Method and device for improving resolution by deconvolution of dominant wavelets

Abstract

The invention relates to the field of digital signal processing such as seismic exploration data processing, and particularly discloses a method and device for improving resolution by deconvolution of dominant wavelets, and the method comprises the steps: obtaining a seismic wavelet amplitude spectrum based on seismic data; broadening the frequency band of the seismic wavelet amplitude spectrum to obtain a dominant wavelet amplitude spectrum; and after compensating high and low frequency bands of the dominant wavelet amplitude spectrum, performing inverse Fourier transform to obtain high-resolution seismic data. According to the method for improving the resolution by deconvolution of the dominant wavelets provided by the invention, amplitude spectrum fitting is performed on the dominant wavelets, amplitude components lost due to absorption attenuation are added, and then the amplitude spectrum frequency band of the dominant wavelets is widened, so that the resolution of seismic signals is improved, and high-quality seismic data are provided for high-resolution exploration.

Description

Technical Field

[0001] The present invention relates to the field of digital signal processing such as seismic exploration data processing, and particularly relates to a method and device for improving resolution by dominant wave deconvolution. Background Art

[0002] Deep exploration, oil and gas prediction, and high-resolution processing have always been key areas in oil and gas exploration.

[0003] Severe absorption attenuation and low resolution of seismic data are prominent problems in oil and gas exploration in complex exploration areas in China. Conventional high-resolution processing methods based on convolution theory usually cannot guarantee the fidelity and signal-to-noise ratio after processing. Therefore, it is very important to develop a method that can effectively improve the resolution of pre-stack and post-stack data while protecting the amplitude characteristics and signal-to-noise ratio, which can provide high-quality basic data for high-resolution seismic imaging and fine reservoir interpretation.

[0004] In conventional seismic data processing, pulse deconvolution is usually performed once before and after stacking. The resolution can be improved, but the signal-to-noise ratio is also reduced. After deconvolution processing, a series of processing such as filtering and stacking is required, and the output signal has a limited bandwidth. For spectral whitening deconvolution, the average amplitude spectrum of the signal is calculated, and then the amplitude spectrum is smoothed and weighted to whiten the output signal spectrum, forming a white noise sequence with a limited bandwidth. The resolution is improved to a certain extent, and the signal-to-noise ratio is also improved to a certain extent, but it still cannot meet the production requirements.

[0005] Based on this technical background, the present invention studies a method and device for improving resolution by dominant wave deconvolution. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a method and device for improving resolution by dominant wave deconvolution. The method fits the amplitude spectrum of the dominant wave, adds the amplitude components lost due to absorption attenuation, and then broadens the frequency band of the amplitude spectrum of the dominant wave, thereby improving the resolution of seismic signals and providing high-quality seismic data for high-resolution exploration.

[0007] To achieve the above object, the first aspect of the present invention provides a method for improving resolution by dominant wave deconvolution, including:

[0008] Obtaining the amplitude spectrum of the seismic wavelet based on seismic data;

[0009] Broadening the frequency band of the amplitude spectrum of the seismic wavelet to obtain the amplitude spectrum of the dominant wave;

[0010] Performing inverse Fourier transform on the compensated high and low frequency bands of the amplitude spectrum of the dominant wave to obtain high-resolution seismic data.

[0011] The second aspect of the present invention provides a device for improving resolution by dominant wave deconvolution, including:

[0012] A wavelet acquisition module, configured to obtain a seismic wavelet amplitude spectrum based on seismic data;

[0013] A spectrum expansion module, configured to broaden the frequency band of the seismic wavelet amplitude spectrum to obtain a dominant wavelet amplitude spectrum;

[0014] A compensation transformation module, configured to perform inverse Fourier transform on the compensated high and low frequency bands of the dominant wavelet amplitude spectrum to obtain high-resolution seismic data.

[0015] A third aspect of the present invention provides an electronic device, the electronic device includes:

[0016] A memory, storing executable instructions;

[0017] A processor, the processor runs the executable instructions in the memory to implement the dominant wavelet deconvolution high-resolution improvement method described in the first aspect.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the dominant wavelet deconvolution high-resolution improvement method described in the first aspect.

[0019] The beneficial effects of the present invention include:

[0020] The dominant wavelet deconvolution high-resolution improvement method proposed by the present invention fits the amplitude spectrum of the dominant wavelet, adds the amplitude components lost due to absorption attenuation, and then broadens the frequency band of the amplitude spectrum of the dominant wavelet, thereby improving the resolution of seismic signals and providing high-quality seismic data for high-resolution exploration.

