OBN data denoising method and related device

Abstract

The present invention discloses an OBN data denoising method and related device. The method comprises: performing a dual-tree complex wavelet transform on the P component data and the Z component data respectively; matching the amplitude spectrum of the Z component data according to the maximum amplitude of the amplitude spectrum of the P component data to obtain the amplitude spectrum amplitude value of the Z component data that matches the P component; calculating the amplitude spectrum of the Z component data after amplitude matching; calculating the amplitude matching coefficient according to the amplitude spectrum of the Z component data after amplitude matching; calculating the complex wavelet coefficient of the Z component data after amplitude matching according to the amplitude matching coefficient; performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficient of the Z component data after amplitude matching to obtain the denoised Z component data. The present invention can significantly suppress Vz noise in the Z component data, and the signal-to-noise ratio of the denoised Z component gather data and stacked profile is significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration data processing, and in particular to an OBN data denoising method and related devices. Background Art

[0002] Ocean bottom nodes (OBNs) are four-component geophones placed on the waterbed, with the seismic source at the sea surface. Each acquisition node is independent of the others. OBNs are fixed at the bottom of the water and relatively unaffected by sea conditions. The seismic data collected is omnidirectional and broadband. OBN nodes consist of four-component receivers. One component receiver is a water-detected pressure geophone, which records scalar wavefields (P-component data). The other three mutually perpendicular component receivers are land-detected velocity geophones, which record vector wavefields (X, Y, and Z-component data). Due to the different operating principles of water-detected and land-detected geophones, water-detected geophones are very sensitive to the pressure of the water around them, while land-detected geophones are very sensitive to the wave propagation velocity at their location. Therefore, even for the same seismic wavefield, the two respond differently. Because P-component data only records scalar wavefields, their signal-to-noise ratio is high. However, due to the influence of seafloor geology and the coupling between the detector and the seafloor, the propagation direction of the seismic wavefield reaching the detector is inconsistent with the detector's receiving direction. This causes the Z-component data to receive leaked shear wave energy and be mixed with low-speed, low-frequency noise, namely Vz noise, resulting in a poor signal-to-noise ratio. If left unprocessed, it will adversely affect subsequent migration imaging. Vz noise has a nearly hyperbolic shape, low frequency, strong amplitude, and aliasing with the location and frequency band of the valid signal. Current methods for removing Vz noise include adaptive subtraction of Z-component data using X and Y component data, KL transform denoising, and frequency division noise suppression. Summary of the Invention

[0003] The inventors discovered that existing techniques struggle to effectively denoise Z-component data. Adaptive subtraction of Z-component data using X and Y component data is ineffective, while the KL transform denoising method can only remove linear interference noise. Frequency division noise suppression, a single-channel processing method, offers poor signal-to-noise decomposition and limited denoising effectiveness. To at least partially address the technical challenges of existing techniques, the inventors developed the present invention, which provides, through specific implementations, a method and related apparatus for denoising OBN data.

[0004] In a first aspect, an embodiment of the present invention provides an OBN data denoising method, comprising:

[0005] Performing dual-tree complex wavelet transform on the P component data and the Z component data respectively to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively;

[0006] Determine the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain;

[0007] Matching the position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data matching the P component;

[0008] Calculating an amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data;

[0009] Calculating an amplitude matching coefficient based on the amplitude spectrum of the Z component data after the amplitude matching;

[0010] Calculating the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients;

[0011] The complex wavelet coefficients of the amplitude-matched Z component data are subjected to a dual-tree complex wavelet inverse transform to obtain denoised Z component data.

[0012] In some optional embodiments, before performing dual-tree complex wavelet transform on the P component data and the Z component data respectively, the method includes:

[0013] Obtain the P component data collected and recorded by the OBN's water-detected pressure geophone after the earthquake source is excited; and obtain the Z component data collected and recorded by the OBN's land-detected velocity geophone after the earthquake source is excited.

[0014] In some optional embodiments, performing dual-tree complex wavelet transform on the P component data and the Z component data respectively to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively includes:

[0015] According to the preset hierarchical levels, the P component data and the Z component data are respectively subjected to dual-tree complex wavelet transform of the corresponding hierarchical levels to obtain the amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain.

[0016] In some optional embodiments, matching a position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain an amplitude spectrum amplitude value of the Z component data that matches the P component includes:

[0017] According to the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain, the amplitude spectrum amplitude value of the Z component data at the same position is determined as the amplitude spectrum amplitude value of the Z component data matching the P component.

[0018] In some optional embodiments, calculating the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data includes:

[0019] The amplitude spectrum of the Z component data after amplitude matching is obtained by dividing the amplitude spectrum amplitude value of the Z component data matched with the P component by the maximum amplitude of the amplitude spectrum of the P component data and multiplying the result by the amplitude spectrum of the P component data.

[0020] In some optional embodiments, calculating the amplitude matching coefficient according to the amplitude spectrum of the Z component data after the amplitude matching includes:

[0021] The amplitude spectrum of the amplitude-matched Z component data is divided by the amplitude spectrum of the Z component data at the same position to obtain the amplitude matching coefficient of each position in the amplitude spectrum of the amplitude-matched Z component data.

[0022] In some optional embodiments, calculating the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients includes:

[0023] The amplitude matching coefficient at each position of the amplitude spectrum of the amplitude-matched Z component data is multiplied by the real part and imaginary part of the complex wavelet coefficient of the Z component data at the corresponding position to obtain the real part and imaginary part of the complex wavelet coefficient of the amplitude-matched Z component data.

[0024] In some optional embodiments, performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the amplitude-matched Z component data to obtain denoised Z component data includes:

[0025] According to the hierarchical levels of the dual-tree complex wavelet transform of the Z component data, the complex wavelet coefficients of the amplitude-matched Z component data are subjected to dual-tree complex wavelet inverse transform step by step to obtain the denoised Z component data.

[0026] In a second aspect, an embodiment of the present invention provides an OBN data denoising device, comprising:

[0027] a forward transformation module, configured to perform dual-tree complex wavelet transform on the P component data and the Z component data, respectively, to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain;

[0028] an amplitude matching module for determining the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain; matching the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data that matches the P component; calculating the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data that matches the P component, and the amplitude spectrum of the P component data; calculating the amplitude matching coefficient based on the amplitude spectrum of the Z component data after amplitude matching; and calculating the complex wavelet coefficient of the Z component data after amplitude matching based on the amplitude matching coefficient;

[0029] The inverse transformation module is used to perform a dual-tree complex wavelet inverse transformation on the complex wavelet coefficients of the Z component data after amplitude matching to obtain the denoised Z component data.

