Data interpolation method and device for suppressing window effect and storage medium

Abstract

The invention provides a data interpolation method and device for suppressing a window effect, and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: performing block processing on input seismic data, converting each block of seismic data into a frequency wave number spectrum, iteratively extracting the frequency wave number spectrum of an effective signal, inversely transforming the frequency wave number spectrum into time-space domain data, and multiplying an amplitude compensation coefficient by the amplitude of a corresponding position to obtain the time-space domain data. And finally, adding results of different windows to obtain a final result. According to the method, the problem that a conventional algorithm is difficult to suppress the window effect is solved, through linear weighting and additional amplitude compensation factors, the energy of window overlapping and non-overlapping areas is consistent, and the effects of seismic data denoising and interpolation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical exploration, can be applied to seismic data noise suppression and data interpolation in geophysical exploration, and specifically relates to a data interpolation method, device and storage medium for suppressing window effect. Background Art

[0002] With the in-depth development of oil and gas exploration, exploration and development has gradually turned to complex oil and gas reservoirs, high-resolution seismic exploration, time-lapse seismic and other exploration technologies, so the requirements for the quality of seismic data have gradually increased. Due to the influence of factors such as surface terrain conditions, seismic data acquisition is often irregular, which seriously affects the quality of seismic data migration imaging. With the development of seismic data interpolation technology, this problem has gradually been solved by five-dimensional regularization technology (five-dimensional regularization technology is a commonly used technology in existing production plants. The function is to input seismic data with irregular coordinate distribution and output seismic data with regular coordinate distribution. Regular data has better migration imaging effect than irregular data), but the intuitive factor of the effect is affected by the window boundary during the interpolation process. This technology mainly compensates the energy of the overlapping area of ​​the window boundary so that the energy of the overlapping area and the non-overlapping area of ​​the window is basically the same. This technology has been gradually and widely used in seismic data noise suppression and data interpolation, which is of great significance to improving the quality of seismic data.

[0003] The traditional technology for suppressing the window effect is to use a weighted approach to smoothly transition at the boundary from a linear weighted approach of 0-1. However, since the signal characteristics between windows are not completely consistent, the traditional method results in low signal energy in the overlapping area and poor window effect suppression effect. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a data interpolation method, device and storage medium for suppressing the window effect. By applying an amplitude compensation factor on the basis of linear weighting, the window boundary effect is effectively eliminated to obtain higher quality pre-stack seismic data.

[0005] The present invention is achieved through the following technical solutions:

[0006] The first aspect of the present invention provides a data interpolation method for suppressing the window effect, which divides the input seismic data into blocks and converts each block of seismic data into a frequency wavenumber spectrum, iteratively extracts the frequency wavenumber spectrum of the effective signal and then inversely transforms it into time-space domain data, multiplies the amplitude compensation coefficient by the amplitude of the corresponding position, and finally adds the results of different windows to obtain the final result.

[0007] A further improvement of the present invention is:

[0008] The method specifically comprises the following steps:

[0009] Step 1: Input seismic data and process the seismic data in blocks;

[0010] Step 2: Convert each piece of seismic data into a frequency wavenumber spectrum;

[0011] Step 3: Iteratively extract the frequency wavenumber spectrum of the effective signal;

[0012] Step 4: Inversely transform the extracted frequency wavenumber spectrum into time-space domain data;

[0013] Step 5: Add the results of different windows to get the final result.

[0014] A further improvement of the present invention is:

[0015] In step 1, seismic data is input and processed in blocks. The specific operations include:

[0016] Input the seismic data X(t,x) to be denoised or interpolated, and divide the seismic data into L blocks X according to the window size and overlap ratio. 1 (t,x),X 2 (t,x)...X L (t,x).

[0017] A further improvement of the present invention is:

[0018] In step 2, each piece of seismic data is converted into a frequency wavenumber spectrum, specifically:

[0019] The frequency spectrum of each piece of seismic data is calculated by Fourier transform, and then the frequency wavenumber spectrum is calculated by irregular Fourier transform.

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

[0021] The specific operations in step 2 include:

[0022] For data X l (t,x) is Fourier transformed to get the spectrum f=0,1,...,F,for Perform irregular Fourier transform to get k=-K,...,-1,0,1,...,K.