[0021] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By describing the exemplary embodiments of the present invention in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present invention will become more obvious.

[0023] Figure 1 It is a schematic flowchart of the dominant wavelet deconvolution high-resolution improvement method proposed by the present invention.

[0024] Figure 2 It is a schematic flowchart of a specific implementation manner of the dominant wavelet deconvolution high-resolution improvement method proposed by the present invention.

[0025] Figure 3Schematic diagram of comparison between the amplitude spectrum of the original wavelet, the Ricker wavelet amplitude spectrum, and the desired wavelet amplitude spectrum in a specific embodiment of the method for improving resolution by dominant wavelet deconvolution proposed by the present invention.

[0026] Figure 4 Schematic diagram of the update of the wavelet amplitude spectrum in a specific embodiment of the method for improving resolution by dominant wavelet deconvolution proposed by the present invention.

[0027] Figure 5 Post-stack original data map of a certain area in a specific embodiment of the method for improving resolution by dominant wavelet deconvolution proposed by the present invention.

[0028] Figure 6 Post-stack data map of improved resolution in a certain area in a specific embodiment of the method for improving resolution by dominant wavelet deconvolution proposed by the present invention.

[0029] Figure 7 Schematic diagram of comparison between the amplitude spectra before and after improving the resolution of the post-stack data in a specific embodiment of the method for improving resolution by dominant wavelet deconvolution proposed by the present invention. Specific Embodiment

[0030] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0031] The present invention provides a method for improving resolution by dominant wavelet deconvolution, as Figure 1 shown, including:

[0032] Obtaining the amplitude spectrum of the seismic wavelet based on seismic data;

[0033] Broadening the frequency band of the amplitude spectrum of the seismic wavelet to obtain the amplitude spectrum of the dominant wavelet;

[0034] Performing inverse Fourier transform on the compensated high and low frequency bands of the amplitude spectrum of the dominant wavelet to obtain high-resolution seismic data.

[0035] In the present invention, the amplitude spectrum of the dominant wavelet is fitted, and the amplitude components lost due to absorption attenuation are added, and then the frequency band of the amplitude spectrum of the dominant wavelet is broadened, thereby improving the resolution of the seismic signal and providing high-quality seismic data for high-resolution exploration.

[0036] According to the present invention, the time-domain expression of seismic data is:

[0037] x(t) = w(t) * r(t);

[0038] where x(t) is the seismic data, w(t) is the seismic wavelet, r(t) is the reflection coefficient, and * is the convolution operation;

[0039] The reflection coefficient is a random sequence, and the seismic wavelet is autocorrelated.

[0040] According to the present invention, the formula for obtaining the amplitude spectrum of the seismic wavelet based on seismic data is:

[0041] X(f) = W(f)R(f);

[0042]

[0043] where X(f), W(f), and R(f) are the spectra of seismic data, seismic wavelet, and reflection coefficient respectively, and A x (f), A w (f), A r (f) are the amplitude spectra of seismic data, seismic wavelet, and reflection coefficient respectively.

[0044] According to the present invention, the formula for obtaining the dominant wavelet amplitude spectrum by broadening the frequency band of the seismic wavelet amplitude spectrum is:

[0045]

[0046] where X ricker (f), W ricker (f), R ricker (f) are the broadband wavelets obtained by Fourier transform convolution of the spectra of seismic data, seismic wavelet, and reflection coefficient respectively. X -1 (f), W -1 (f), R -1 (f) are the values obtained after Fourier transform of the spectra of seismic data, seismic wavelet, and reflection coefficient and then convolution with the broadband wavelet respectively. A x -1 (f), A w -1 (f), A r- 1 (f) are the amplitude spectra obtained by Fourier transform of the spectra of seismic data, seismic wavelet, and reflection coefficient and then convolution with the broadband wavelet respectively.

[0047] Preferably, the formula for compensating the high and low frequency bands of the dominant wavelet amplitude spectrum is:

[0048]

[0049] where Y is the average value of the amplitude spectrum of the reflection coefficient near the main frequency, a and b are the low frequency and high frequency coefficients respectively, f l 、f R are the low frequency band and high frequency band frequencies respectively, and X 1 (f) is the spectrum of the compensated dominant wavelet.

[0050] According to the present invention, the broadband wavelet is a broadband Ricker wavelet, and its dominant frequency is higher than the dominant frequency of the seismic data.