[0030] In some optional embodiments, the method further includes:

[0031] The P and Z component data collection module is used to obtain the P component data collected and recorded by the OBN's water-detected pressure geophone after the earthquake source is excited; and the Z component data collected and recorded by the OBN's land-detected velocity geophone after the earthquake source is excited.

[0032] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the aforementioned OBN data denoising method is implemented.

[0033] Based on the same inventive concept, an embodiment of the present invention further provides a terminal device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned OBN data denoising method when executing the program.

[0034] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0035] The embodiment of the present invention provides an OBN data denoising method and related device, which utilizes the dual-tree complex wavelet transform to perform frequency division and multi-directional decomposition characteristics of the signal. The Vz noise in the denoised Z component data is significantly suppressed, and the signal-to-noise ratio of the denoised Z component gather data and stacked profiles is significantly improved.

[0036] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 Flowchart of an OBN data denoising method based on dual-tree complex wavelet transform in an embodiment of the present invention;

[0040] Figure 2a Schematic diagram of dual-tree complex wavelet forward transform in an embodiment of the present invention;

[0041] Figure 2b Schematic diagram of dual-tree complex wavelet inverse transform in an embodiment of the present invention;

[0042] Figure 3a The pulse point image in the embodiment of the present invention;

[0043] Figure 3b The amplitude spectra at each level are obtained by performing a three-level decomposition of the pulse point image by performing a dual-tree complex wavelet transform in an embodiment of the present invention;

[0044] Figure 4 This is a waveform diagram corresponding to the complex wavelet coefficients at each level of the dual-tree complex wavelet transform in an embodiment of the present invention;

[0045] Figure 5 This is a waveform diagram corresponding to the q-21 quadrant complex wavelet coefficients of the dual-tree complex wavelet transform in an embodiment of the present invention;

[0046] Figure 6 This is a waveform diagram corresponding to the q-22 quadrant complex wavelet coefficients of the dual-tree complex wavelet transform in an embodiment of the present invention;

[0047] Figure 7 This is a waveform diagram corresponding to the q-23 quadrant complex wavelet coefficients of the dual-tree complex wavelet transform in an embodiment of the present invention;

[0048] Figure 8a The amplitude spectrum of the third-level decomposition of the dual-tree complex wavelet transform of the P component data in the embodiment of the present invention;

[0049] Figure 8b The amplitude spectrum of the three-level decomposition of the dual-tree complex wavelet transform of the Z component data in the embodiment of the present invention;

[0050] Figure 8c The amplitude spectrum of the Z component data after amplitude matching by dual-tree complex wavelet transform at three levels in the embodiment of the present invention;

[0051] Figure 9 This is a comparison diagram of the gathers before and after the joint denoising of the P / Z component data in the dual-tree complex wavelet domain in an embodiment of the present invention;

[0052] Figure 10 This is a comparison diagram of the stacked sections before and after the joint denoising of the P / Z component data in the dual-tree complex wavelet domain in an embodiment of the present invention;

[0053] Figure 11 This is a specific implementation flow chart of an OBN data denoising method based on dual-tree complex wavelet transform in an embodiment of the present invention;

[0054] Figure 12 This is a block diagram of an OBN data denoising device based on dual-tree complex wavelet transform in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] In order to solve the problems existing in the prior art, an embodiment of the present invention provides an OBN data denoising method and related devices.

[0057] Example 1

[0058] The first embodiment of the present invention provides an OBN data denoising method, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0059] Step S101: performing dual-tree complex wavelet transform on the P component data and the Z component data respectively, to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively.

[0060] P-component data are scalar wavefields recorded by OBN (Ocean Bottom Node) hydrophones, denoted as P(i,j) (i = 1, 2, 3, …, nr0; j = 1, 2, 3, …, nc0), where i represents the row number, nr0 represents the row number, j represents the column number, and nc0 represents the column number. Z-component data are vector wavefields recorded by OBN land-based velocity geophones, denoted as Z(i,j) (i = 1, 2, 3, …, nr0; j = 1, 2, 3, …, nc0), where i represents the row number, nr0 represents the row number, j represents the column number, and nc0 represents the column number. P-component data have a high signal-to-noise ratio (SNR). Z-component data are aliased with low-velocity, low-frequency noise, known as Vz noise, resulting in a poor SNR.

[0061] The dual-tree complex wavelet transform (DTCWT) is implemented by two sets of parallel real discrete wavelet transforms. The dual-tree complex wavelet transform includes forward transform and inverse transform. The forward transform process refers to Figure 2a As shown in Figure 2, h0(n), h1(n), g0(n) and g1(n) are filtering operators, n represents the sample point, and ↓2 represents downsampling. Figure 2b As shown, and (n) is the filtering operator, n represents the sample point, and ↑2 represents upsampling. The subband coefficients generated in Tree 1 and Tree 2 are multiplied by 0.5 before outputting the dual-tree complex wavelet transform result. One set of real discrete wavelet transforms is performed in Tree 1, and the other set of real discrete wavelet transforms is performed in Tree 2. The subband coefficients generated in Tree 1 and Tree 2 are the real and imaginary parts of the complex wavelet coefficients, respectively. The real and imaginary parts of the complex wavelet coefficients constitute the complex wavelet coefficients. Compared to the real discrete wavelet transform, the dual-tree complex wavelet transform not only shares the same multi-scale frequency division characteristics, but also exhibits better translation invariance and more directional sorting.