[0023] A further improvement of the present invention is:

[0024] In step 3, the frequency wavenumber spectrum of the effective signal is iteratively extracted. The specific operations include:

[0025] (1) Let f = 0, l = 1;

[0026] (2) Let iter = 0, generate Same size The values ​​are all 0+0i, i is a complex number;

[0027] (3) Find the current frequency slice The maximum value of and find the position (f 1 ,k 1 ),remember for amp;

[0028] (4) Calculation

[0029] (5) iter = iter + 1, determine whether iter is less than the number of iterations. If so, jump to (2), otherwise jump to (6);

[0030] (6) f = f + 1, determine whether f is equal to F, if so, jump to (7), if not, jump to (2);

[0031] (7) l = l + 1, determine whether l is equal to L, if so, jump to (8), if not, jump to (2);

[0032] (8)End.

[0033] A further improvement of the present invention is:

[0034] In step 4, the extracted frequency wavenumber spectrum is inversely transformed into time-space domain data. The specific operations include:

[0035] Calculate all data blocks l=1,2,...,L, convert the discrete frequency wavenumber spectrum into continuous time domain data;

[0036] Calculate the amplitude compensation coefficient of the overlapping area and multiply the amplitude of the corresponding position by comp(z)×X(t,z).

[0037] A further improvement of the present invention is:

[0038] The specific calculation process of the amplitude compensation coefficient of the overlapping area is as follows:

[0039] Set the window size, m sampling points in the time direction, n sampling points in the space direction, and the overlap ratio to o(0.2 <o<0.5),

[0040] The window overlap length is O, and at the coordinate z within the overlap range, the linear weighting weight and amplitude compensation coefficient are:

[0041] Amplitude compensation coefficient:

[0042] A second aspect of the present invention provides a data interpolation device for suppressing window effect, comprising:

[0043] An input unit, used for inputting seismic data and processing the seismic data in blocks;

[0044] A conversion unit, used for converting each block of seismic data into a frequency wave number spectrum;

[0045] An extraction unit, used for iteratively extracting a frequency wave number spectrum of a valid signal;

[0046] An inverse transformation unit, used for inversely transforming the extracted frequency wave number spectrum into time and space domain data;

[0047] The merging unit is used to add the results of different windows to obtain the final result.

[0048] The third aspect of the present invention is a computer-readable storage medium, which stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the data interpolation method for suppressing the window effect as described above.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention solves the problem that conventional algorithms are difficult to suppress window effects. Through linear weighting and additional amplitude compensation factors, the energy of window overlapping and non-overlapping areas is made consistent, thereby improving the effects of seismic data denoising and interpolation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention provides a flow chart of a data interpolation method for suppressing window effect;

[0052] Figure 2 is a linear weighted schematic diagram before optimization;

[0053] Figure 3 is the optimized weighted schematic diagram;

[0054] Figure 4 Seismic data before processing;

[0055] Figure 5 It is the result of seismic data processed by existing conventional methods;

[0056] Figure 6 It is the result of seismic data processed by the method of the present invention. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0058] Aiming at the problem that conventional window boundary effect suppression is not effective, the present invention proposes a data interpolation method for suppressing window effect, which mainly calculates amplitude compensation coefficient and applies the amplitude compensation coefficient to existing seismic data interpolation and denoising algorithms to achieve false frequency suppression.

[0059] The present invention provides a data interpolation method for suppressing the window effect. After the input seismic data is processed in blocks, each block of seismic data is converted into a frequency wavenumber spectrum. After the frequency wavenumber spectrum of the effective signal is iteratively extracted, it is inversely transformed into time-space domain data, and then interpolation is performed.

[0060] [Example 1]

[0061] The embodiment of the present invention provides a data interpolation method for suppressing window effect, such as Figure 1 As shown, the method specifically comprises the following steps:

[0062] Step 1: Input seismic data and process the seismic data in blocks;

[0063] Specific operations include:

[0064] Input the seismic data X(t,x) to be denoised or interpolated, and divide the seismic data into L blocks X according to the window size and overlap ratio. 1 (t,x),X 2 (t,x)...X L (t,x);

[0065] The input seismic data is time-space domain data.

[0066] Step 2: Convert each piece of seismic data into a frequency wavenumber spectrum;

[0067] The frequency spectrum of each piece of seismic data is calculated by Fourier transform, and the frequency wavenumber spectrum is calculated by irregular Fourier transform;

[0068] Since time is sampled regularly, a direct Fourier transform is sufficient, but the spatial coordinates are irregular, so an irregular Fourier transform is required. However, this spectrum cannot be used directly, and it is necessary to iteratively extract the energy of the effective signal through step 3.

[0069] Specific operations include:

[0070] For data X l (t,x) is Fourier transformed to get the spectrum f=0,1,...,F,for Perform irregular Fourier transform to get k=-K,...,-1,0,1,...,K.