[0051] Preferably, the formula for obtaining high-resolution seismic data by performing inverse Fourier transform is:

[0052]

[0053]

[0054] where C 1 (f) is the amplitude spectrum of the broadband wavelet, X 2 (f) is the spectrum of the high-resolution seismic data, and x 2 (t) is the high-resolution seismic data.

[0055] The present invention will be described in more detail below through embodiments.

[0056] Embodiment 1:

[0057] As Figure 2 shown, this embodiment provides a method for improving resolution by dominant wavelet deconvolution, and the specific steps are as follows:

[0058] 1) Estimation of the amplitude spectrum of the dominant wavelet:

[0059] For seismic signals, the acquisition of the amplitude spectrum is generally completed through Fourier transform. Without considering noise, according to the convolution model, the seismic record s(t) can be written as the convolution of the seismic wavelet w(t) and the reflection coefficient sequence r(t), that is

[0060] s(t) = w(t) * r(t) (1)

[0061] It is usually assumed that the reflection coefficient is a random sequence. At this time, the autocorrelation of the seismic record can be approximated as the autocorrelation of the seismic wavelet; according to the properties of the Fourier transform, convolution in the time domain is equivalent to multiplication in the frequency domain, so there is

[0062] S(t) = W(t) * R(t) (2)

[0063] The Fourier transforms of the seismic data, seismic wavelet, and reflection coefficient can also be written as:

[0064]

[0065] where A x (f), A w (f), and A r (f) are the amplitude spectra of the seismic data, seismic wavelet, and reflection coefficient respectively;

[0066] 2) Widen the amplitude spectrum of the dominant wavelet:

[0067] Perform the Fourier transform on equation (3) again. After the transformation, convolve it with a broadband wavelet. This wavelet is usually a broadband Ricker wavelet, and its main frequency is higher than the main frequency of the seismic data;

[0068]

[0069] The generated desired wavelet has a higher main frequency and a wider frequency band in the amplitude spectrum, as Figure 3 shown;

[0070] 3) Wavelet amplitude spectrum update:

[0071] Assume that the Fourier transform of the reflection coefficient after wavelet elimination is

[0072] X 1 (f) = X(f) · C -1 (f) (5)

[0073] After wavelet elimination, outliers will appear in the high and low frequency parts, as Figure 4 shown, and attenuation is required. Assume that the average value near the main frequency of the reflection coefficient amplitude spectrum is Y, then the high and low frequencies can be attenuated according to the following formula;

[0074]

[0075] Multiply the amplitude spectrum C 1 (f) of the desired Ricker wavelet by the Fourier transform of the reflection coefficient after wavelet elimination to obtain the Fourier transform of the data with improved resolution:

[0076]

[0077] According to the inverse Fourier transform, the high-resolution seismic record after wavelet replacement processing is;

[0078]

[0079] In this embodiment, as Figure 5 is the actual seismic data of a certain area. The structural amplitude of this area is relatively large, the signal resolution is low, and it is difficult to determine the structure. After improving the resolution, from Figure 6 and Figure 7 it can be seen that the data resolution has been greatly improved, the frequency band has been broadened, and the structure has been better determined.

[0080] Embodiment 2:

[0081] This embodiment provides a method for improving resolution by dominant wavelet deconvolution, as Figure 1 shown, including:

[0082] Obtain the seismic wavelet amplitude spectrum based on the seismic data;

[0083] Widen the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wavelet amplitude spectrum;

[0084] After compensating the high and low frequency bands of the dominant wavelet amplitude spectrum, perform inverse Fourier transform to obtain high-resolution seismic data;

[0085] The time-domain expression of the seismic data is:

[0086] x(t) = w(t) * r(t);

[0087] where x(t) is the seismic data, w(t) is the seismic wavelet, r(t) is the reflection coefficient, and * is the convolution operation;

[0088] The reflection coefficient is a random sequence, and the seismic wavelet is autocorrelated;

[0089] The formula for obtaining the seismic wavelet amplitude spectrum based on the seismic data is:

[0090] X(f) = W(f)R(f);

[0091]

[0092] where X(f), W(f), and R(f) are the spectra of the seismic data, seismic wavelet, and reflection coefficient respectively, and A x (f), A w (f), A r (f) are the amplitude spectra of the seismic data, seismic wavelet, and reflection coefficient respectively;

[0093] The formula for widening the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wavelet amplitude spectrum is:

[0094]