[0062] For example, a pulse point image is transformed by dual-tree complex wavelet transform, and the pulse point image is referenced Figure 3a As shown in the figure, the number of pulse data rows is 128 and the number of columns is 128. Taking the three-level decomposition of the pulse point as an example, Figure 3b The amplitude spectra at each level are obtained by performing a three-level decomposition of the pulse point image using a dual-tree complex wavelet transform. Figure 3b The number of rows and columns of the pulse data is 256. The amplitude spectrum of the complex wavelet coefficients can be divided into four quadrants at each level of decomposition, and the next level of decomposition is performed on the image of one of the quadrants. Figure 3b In the figure, the first-level decomposition produces images of four quadrants. One quadrant image is selected as the second-level decomposition image, and the first-level decomposition complex wavelet coefficient amplitude spectra of the three quadrants q-11, q-12, and q-13 remain; the second-level decomposition produces images of four quadrants. One quadrant image is selected as the third-level decomposition image, and the second-level decomposition complex wavelet coefficient amplitude spectra of the three quadrants q-21, q-22, and q-23 remain; the four quadrants q-31, q-32, q-33, and q-34 are the last level, that is, the third-level decomposition complex wavelet coefficient amplitude spectrum, and the left and right blocks of each quadrant are the directions divided by the dual-tree complex wavelet transform.

[0063] Figure 4 This is the waveform diagram corresponding to the complex wavelet coefficients at each level of the dual-tree complex wavelet transform. Figure 4 Figure a shows the waveforms corresponding to the three quadrants of the first-order complex wavelet coefficients q11, q12, and q13; Figure 4 Figure b shows the waveforms corresponding to the three quadrants of the secondary complex wavelet coefficients q21, q22, and q23; Figure 4Figure c shows the waveforms corresponding to the three quadrants of the third-level complex wavelet coefficients q31, q32, and q33; Figure 4 Figure d in the figure is the waveform corresponding to the third-level complex wavelet coefficient q34 quadrant. Figure 4 It can be seen that the dual-tree complex wavelet transform has the ability to perform multi-scale frequency decomposition on data, and the frequency components of each level gradually decrease with the increase of the level. However, after reaching a certain level, the frequency components of the data itself are limited, and further grading will no longer produce a significant effect of reducing the frequency components. Therefore, it is necessary to select a reasonable grading level based on the frequency components of the data itself, so as to achieve the expected effect of reducing the frequency components after the dual-tree complex wavelet transform and save time and computing resources.

[0064] The dual-tree complex wavelet transform has the characteristic of waveform direction sorting. Take the three quadrants of the second-level decomposition as an example:

[0065] Figure 5 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-21 quadrant. The q-21 quadrant refers to Figure 3b In the q-21 quadrant, Figure 5 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q21 quadrant. The waveform direction can be marked as +15 ○ ; Figure 5 Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left half of the q21 quadrant. The waveform direction can be marked as +15 ○ ; Figure 5 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q21 quadrant. The waveform direction can be marked as -15 ○ ; Figure 5 Figure d in the figure is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right half of the q21 quadrant. The waveform direction can be marked as -15 ○ It can be seen that the waveform direction of the complex wavelet coefficient after the dual-tree complex wavelet transform is ±15 ○ .

[0066] Figure 6 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-22 quadrant. The q-22 quadrant refers to Figure 3b In the q-22 quadrant, Figure 6 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q22 quadrant. The waveform direction can be marked as +75 ○ ; Figure 6 Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left side of the q22 quadrant. The waveform direction can be marked as +75 ○ ; Figure 6 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q22 quadrant. The waveform direction can be marked as -75 ○ ; Figure 6Figure d in the figure is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right half of the q22 quadrant. The waveform direction can be marked as -75 ○ It can be seen that the waveform direction of the complex wavelet coefficient after the dual-tree complex wavelet transform is ±75 ○ .

[0067] Figure 7 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-23 quadrant, q-23 quadrant refers to Figure 3b In the q-23 quadrant, Figure 7 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q23 quadrant. The waveform direction can be marked as +45. ○ ; Figure 7 Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left side of the q23 quadrant. The waveform direction can be marked as +45 ○ ; Figure 7 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q23 quadrant. The waveform direction can be marked as -45. ○ ; Figure 7 Figure d is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right side of the q23 quadrant. The waveform direction can be marked as -45. ○ It can be seen that the waveform direction of the complex wavelet coefficient after the dual-tree complex wavelet transform is ±45 ○ .

[0068] Depend on Figures 5 to 7 It can be seen that the dual-tree complex wavelet transform has ±15 ○ ±45 ○ and ±75 ○ Similarly, the waveforms of the complex wavelet coefficients in the quadrants corresponding to other hierarchical levels also have ±15 ○ ±75 ○ and ±45 ○ direction, that is, the dual-tree complex wavelet transform has the waveform direction sorting characteristics, which can decompose the data in multiple directions, and Figure 4 Analysis shows that the dual-tree complex wavelet transform (DWT) is capable of performing multi-scale frequency decomposition on data, and within a reasonable range of hierarchical levels, the frequency components at each level gradually decrease as the number of levels increases. In short, the dual-tree complex wavelet transform (DWT) is capable of performing frequency division and multi-directional decomposition on data. Due to its frequency division and multi-directional decomposition characteristics, the DWT is advantageous for signal-to-noise separation of Z-component data containing Vz noise, which exhibits near-hyperbolic, low-frequency, high-amplitude noise and exhibits positional and frequency band aliasing with the signal.

[0069] Perform dual-tree complex wavelet transform on the P component data P(i,j)(i=1,2,3,…,nr0;j=1,2,3,…,nc0) to obtain the amplitude spectrum A_P(i,j)(i=1,2,3,…,nr;j=1,2,3,…,nc) of the P component data in the dual-tree complex wavelet domain, where i represents the row number, nr0 and nr are the row numbers, j represents the column number, nc0 and nc are the column numbers, and according to the characteristics of dual-tree complex wavelet transform, nr=2nr0,nc=2nc0. The amplitude spectrum of the P component data in the dual-tree complex wavelet domain is referred to Figure 8a As shown, similar to the aforementioned three-level decomposition of the pulse point image, the P component data is subjected to a three-level decomposition of the dual-tree complex wavelet transform, including the three first-level decomposition complex wavelet coefficient amplitude spectra remaining from the second-level decomposition, the three second-level decomposition complex wavelet coefficient amplitude spectra remaining from the third-level decomposition, and the four third-level decomposition complex wavelet coefficient amplitude spectra generated by the third-level decomposition.

