability of each pixel value belonging to random noise is calculated using formula (4) on the seismic data after compression transformation. When the probability is ≥60%, the value of the corresponding synchronous compression transformation domain is set to 0. After all pixel operations are completed, the time domain is returned to obtain the denoised seismic data.

7. A device for suppressing random noise in nodal data based on compression transformation, characterized in that, include: The input unit is used to input passive source data and raw seismic data; The first statistical unit is used to extract random noise from passive source data and to statistically analyze the distribution of random noise. The second statistical unit is used to simulate random noise based on the distribution of random noise, and to statistically analyze the distribution of the simulated random noise in the squeezing transform domain. The noise suppression unit is used to denoise the original seismic data based on the distribution of random noise in the compression transform domain, and obtain denoised seismic data.

8. The apparatus according to claim 7, characterized in that, The second statistical unit includes: The random noise simulation sub-unit is used to generate N random numbers based on the length N of the original seismic data to simulate random noise. The transformation sub-unit is used to transform the simulated random noise into the squeeze transform domain; The statistical sub-unit is used to statistically simulate the distribution of random noise in the squeeze transform domain.

9. The apparatus according to claim 8, characterized in that, The transformation subunit is used to transform the simulated random noise into the squeeze transform domain, specifically by performing the following operations: The simulated random noise is transformed into the squeeze transform domain using the following formula: in s =ST(v) Where v is random noise data, v s It is the data after random noise compression transformation, and ST is the compression transformation operator.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the nodal data random noise suppression method based on squeezing transform as described in any one of claims 1-6.

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