[0071] Step 3: Iteratively extract the frequency wavenumber spectrum of the effective signal;

[0072] Iteratively extract the maximum energy of the frequency wave number spectrum, and subtract the contribution of the extracted maximum energy to the current frequency wave number spectrum;

[0073] Specific operations include:

[0074] (1) Let f = 0, l = 1;

[0075] (2) The number of iterations for a single frequency slice is 400. Let iter = 0 to generate the same Same size The values ​​are all 0+0i, i is a complex number;

[0076] (3) Find the current frequency slice The maximum value of and find the position (f 1 ,k 1 ),remember for amp;

[0077] (4) Calculation

[0078] (5) iter = iter + 1, determine whether iter is less than the number of iterations 400, if so, jump to (2), if not, jump to (6);

[0079] (6) f = f + 1, determine whether f is equal to F, if so, jump to (7), if not, jump to (2);

[0080] (7) l = l + 1, determine whether l is equal to L, if so, jump to (8), if not, jump to (2);

[0081] (8)End.

[0082] Step 4: Inversely transform the extracted frequency wavenumber spectrum into time-space domain data;

[0083] Specific operations include:

[0084] Calculate all data blocks l=1,2,...,L, convert the discrete frequency wavenumber spectrum into continuous time domain data;

[0085] Calculate the amplitude compensation coefficient of the overlapping area and multiply the amplitude of the corresponding position by comp(z)×X(t,z).

[0086] The specific calculation process of the amplitude compensation coefficient of the overlapping area is as follows:

[0087] Set the window size, m sampling points in the time direction, n sampling points in the space direction, and the overlap ratio to o(0.2 <o<0.5),

[0088] The window overlap length is O, and at the coordinate z within the overlap range, the linear weighting weight and amplitude compensation coefficient are:

[0089] Linear weighting of the left window: w 1 =(Oz) / O(1)

[0090] Linear weighting of the right window: w 2 = z / O(2)

[0091] Amplitude compensation coefficient:

[0092] Step 5: Add the results of different windows to get the final result.

[0093] Figure 2 , Figure 3 They are the weights before and after optimization. Figure 4 is the data before processing, Figure 5 It is the result of seismic data processed by existing conventional methods. Figure 5 As can be seen, the energy in the middle is inconsistent with the energy on both sides. Figure 6 is the result of seismic data processed by the method of the present invention, from Figure 6 It can be seen that the energy in the middle is consistent with the energy on both sides, and the effect is more stable.

[0094] [Example 2]

[0095] An embodiment of the present invention provides a data interpolation device for suppressing window effect, comprising:

[0096] The input unit is used to input seismic data and process the seismic data in blocks. Specifically, the following operations are performed:

[0097] Input the seismic data X(t,x) to be denoised or interpolated, and divide the seismic data into L blocks X according to the window size and overlap ratio. 1 (t,x),X 2 (t,x)...X L (t,x);

[0098] The input seismic data is time-space domain data.

[0099] The conversion unit is used to convert each piece of seismic data into a frequency wavenumber spectrum, and specifically performs the following operations:

[0100] For data X l (t,x) is Fourier transformed to get the spectrum f=0,1,...,F,for Perform irregular Fourier transform to get k=-K,...,-1,0,1,...,K.

[0101] The extraction unit is used to iteratively extract the frequency wave number spectrum of the effective signal, and specifically performs the following operations:

[0102] (1) Let f = 0, l = 1;

[0103] (2) The number of iterations for a single frequency slice is 400. Let iter = 0 to generate the same Same size The values ​​are all 0+0i, i is a complex number;

[0104] (3) Find the current frequency slice The maximum value of and find the position (f 1 ,k 1 ),remember for amp;

[0105] (4) Calculation

[0106] (5) iter = iter + 1, determine whether iter is less than the number of iterations 400, if so, jump to (2), if not, jump to (6);

[0107] (6) f = f + 1, determine whether f is equal to F, if so, jump to (7), if not, jump to (2);

[0108] (7) l = l + 1, determine whether l is equal to L, if so, jump to (8), if not, jump to (2);

[0109] (8)End.

[0110] The inverse transformation unit is used to inversely transform the extracted frequency wave number spectrum into time and space domain data, specifically performing the following operations:

[0111] Calculate all data blocks l=1,2,...,L, convert the discrete frequency wavenumber spectrum into continuous time domain data;

[0112] Calculate the linear weighted weight and amplitude compensation coefficient of the overlapping area, and multiply the amplitude compensation coefficient with the amplitude of the corresponding position comp(z)×X(t,z).