[0070] Perform dual-tree complex wavelet transform on the Z component data Z(i,j)(i=1,2,3,…,nr0;j=1,2,3,…,nc0) to obtain the amplitude spectrum A_Z(i,j)(i=1,2,3,…,nr;j=1,2,3,…,nc) of the Z component data in the dual-tree complex wavelet domain, where i represents the row number, nr0 and nr are the row numbers, j represents the column number, nc0 and nc are the column numbers, and according to the characteristics of dual-tree complex wavelet transform, nr=2nr0,nc=2nc0. The amplitude spectrum of the Z component data in the dual-tree complex wavelet domain is referred to Figure 8b As shown, similar to the aforementioned three-level decomposition of the pulse point image, the Z component data is subjected to a three-level decomposition of the dual-tree complex wavelet transform, including the three first-level decomposition complex wavelet coefficient amplitude spectra remaining from the second-level decomposition, the three second-level decomposition complex wavelet coefficient amplitude spectra remaining from the third-level decomposition, and the four third-level decomposition complex wavelet coefficient amplitude spectra generated by the third-level decomposition.

[0071] Step S102: determining the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain.

[0072] By comparison, the maximum amplitude of the amplitude spectrum of the P component data is found, recorded as A_P_max, and the amplitude point corresponding to the maximum value A_P_max is found. Since the position in the dual-tree complex wavelet domain can be represented by row and column numbers, the row and column numbers representing the position of the amplitude point corresponding to the maximum value A_P_max in the dual-tree complex wavelet domain can be obtained, represented by (i0, j0).

[0073] Step S103: matching the amplitude spectrum of the Z component data according to the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain to obtain the amplitude value of the amplitude spectrum of the Z component data matching the P component.

[0074] According to the amplitude point (i0, j0) corresponding to the maximum value A_P_max, find the amplitude value of the amplitude spectrum of the Z component data at the position (i0, j0) in the dual-tree complex wavelet domain, that is, the amplitude value of the amplitude spectrum of the Z component data matching the P component, recorded as A_Z_0.

[0075] Step S104: Calculate the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matching the P component, and the amplitude spectrum of the P component data.

[0076] The amplitude spectrum amplitude value A_Z_0 of the Z component data matched with the P component is divided by the maximum amplitude value A_P_max of the amplitude spectrum of the P component data, and multiplied by the amplitude spectrum A_P(i,j) (i=1,2,3,…,nr; j=1,2,3,…,nc) of the P component data at each position, where i represents the row number, nr is the row number, j represents the column number, and nc is the column number, to obtain the amplitude spectrum of the Z component data after amplitude matching. The amplitude spectrum of the Z component data after amplitude matching is recorded as A_Z_M(i,j) (i=1,2,3,…,nr; j=1,2,3,…,nc), where i represents the row number, nr is the row number, j represents the column number, and nc is the column number. The above process can be expressed by the following formula:

[0077]

[0078] Amplitude spectrum reference of Z component data after amplitude matching Figure 8c As shown, since A_Z_M(i,j) is calculated from A_P(i,j), A_P(i,j) is the result of the three-level decomposition of the dual-tree complex wavelet transform on the P classification data, so A_Z_M(i,j) also includes the three-level complex wavelet coefficient amplitude spectrum.

[0079] Step S105: Calculate the amplitude matching coefficient according to the amplitude spectrum of the Z component data after the amplitude matching.

[0080] Divide the amplitude spectrum A_Z_M(i, j) of the Z component data after amplitude matching by the amplitude spectrum A_Z(i, j) of the Z component data at the same position to obtain the amplitude matching coefficient at the corresponding position (i, j). The amplitude matching coefficient at the position (i, j) is recorded as coe(i, j). The above process can be expressed as follows:

[0081]

[0082] The amplitude matching coefficient of each position of the amplitude spectrum A_Z_M of the Z component data after amplitude matching is calculated according to the above method.

[0083] Step S106: Calculate the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients.

[0084] Multiply the amplitude matching coefficient coe(i, j) at position (i, j) by the real part of the complex wavelet coefficient and the imaginary part of the complex wavelet coefficient of the Z component data at the same position, respectively, to obtain the real part and the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching. Among them, the real part of the complex wavelet coefficient of the Z component data at position (i, j) can be recorded as R_Z(i, j), the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j) can be recorded as I_Z(i, j), the real part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching can be recorded as R_M_Z(i, j), and the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching can be recorded as I_M_Z(i, j). The above process can be expressed as:

[0085] R_M_Z(i,j)=R_Z(i,j)_coe(i,j)

[0086] I_M_Z(i,j)=I_Z(i,j)_coe(i,j)

[0087] The real and imaginary parts of the complex wavelet coefficients of the Z component data after amplitude matching at all positions are calculated. The real and imaginary parts of the complex wavelet coefficients after amplitude matching at all positions constitute the complex wavelet coefficients of the Z component data after amplitude matching.

[0088] Step S107: performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the amplitude-matched Z component data to obtain denoised Z component data.

[0089] Inverse transformation process reference Figure 2b As shown, and It is a filtering operator, n represents the sample point, ↑2 represents upsampling, and the subband coefficients generated in tree 1 and tree 2 are multiplied by 0.5 before outputting the dual-tree complex wavelet transform result.

[0090] The effect after suppressing Vz noise is shown in the following figure: Figure 9 、 Figure 10 As shown, Figure 9 This is a comparison diagram of the P / Z component data before and after joint denoising in the dual-tree complex wavelet domain. Figure 10 This is a comparison diagram of the stacked sections before and after the joint denoising of the P / Z component data in the dual-tree complex wavelet domain, where: Figure 9 Figure a in the figure is the gather image before denoising, corresponding to Figure 10 Figure a in Figure 10 Figure a in the figure is the superimposed cross-section before denoising. Figure 9 Figure b in the figure is the gather image after denoising by the technical solution of the present invention, corresponding to Figure 10 Figure b in the figure, Figure 10 Figure b is a superimposed cross-sectional view after denoising using the technical solution of the present invention. Figure 9 Figure c in the figure is a gather image with Vz noise removed by applying the technical solution of the present invention. Figure 9 、 Figure 10 It can be seen that the method introduced in this embodiment has a very significant denoising effect and can effectively suppress the Vz noise in the Z component.

[0091] In the above method of this embodiment, the dual-tree complex wavelet transform is used to perform frequency division and multi-directional decomposition characteristics on the signal, the Vz noise in the denoised Z component data is significantly suppressed, and the signal-to-noise ratio of the denoised Z component gather data and the stacked profile is significantly improved.