[0095] where X ricker (f), W ricker (f), R ricker (f) are the wide-band wavelets of the Fourier transform convolution of the spectra of the seismic data, seismic wavelet, and reflection coefficient respectively, and X -1 (f), W -1 (f), R -1 (f) are the values after Fourier transform of the spectra of the seismic data, seismic wavelet, and reflection coefficient and convolution with the wide-band wavelet respectively, and A x -1 (f), A w -1 (f), A r- 1 (f) are the amplitude spectra obtained by Fourier transform of the spectra of the seismic data, seismic wavelet, and reflection coefficient and convolution with the wide-band wavelet;

[0096] The formula for compensating the high and low frequency bands of the dominant wave amplitude spectrum is as follows:

[0097]

[0098] Among them, Y is the average value of the amplitude spectrum of the reflection coefficient near the main frequency, a and b are the low-frequency and high-frequency coefficients respectively, f l 、f R are the low-frequency band and high-frequency band frequencies respectively, and X 1 (f) is the spectrum of the compensated dominant wave;

[0099] The broadband wavelet is a broadband Ricker wavelet, and its main frequency is higher than the main frequency of the seismic data;

[0100] The formula for obtaining high-resolution seismic data by performing inverse Fourier transform is as follows:

[0101]

[0102]

[0103] Among them, C 1 (f) is the amplitude spectrum of the broadband wavelet, X 2 (f) is the spectrum of the high-resolution seismic data, and x 2 (t) is the high-resolution seismic data.

[0104] Example 3:

[0105] This embodiment provides a device for improving resolution by dominant wave deconvolution, including:

[0106] A wavelet acquisition module for obtaining the seismic wavelet amplitude spectrum based on seismic data;

[0107] A spectrum expansion module for expanding the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wave amplitude spectrum;

[0108] A compensation transformation module for performing inverse Fourier inverse transformation on the dominant wave amplitude spectrum after compensating the high and low frequency bands to obtain high-resolution seismic data;

[0109] The time-domain expression of the seismic data is:

[0110] x(t) = w(t) * r(t);

[0111] Among them, x(t) is the seismic data, w(t) is the seismic wavelet, r(t) is the reflection coefficient, and * is the convolution operation;

[0112] The reflection coefficient is a random sequence, and the seismic wavelet is autocorrelated;

[0113] The formula for obtaining the amplitude spectrum of a seismic wavelet from seismic data is as follows:

[0114] X(f) = W(f)R(f);

[0115]

[0116] where X(f), W(f), and R(f) are the spectra of seismic data, seismic wavelet, and reflection coefficient respectively, and A x (f), A w (f), A r (f) are the amplitude spectra of seismic data, seismic wavelet, and reflection coefficient respectively;

[0117] The formula for obtaining the dominant wavelet amplitude spectrum by broadening the frequency band of the seismic wavelet amplitude spectrum is as follows:

[0118]

[0119] where X ricker (f), W ricker (f), R ricker (f) are the broadband wavelets obtained by Fourier transform convolution of the spectra of seismic data, seismic wavelet, and reflection coefficient respectively, and X -1 (f), W -1 (f), R -1 (f) are the values after Fourier transform of the spectra of seismic data, seismic wavelet, and reflection coefficient respectively and convolution with the broadband wavelet, and A x -1 (f), A w -1 (f), A r- 1 (f) are the amplitude spectra obtained by Fourier transform of the spectra of seismic data, seismic wavelet, and reflection coefficient respectively and convolution with the broadband wavelet;

[0120] The formula for compensating the high and low frequency bands of the dominant wavelet amplitude spectrum is as follows:

[0121]

[0122] where Y is the average value of the amplitude spectrum of the reflection coefficient near the main frequency, a and b are the low frequency and high frequency coefficients respectively, f l 、f R are the low frequency band and high frequency band frequencies respectively, and X 1 (f) is the spectrum of the compensated dominant wavelet;

[0123] The broadband wavelet is a broadband Ricker wavelet, and its main frequency is higher than the main frequency of the seismic data;

[0124] The formula for obtaining high-resolution seismic data by performing inverse Fourier transform is as follows:

[0125]

[0126]

[0127] Among them, C 1 (f) is the broadband wavelet amplitude spectrum, X 2 (f) is the spectrum of high-resolution seismic data, x 2 (t) is the high-resolution seismic data.

[0128] Example 4:

[0129] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0130] The memory stores executable instructions.

[0131] The processor runs the executable instructions in the memory to implement the method for improving resolution by dominant wavelet deconvolution.