[0113] The specific calculation process of the amplitude compensation coefficient of the overlapping area is as follows:

[0114] Set the window size, m sampling points in the time direction, n sampling points in the space direction, and the overlap ratio to o(0.2 <o<0.5),

[0115] The window overlap length is O, and at the coordinate z within the overlap range, the linear weighting weight and amplitude compensation coefficient are:

[0116] Linear weighting of the left window: w 1 =(Oz) / O(1)

[0117] Linear weighting of the right window: w 2 = z / O(2)

[0118] Amplitude compensation coefficient:

[0119] The merging unit is used to add the results of different windows to obtain the final result.

[0120] [Example 3]

[0121] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the data interpolation method for suppressing the window effect as described above.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0123] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A data interpolation method to suppress window effect, It is characterized in that After the input seismic data is processed in blocks, each block of seismic data is converted into a frequency wavenumber spectrum. The frequency wavenumber spectrum of the effective signal is iteratively extracted and then inversely transformed into time-space domain data. The amplitude compensation coefficient is multiplied by the amplitude of the corresponding position, and finally the results of different windows are added to obtain the final result.

2. The data interpolation method for suppressing window effect according to claim 1, It is characterized in that The method specifically comprises the following steps: Step 1: Input seismic data and process the seismic data in blocks; Step 2: Convert each piece of seismic data into a frequency wavenumber spectrum; Step 3: Iteratively extract the frequency wavenumber spectrum of the effective signal; Step 4: Inversely transform the extracted frequency wavenumber spectrum into time-space domain data; Step 5: Add the results of different windows to get the final result.

3. The data interpolation method for suppressing window effect according to claim 2, It is characterized in that In step 1, seismic data is input and processed in blocks. The specific operations include: Input the seismic data X(t,x) to be denoised or interpolated, and divide the seismic data into L blocks X according to the window size and overlap ratio. 1 (t,x),X 2 (t,x)...X L (t,x).

4. The data interpolation method for suppressing window effect according to claim 2, It is characterized in that In step 2, each piece of seismic data is converted into a frequency wavenumber spectrum, specifically: The frequency spectrum of each piece of seismic data is calculated by Fourier transform, and then the frequency wavenumber spectrum is calculated by irregular Fourier transform.

5. The data interpolation method for suppressing window effect according to claim 4, It is characterized in that The specific operations in step 2 include: For data X l (t,x) is Fourier transformed to get the spectrum f=0,1,...,F,for Perform irregular Fourier transform to get k=-K,...,-1,0,1,...,K.

6. The data interpolation method for suppressing window effect according to claim 5, It is characterized in that In step 3, the frequency wavenumber spectrum of the effective signal is iteratively extracted. The specific operations include: (1) Let f = 0, l = 1; (2) Let iter = 0, generate Same size The values ​​are all 0+0i, i is a complex number; (3) Find the current frequency slice The maximum value of and find the position (f 1 ,k 1 ),remember for amp; (4) Calculation (5) iter = iter + 1, determine whether iter is less than the number of iterations. If so, jump to (2), otherwise jump to (6); (6) f = f + 1, determine whether f is equal to F, if so, jump to (7), if not, jump to (2); (7) l = l + 1, determine whether l is equal to L, if so, jump to (8), if not, jump to (2); (8)End.

7. The data interpolation method for suppressing window effect according to claim 6, It is characterized in that In step 4, the extracted frequency wavenumber spectrum is inversely transformed into time-space domain data. The specific operations include: Calculate all data blocks Convert discrete frequency wavenumber spectra to continuous time domain data; Calculate the amplitude compensation coefficient of the overlapping area and multiply the amplitude of the corresponding position by comp(z)×X(t,z).

8. The data interpolation method for suppressing window effect according to claim 7, It is characterized in that The specific calculation process of the amplitude compensation coefficient of the overlapping area is as follows: Set the window size, m sampling points in the time direction, n sampling points in the space direction, and the overlap ratio to o(0.2 <o<0.5), The window overlap length is O, and at the coordinate z within the overlap range, the linear weighting weight and amplitude compensation coefficient are: Amplitude compensation coefficient:

9. A data interpolation device for suppressing window effect, It is characterized in that include: An input unit, used for inputting seismic data and processing the seismic data in blocks; A conversion unit, used for converting each block of seismic data into a frequency wave number spectrum; An extraction unit, used for iteratively extracting a frequency wave number spectrum of a valid signal; An inverse transformation unit, used for inversely transforming the extracted frequency wave number spectrum into time and space domain data; The merging unit is used to add the results of different windows to obtain the final result.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the data interpolation method for suppressing the window effect as described in any one of claims 1 to 8.

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