[0092] Example 2

[0093] The second embodiment of the present invention provides a specific implementation process of an OBN data denoising method, the process of which is as follows: Figure 11 As shown, the following steps are included:

[0094] Step S201: Acquire the P component data collected and recorded by the OBN's water pressure geophone after the earthquake source is excited; and the Z component data collected and recorded by the OBN's land velocity geophone after the earthquake source is excited.

[0095] The seismic source is excited by a seismic source excitation tool such as an air gun, and the seismic source generates seismic waves.

[0096] Ocean bottom nodes (OBNs) are four-component geophones placed on the waterbed. Their seismic sources are at the sea surface, and each acquisition node is independent of the others. OBNs are fixed underwater and relatively unaffected by sea conditions. The seismic data they collect is omnidirectional and broadband. OBN nodes consist of four-component receivers: one is a water-detected pressure geophone, which records scalar wavefields (P component data); the other three, perpendicular to each other, are land-detected velocity geophones, which record vector wavefields (X, Y, and Z component data).

[0097] P-component data are scalar wavefields recorded by OBN's water pressure geophones, denoted as P(i,j) (i = 1, 2, 3, ..., nr0; j = 1, 2, 3, ..., nc0), where i represents the row number, nr0 represents the row number, j represents the column number, and nc0 represents the column number. Z-component data are vector wavefields recorded by OBN's land velocity geophones, denoted as Z(i,j) (i = 1, 2, 3, ..., nr0; j = 1, 2, 3, ..., nc0), where i represents the row number, nr0 represents the row number, j represents the column number, and nc0 represents the column number. P-component data have a high signal-to-noise ratio. Z-component data are aliased with low-velocity, low-frequency noise, known as Vz noise, resulting in a poor signal-to-noise ratio.

[0098] Step S202: performing dual-tree complex wavelet transform on the P component data and the Z component data respectively, to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively.

[0099] The dual-tree complex wavelet transform (DTCWT) is implemented by two sets of parallel real discrete wavelet transforms. The dual-tree complex wavelet transform includes forward transform and inverse transform. The forward transform process refers to Figure 2a As shown in Figure 2, h0(n), h1(n), g0(n) and g1(n) are filtering operators, n represents the sample point, and ↓2 represents downsampling. Figure 2b As shown, and (n) is the filtering operator, n represents the sample point, and ↑2 represents upsampling. The subband coefficients generated in Tree 1 and Tree 2 are multiplied by 0.5 before outputting the dual-tree complex wavelet transform result. One set of real discrete wavelet transforms is performed in Tree 1, and the other set of real discrete wavelet transforms is performed in Tree 2. The subband coefficients generated in Tree 1 and Tree 2 are the real and imaginary parts of the complex wavelet coefficients, respectively. The real and imaginary parts of the complex wavelet coefficients constitute the complex wavelet coefficients. Compared to the real discrete wavelet transform, the dual-tree complex wavelet transform not only shares the same multi-scale frequency division characteristics, but also exhibits better translation invariance and more directional sorting.

[0100] In some optional embodiments, according to a preset hierarchical level, the P component data and the Z component data are respectively subjected to a dual-tree complex wavelet transform of a corresponding hierarchical level to obtain the amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain.

[0101] The P component data and the Z component data are respectively subjected to step-by-step decomposition by performing a dual-tree complex wavelet transform, and the next-level decomposition is performed in a quadrant of the first-level decomposition complex wavelet coefficient amplitude spectrum to form the next-level decomposition complex wavelet coefficient amplitude spectrum, until the decomposition complex wavelet coefficient amplitude spectrum of the selected hierarchical level is formed, and the last-level decomposition complex wavelet coefficient amplitude spectrum of the P component data / Z component data is used as the amplitude spectrum of the P component data / Z component data in the dual-tree complex wavelet domain.

[0102] For example, a pulse point image is transformed by dual-tree complex wavelet transform, and the pulse point image is referenced Figure 3a As shown in the figure, the number of pulse data rows is 128 and the number of columns is 128. Taking the three-level decomposition of the pulse point as an example, Figure 3b The amplitude spectra at each level are obtained by performing a three-level decomposition of the pulse point image using a dual-tree complex wavelet transform. Figure 3b The number of rows and columns of the pulse data is 256. The amplitude spectrum of the complex wavelet coefficients can be divided into four quadrants at each level of decomposition, and the image of one quadrant is used for the next level of decomposition. Figure 3b In the figure, the first-level decomposition produces images of four quadrants. One quadrant image is selected as the second-level decomposition image, and the first-level decomposition complex wavelet coefficient amplitude spectra of the three quadrants q-11, q-12, and q-13 remain; the second-level decomposition produces images of four quadrants. One quadrant image is selected as the third-level decomposition image, and the second-level decomposition complex wavelet coefficient amplitude spectra of the three quadrants q-21, q-22, and q-23 remain; the four quadrants q-31, q-32, q-33, and q-34 are the last level, that is, the third-level decomposition complex wavelet coefficient amplitude spectrum, and the left and right blocks of each quadrant are the directions divided by the dual-tree complex wavelet transform.

[0103] Figure 4 This is the waveform diagram corresponding to the complex wavelet coefficients at each level of the dual-tree complex wavelet transform. Figure 4 Figure a shows the waveforms corresponding to the three quadrants of the first-order complex wavelet coefficients q11, q12, and q13; Figure 4 Figure b shows the waveforms corresponding to the three quadrants of the secondary complex wavelet coefficients q21, q22, and q23; Figure 4 Figure c shows the waveforms corresponding to the three quadrants of the third-level complex wavelet coefficients q31, q32, and q33; Figure 4 The d figure in the figure is the waveform corresponding to the third-level complex wavelet coefficient q34 quadrant. Figure 4 It can be seen that the dual-tree complex wavelet transform has the ability to perform multi-scale frequency decomposition on data, and the frequency components of each level gradually decrease with the increase of the level. However, after reaching a certain level, the frequency components of the data itself are limited, and further grading will no longer produce a significant effect of reducing the frequency components. Therefore, it is necessary to select a reasonable grading level based on the frequency components of the data itself, so as to achieve the expected effect of reducing the frequency components after the dual-tree complex wavelet transform and save time and computing resources.