[0132] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0133] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0134] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, this embodiment may also include well-known structures such as communication buses, interfaces, etc., and these well-known structures should also be included in the protection scope of the present invention.

[0135] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0136] Example 5:

[0137] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the method for improving resolution by dominant wavelet deconvolution.

[0138] A computer-readable storage medium according to an embodiment of the present invention stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0139] The above computer-readable storage medium includes but is 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 removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0140] The method for improving resolution by dominant wave deconvolution proposed in the embodiments of the present invention fits the amplitude spectrum of the dominant wave, adds the amplitude components lost due to absorption attenuation, and then broadens the frequency band of the amplitude spectrum of the dominant wave, thereby improving the resolution of seismic signals and providing high-quality seismic data for high-resolution exploration.

[0141] The various embodiments of the present invention have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments.

Claims

1. A method for improving resolution by dominant wave deconvolution, characterized in that, it includes: Obtaining the seismic wavelet amplitude spectrum based on seismic data; Broadening the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wavelet amplitude spectrum; Performing inverse Fourier transform after compensating the high and low frequency bands of the dominant wavelet amplitude spectrum to obtain high-resolution seismic data.

2. The method according to claim 1, characterized in that, The time-domain expression of the seismic data is: x(t) = w(t) * r(t); where x(t) is the seismic data, w(t) is the seismic wavelet, r(t) is the reflection coefficient, and * is the convolution operation; The reflection coefficient is a random sequence, and the seismic wavelet is autocorrelation.

3. The method according to claim 2, characterized in that, The formula for obtaining the seismic wavelet amplitude spectrum based on seismic data is: X(f) = W(f)R(f); where \(X(f)\), \(W(f)\), and \(R(f)\) are the spectra of seismic data, seismic wavelet, and reflection coefficient respectively, and \(A_{\langle0000001\rangle}(f)\), \(A_{\langle0000002\rangle}(f)\), \(A_{\langle0000003\rangle}(f)\) are the amplitude spectra of seismic data, seismic wavelet, and reflection coefficient respectively. x (f), \(A_{\langle0000002\rangle}\) w (f), \(A_{\langle0000003\rangle}\) r (f) are the amplitude spectra of seismic data, seismic wavelet, and reflection coefficient respectively.

4. The method according to claim 3, characterized in that, The formula for broadening the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wavelet amplitude spectrum is: Among them, X ricker (f), W ricker (f), R ricker (f) are the broadband sub-waves of the spectral Fourier transform convolution of seismic data, seismic wavelets, and reflection coefficients respectively. X -1 (f), W -1 (f), R -1 (f) are the values after the spectral Fourier transform of seismic data, seismic wavelets, and reflection coefficients and then convolving with the broadband sub-wave respectively. A x -1 (f), A w -1 (f), A r- 1 (f) are the amplitude spectra obtained by the spectral Fourier transform of seismic data, seismic wavelets, and reflection coefficients and then convolving with the broadband sub-wave respectively.

5. The method according to claim 4, characterized in that, The formula for compensating the high and low frequency bands of the dominant wavelet amplitude spectrum is: where Y is the average value of the amplitude spectrum of the reflection coefficient near the main frequency, a and b are the low-frequency and high-frequency coefficients respectively, and f l , f R are the low-frequency band and high-frequency band frequencies respectively, and X 1 (f) is the spectrum of the compensated dominant wavelet.

6. The method according to claim 5, characterized in that, The broadband wavelet is a broadband Ricker wavelet, and its main frequency is higher than the main frequency of the seismic data.

7. The method according to claim 6, characterized in that, The formula for performing inverse Fourier transform to obtain high-resolution seismic data is: Among them, C 1 (f) is the broadband wavelet amplitude spectrum, X 2 (f) is the high-resolution seismic data spectrum, x 2 (t) is the high-resolution seismic data.

8. An apparatus for improving resolution by dominant wave deconvolution, characterized in that, it includes: A wavelet acquisition module for obtaining the seismic wavelet amplitude spectrum based on seismic data; A spectrum expansion module for broadening the frequency band of the seismic wavelet amplitude spectrum to obtain the dominant wavelet amplitude spectrum; A compensation transformation module for performing inverse Fourier transform after compensating the high and low frequency bands of the dominant wavelet amplitude spectrum to obtain high-resolution seismic data.

9. An electronic device, characterized in that, the electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the method for improving resolution by dominant wave deconvolution according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method for improving resolution by dominant wave deconvolution according to any one of claims 1-7.

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