[0104] The dual-tree complex wavelet transform has the characteristic of waveform direction sorting. Take the three quadrants of the second-level decomposition as an example:

[0105] Figure 5 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-21 quadrant. The q-21 quadrant refers to Figure 3b In the q-21 quadrant, Figure 5 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q21 quadrant. The waveform direction can be marked as +15 ○ ; Figure 5 Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left half of the q21 quadrant. The waveform direction can be marked as +15 ○ ; Figure 5 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q21 quadrant. The waveform direction can be marked as -15 ○ ; Figure 5 Figure d in the figure is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right half of the q21 quadrant. The waveform direction can be marked as -15 ○ It can be seen that the waveform direction of the complex wavelet coefficient after the dual-tree complex wavelet transform is ±15 ○ .

[0106] Figure 6 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-22 quadrant. The q-22 quadrant refers to Figure 3b In the q-22 quadrant, Figure 6 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q22 quadrant. The waveform direction can be marked as +75 ○ ; Figure 6 Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left side of the q22 quadrant. The waveform direction can be marked as +75 ○ ; Figure 6 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q22 quadrant. The waveform direction can be marked as -75 ○ ; Figure 6 Figure d in the figure is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right half of the q22 quadrant. The waveform direction can be marked as -75 ○ It can be seen that the waveform direction of the complex wavelet coefficient after the dual-tree complex wavelet transform is ±75 ○ .

[0107] Figure 7 This is the waveform corresponding to the complex wavelet coefficient of the dual-tree complex wavelet transform q-23 quadrant, q-23 quadrant refers to Figure 3b In the q-23 quadrant, Figure 7 Figure a is the waveform corresponding to the real part of the complex wavelet coefficient on the left side of the q23 quadrant. The waveform direction can be marked as +45. ○ ; Figure 7Figure b is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the left side of the q23 quadrant. The waveform direction can be marked as +45 ○ ; Figure 7 Figure c is the waveform corresponding to the real part of the complex wavelet coefficient on the right side of the q23 quadrant. The waveform direction can be marked as -45. ○ ; Figure 7 Figure d is the waveform corresponding to the imaginary part of the complex wavelet coefficient on the right side of the q23 quadrant. The waveform direction can be marked as -45. ○ It can be seen that the complex wavelet coefficient waveform direction after dual-tree complex wavelet transform is ±45 ○ .

[0108] Depend on Figures 5 to 7 It can be seen that the dual-tree complex wavelet transform has ±15 ○ ±45 ○ and ±75 ○ Similarly, the waveforms of the complex wavelet coefficients in the quadrants corresponding to other hierarchical levels also have ±15 ○ ±75 ○ and ±45 ○ direction, that is, the dual-tree complex wavelet transform has the waveform direction sorting characteristics, which can decompose the data in multiple directions and Figure 4 Analysis shows that the dual-tree complex wavelet transform (DWT) is capable of performing multi-scale frequency decomposition on data, and within a reasonable range of hierarchical levels, the frequency components at each level gradually decrease as the number of levels increases. In short, the dual-tree complex wavelet transform (DWT) is capable of performing frequency division and multi-directional decomposition on data. Due to its frequency division and multi-directional decomposition characteristics, the DWT is advantageous for signal-to-noise separation of Z-component data containing Vz noise, which exhibits near-hyperbolic, low-frequency, high-amplitude noise and exhibits positional and frequency band aliasing with the signal.

[0109] Perform dual-tree complex wavelet transform on the P component data P(i,j)(i=1,2,3,…,nr0;j=1,2,3,…,nc0) to obtain the amplitude spectrum A_P(i,j)(i=1,2,3,…,nr;j=1,2,3,…,nc) of the P component data in the dual-tree complex wavelet domain, where i represents the row number, nr0 and nr are the row numbers, j represents the column number, nc0 and nc are the column numbers, and according to the characteristics of dual-tree complex wavelet transform, nr=2nr0,nc=2nc0. The amplitude spectrum of the P component data in the dual-tree complex wavelet domain is referred to Figure 8a As shown, similar to the aforementioned three-level decomposition of the pulse point image, the P component data is subjected to a three-level decomposition of the dual-tree complex wavelet transform, including the three first-level decomposition complex wavelet coefficient amplitude spectra remaining from the second-level decomposition, the three second-level decomposition complex wavelet coefficient amplitude spectra remaining from the third-level decomposition, and the four third-level decomposition complex wavelet coefficient amplitude spectra generated by the third-level decomposition.

[0110] Perform dual-tree complex wavelet transform on the Z component data Z(i,j)(i=1,2,3,…,nr0;j=1,2,3,…,nc0) to obtain the amplitude spectrum A_Z(i,j)(i=1,2,3,…,nr;j=1,2,3,…,nc) of the Z component data in the dual-tree complex wavelet domain, where i represents the row number, nr0 and nr are the row numbers, j represents the column number, nc0 and nc are the column numbers, and according to the characteristics of dual-tree complex wavelet transform, nr=2nr0,nc=2nc0. The amplitude spectrum of the Z component data in the dual-tree complex wavelet domain is referred to Figure 8b As shown, similar to the aforementioned three-level decomposition of the pulse point image, the Z component data is subjected to a three-level decomposition of the dual-tree complex wavelet transform, including the three first-level decomposition complex wavelet coefficient amplitude spectra remaining from the second-level decomposition, the three second-level decomposition complex wavelet coefficient amplitude spectra remaining from the third-level decomposition, and the four third-level decomposition complex wavelet coefficient amplitude spectra generated by the third-level decomposition.

[0111] Step S203: determining the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain.

[0112] According to the maximum amplitude value in the amplitude spectrum of the P component data, a position corresponding to the maximum amplitude value is found.

[0113] By comparison, the maximum amplitude of the amplitude spectrum of the P component data is found, recorded as A_P_max, and the corresponding position of the maximum value A_P_max in the dual-tree complex wavelet domain is found. Since the position in the dual-tree complex wavelet domain can be represented by row numbers and column numbers, the row number and column number representing the position corresponding to the maximum value A_P_max in the dual-tree complex wavelet domain can be obtained, represented by (i0, j0).

[0114] Step S204: Match the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data matching the P component.

[0115] In some optional embodiments, matching a position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain an amplitude spectrum amplitude value of the Z component data that matches the P component includes:

[0116] According to the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain, the amplitude spectrum amplitude value of the Z component data at the same position is determined as the amplitude spectrum amplitude value of the Z component data matching the P component.

[0117] For example, based on the amplitude point (i0, j0) corresponding to the maximum value A_P_max, find the amplitude value of the amplitude spectrum of the Z component data at the position (i0, j0) in the dual-tree complex wavelet domain, that is, the amplitude value of the amplitude spectrum of the Z component data matching the P component, recorded as A_Z_0.

[0118] Step S205: Calculate the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matching the P component, and the amplitude spectrum of the P component data.

[0119] In some optional embodiments, calculating the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data includes:

[0120] The amplitude spectrum of the Z component data after amplitude matching is obtained by dividing the amplitude spectrum amplitude value of the Z component data matched with the P component by the maximum amplitude of the amplitude spectrum of the P component data and multiplying the result by the amplitude spectrum of the P component data.

[0121] For example, the amplitude spectrum amplitude value A_Z_0 of the Z component data that matches the P component is divided by the maximum amplitude value A_P_max of the amplitude spectrum of the P component data, and multiplied by the amplitude spectrum A_P(i,j) (i=1,2,3,…,nr; j=1,2,3,…,nc) of the P component data at each position, where i represents the row number, nr is the row number, j represents the column number, and nc is the column number, to obtain the amplitude spectrum of the Z component data after amplitude matching. The amplitude spectrum of the Z component data after amplitude matching is recorded as A_Z_M(i,j) (i=1,2,3,…,nr; j=1,2,3,…,nc), where i represents the row number, nr is the row number, j represents the column number, and nc is the column number. The above process can be expressed by the following formula:

[0122]

[0123] Amplitude spectrum reference of Z component data after amplitude matching Figure 8c As shown, since A_Z_M(i,j) is calculated from A_P(i,j), A_P(i,j) is the result of the three-level decomposition of the dual-tree complex wavelet transform on the P classification data, so A_Z_M(i,j) also includes the three-level complex wavelet coefficient amplitude spectrum.

[0124] Step S206: Calculate an amplitude matching coefficient based on the amplitude spectrum of the Z component data after the amplitude matching.

[0125] In some optional embodiments, the amplitude spectrum of the amplitude-matched Z component data is divided by the amplitude spectrum of the Z component data at the same position to obtain the amplitude matching coefficient of each position in the amplitude spectrum of the amplitude-matched Z component data.

[0126] For example, the amplitude spectrum A_Z_M(i, j) of the Z component data after amplitude matching is divided by the amplitude spectrum A_Z(i, j) of the Z component data at the same position to obtain the amplitude matching coefficient at the corresponding position (i, j). The amplitude matching coefficient at the position (i, j) is recorded as coe(i, j). The above process can be expressed as follows:

[0127]

[0128] The amplitude matching coefficient of each position of the amplitude spectrum A_Z_M of the Z component data after amplitude matching is calculated according to the above method.

[0129] Step S207: Calculate the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients.

[0130] In some optional embodiments, the amplitude matching coefficient at each position of the amplitude spectrum of the amplitude-matched Z component data is multiplied by the real part and imaginary part of the complex wavelet coefficient of the Z component data at the corresponding position to obtain the real part and imaginary part of the complex wavelet coefficient of the amplitude-matched Z component data.

[0131] For example, the amplitude matching coefficient coe(i, j) at position (i, j) is multiplied by the real part of the complex wavelet coefficient of the Z component data at the same position and the imaginary part of the complex wavelet coefficient of the Z component data at the same position, respectively, to obtain the real part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching and the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j). Among them, the real part of the complex wavelet coefficient of the Z component data at position (i, j) can be recorded as R_Z(i, j), the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j) can be recorded as I_Z(i, j), the real part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching can be recorded as R_M_Z(i, j), and the imaginary part of the complex wavelet coefficient of the Z component data at position (i, j) after amplitude matching can be recorded as I_M_Z(i, j). The above process can be expressed as:

[0132] R_M_Z(i,j)=R_Z(i,j)_coe(i,j)

[0133] I_M_Z(i,j)=I_Z(i,j)_coe(i,j)

[0134] The real and imaginary parts of the complex wavelet coefficients of the Z component data after amplitude matching at all positions are calculated. The real and imaginary parts of the complex wavelet coefficients after amplitude matching at all positions constitute the complex wavelet coefficients of the Z component data after amplitude matching.

[0135] Step S208: performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the amplitude-matched Z component data to obtain denoised Z component data.

[0136] In some optional embodiments, according to the hierarchical levels of the dual-tree complex wavelet transform of the Z component data, the complex wavelet coefficients of the amplitude-matched Z component data are subjected to dual-tree complex wavelet inverse transform step by step to obtain denoised Z component data.

[0137] Inverse transformation process reference Figure 2b As shown, and It is a filtering operator, n represents the sample point, ↑2 represents upsampling, and the subband coefficients generated in tree 1 and tree 2 are multiplied by 0.5 before outputting the dual-tree complex wavelet transform result.

[0138] The effect after suppressing Vz noise is shown in the following figure: Figure 9 、 Figure 10 As shown, Figure 9 This is a comparison diagram of the P / Z component data before and after joint denoising in the dual-tree complex wavelet domain. Figure 10 This is a comparison diagram of the stacked sections before and after the joint denoising of the P / Z component data in the dual-tree complex wavelet domain, where: Figure 9 Figure a in the figure is the gather image before denoising, corresponding to Figure 10 Figure a in Figure 10 Figure a in the figure is the superimposed cross-section before denoising. Figure 9 Figure b in the figure is the gather image after denoising by the technical solution of the present invention, corresponding to Figure 10 Figure b in the figure, Figure 10 Figure b is a superimposed cross-sectional view after denoising using the technical solution of the present invention. Figure 9 Figure c in the figure is a gather image with Vz noise removed by applying the technical solution of the present invention. Figure 9 、 Figure 10 It can be seen that the method introduced in this embodiment has a very significant denoising effect and can effectively suppress the Vz noise in the Z component.

[0139] In the above method of this embodiment, the dual-tree complex wavelet transform is used to perform frequency division and multi-directional decomposition characteristics on the signal, the Vz noise in the denoised Z component data is significantly suppressed, and the signal-to-noise ratio of the denoised Z component gather data and the stacked profile is significantly improved.

[0140] Example 3

[0141] The third embodiment of the present invention provides an OBN data denoising device, the structure of which is as follows: Figure 12 Shown, including:

[0142] A forward transformation module 102 is configured to perform dual-tree complex wavelet transform on the P component data and the Z component data, respectively, to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain;

[0143] The amplitude matching module 103 is configured to determine the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain; match the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data that matches the P component; calculate the amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data that matches the P component, and the amplitude spectrum of the P component data; calculate the amplitude matching coefficient based on the amplitude spectrum of the Z component data after amplitude matching; and calculate the complex wavelet coefficient of the Z component data after amplitude matching based on the amplitude matching coefficient.

[0144] The inverse transform module 104 is configured to perform a dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the amplitude-matched Z component data to obtain denoised Z component data.

[0145] In some optional embodiments, it further includes:

[0146] The P and Z component data collection module 101 is used to obtain the P component data collected and recorded by the OBN's water pressure detector after the earthquake source is excited; and the Z component data collected and recorded by the OBN's land velocity detector after the earthquake source is excited.

[0147] In the above-mentioned device of this embodiment, the dual-tree complex wavelet transform is used to perform frequency division and multi-directional decomposition characteristics on the signal, the Vz noise in the denoised Z component data is significantly suppressed, and the signal-to-noise ratio of the denoised Z component gather data and the stacked profile is significantly improved.

[0148] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the aforementioned OBN data denoising method is implemented.

[0149] Based on the same inventive concept, an embodiment of the present invention further provides a terminal device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned OBN data denoising method when executing the program.

[0150] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0151] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0152] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0153] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. An OBN data denoising method, characterized in that: include: Performing dual-tree complex wavelet transform on the P component data and the Z component data respectively to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively; Determine the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain; Matching the position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data matching the P component; Calculating an amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data; Calculating an amplitude matching coefficient based on the amplitude spectrum of the Z component data after the amplitude matching; Calculating the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients; The complex wavelet coefficients of the amplitude-matched Z component data are subjected to a dual-tree complex wavelet inverse transform to obtain denoised Z component data.

2. The method according to claim 1, wherein Before performing dual-tree complex wavelet transform on the P component data and the Z component data respectively, the method includes: Obtain the P component data collected and recorded by the OBN's water-detected pressure geophone after the earthquake source is excited; and obtain the Z component data collected and recorded by the OBN's land-detected velocity geophone after the earthquake source is excited.

3. The method according to claim 1, wherein The performing dual-tree complex wavelet transform on the P component data and the Z component data respectively to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain respectively includes: According to the preset hierarchical levels, the P component data and the Z component data are respectively subjected to dual-tree complex wavelet transform of the corresponding hierarchical levels to obtain the amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain.

4. The method according to claim 1, wherein The step of matching a position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain an amplitude spectrum amplitude value of the Z component data matching the P component includes: According to the corresponding position of the maximum amplitude of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain, the amplitude spectrum amplitude value of the Z component data at the same position is determined as the amplitude spectrum amplitude value of the Z component data matching the P component.

5. The method according to claim 1, wherein Calculating an amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data includes: The amplitude spectrum of the Z component data after amplitude matching is obtained by dividing the amplitude spectrum amplitude value of the Z component data matched with the P component by the maximum amplitude of the amplitude spectrum of the P component data and multiplying the result by the amplitude spectrum of the P component data.

6. The method according to claim 1, wherein The calculating the amplitude matching coefficient according to the amplitude spectrum of the Z component data after the amplitude matching includes: The amplitude spectrum of the amplitude-matched Z component data is divided by the amplitude spectrum of the Z component data at the same position to obtain the amplitude matching coefficient of each position in the amplitude spectrum of the amplitude-matched Z component data.

7. The method according to claim 1, wherein The step of calculating the complex wavelet coefficient of the Z component data after amplitude matching based on the amplitude matching coefficient includes: The amplitude matching coefficient at each position of the amplitude spectrum of the amplitude-matched Z component data is multiplied by the real part and imaginary part of the complex wavelet coefficient of the Z component data at the corresponding position to obtain the real part and imaginary part of the complex wavelet coefficient of the amplitude-matched Z component data.

8. The method according to claim 1, wherein The step of performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the amplitude-matched Z component data to obtain denoised Z component data includes: According to the hierarchical levels of the dual-tree complex wavelet transform of the Z component data, the complex wavelet coefficients of the amplitude-matched Z component data are subjected to dual-tree complex wavelet inverse transform step by step to obtain the denoised Z component data.

9. An OBN data denoising device, characterized in that: include: a forward transformation module, configured to perform dual-tree complex wavelet transform on the P component data and the Z component data, respectively, to obtain amplitude spectra of the P component data and the Z component data in the dual-tree complex wavelet domain; an amplitude matching module, configured to determine the maximum amplitude of the amplitude spectrum of the P component data and the corresponding position of the maximum amplitude in the dual-tree complex wavelet domain; Matching the position corresponding to the maximum amplitude value of the amplitude spectrum of the P component data in the dual-tree complex wavelet domain with the amplitude spectrum of the Z component data to obtain the amplitude value of the amplitude spectrum of the Z component data matching the P component; Calculating an amplitude spectrum of the Z component data after amplitude matching based on the maximum amplitude of the amplitude spectrum of the P component data, the amplitude value of the amplitude spectrum of the Z component data matched with the P component, and the amplitude spectrum of the P component data; Calculating an amplitude matching coefficient based on the amplitude spectrum of the Z component data after the amplitude matching; Calculating the complex wavelet coefficients of the Z component data after amplitude matching based on the amplitude matching coefficients; The inverse transformation module is used to perform a dual-tree complex wavelet inverse transformation on the complex wavelet coefficients of the Z component data after amplitude matching to obtain the denoised Z component data.

10. The device according to claim 9, wherein Also includes: The P and Z component data collection module is used to obtain the P component data collected and recorded by the OBN's water pressure geophone after the source is excited; and the Z-component data collected and recorded by the OBN's land velocity geophone after exciting the source.

11. A computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the OBN data denoising method according to any one of claims 1 to 8.

12. A terminal device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the OBN data denoising method according to any one of claims 1 to 8 is implemented.

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