Offshore seismic data dual-detection merging method based on multi-channel pattern matching

Abstract

The invention relates to the technical field of seismic signal processing, and relates to an offshore seismic data double-detection merging method based on multi-channel pattern matching, which comprises the following steps: inputting water detection data and land detection data; introducing an instrument correction factor to perform frequency matching on the land detection data to obtain corrected land detection data; obtaining a phase matching factor from the corrected land detection data in a Hilbert transform domain by using a phase matching factor characteristic equation, and applying the phase matching factor to the land detection data to obtain land detection data after phase correction; enveloping and smoothing the land detection data after phase correction to realize amplitude energy balance, and obtaining the land detection data after amplitude consistency correction; calculating multi-channel joint coefficients for the land detection data, and acting each joint coefficient on respective adjacent multi-channel data to obtain multi-channel joint land detection data; and carrying out pattern matching on the obtained multi-channel combined land detection data, and then combining the land detection data with the water detection data to obtain a double-detection combination result. According to the invention, an accurate water and land detection data merging result can be obtained.

Description

Technical Field

[0001] This invention relates to the field of seismic signal processing technology, and in particular, to a dual-detection merging method for marine seismic data based on multichannel pattern matching. Background Technology

[0002] With the development of seismic exploration technology, offshore oil and gas exploration places increasingly higher demands on the signal-to-noise ratio and resolution of seismic data. Single reflections from the same subsurface interface recorded by land-sea geophones have in-phase axes of the same polarity, while virtual reflections and seawater rumble have opposite polarities. Land-sea geophone data merging techniques can effectively eliminate multiple waves from seawater rumble. Multichannel merging considers the temporal and spatial correlation of seismic data, providing more accurate dual-detector merged data for data processing. However, commonly used methods for merging land-sea geophone data in geophysical exploration neglect the spatiotemporal variation of seismic wavelets, failing to fully utilize the characteristics of marine seismic data. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies that neglect the spatiotemporal variation characteristics of seismic wavelets and fail to fully utilize the characteristics of marine seismic data when merging land and sea seismic data. This invention provides a dual-detection merging method for marine seismic data based on multichannel mode matching, which can further improve merging accuracy and provide good data for subsequent imaging processing.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for dual-detection merging of marine seismic data based on multi-channel mode matching, comprising the following steps: S1. Input water inspection data and land inspection data; S2. Introduce an instrument calibration factor to perform frequency matching on the input land inspection data to eliminate the influence of differences between the land inspection data and the water inspection data and obtain the land inspection data after instrument calibration. S3. The phase matching factor is obtained in the Hilbert transform domain of the land detection data after instrument calibration in step S2 using the characteristic equation of the phase matching factor. The phase matching factor is then applied to the land detection data to obtain phase-corrected land detection data. S4. Envelope and smooth the phase-corrected land detection data to achieve amplitude energy balance and obtain amplitude consistency-corrected land detection data. S5. Calculate the multichannel joint coefficients for the amplitude consistency-corrected land inspection data obtained in step S4, and apply each joint coefficient to its respective adjacent multichannel data to obtain multichannel joint land inspection data. S6. Perform pattern matching on the multi-channel combined land inspection data obtained in step S5, and then merge it with the water inspection data to obtain the dual inspection merged result.

[0005] Preferably, in step S3, the Hilbert seismic trace sequence is obtained by using the Blackman window Hilbert factor transformation on the instrument-calibrated land detection data, the phase matching factor is obtained by using the characteristic equation coefficients of the phase matching factor, and the obtained phase matching factor is convolved with the instrument-calibrated land detection data to obtain the phase-calibrated land detection data.

[0006] Preferably, the phase-corrected land detection data is :

[0007] Among them, the Hilbert seismic trace sequence Land inspection data calibrated by the instrument and Blackman window Hilbert factor sequence Convolution yields:

[0008] Constant phase matching is represented as:

[0009] Where θ is the phase correction angle, and a, b, and c are the coefficients of the characteristic equation of the phase matching factor.

[0010] Preferably, the Blackman-Hilbert factor sequence :

[0011] Where N is the length of the factor sequence to be solved.

[0012] Preferably, the coefficients of the characteristic equation of the phase matching factor are:

[0013] Where p represents the water test data, I and J represent the size of the selected data window in two directions, and i and j represent the position of the data within the window.

[0014] Preferably, in step S4, a Hilbert transform is performed on the phase-corrected land detection data to obtain a data envelopment, a quasi-Gaussian linear weighted average smoothing filter is applied to the data envelopment to obtain an amplitude consistency correction coefficient, and the obtained correction coefficient is applied to the phase-corrected land detection data to obtain amplitude consistency corrected land detection data.

[0015] Preferably, in step S4, in order to reduce the influence of strong reflected signals on nearby signals, it is necessary to perform phase correction on the land surveillance data. Perform amplitude consistency correction on the data. Perform a Hilbert transform to obtain the transformed sequence. Combined into analytical signals :

[0016]

[0017] Therefore, land inspection data are obtained in the Hilbert transform domain. Envelope:

[0018] By obtaining the envelope for each seismic data point, we can obtain... Then, perform a quasi-Gaussian smoothing filter on it to obtain smoothed data. z and x represent the location coordinates of the corresponding seismic trace:

[0019] in, and Relative to the point in the z and x directions, respectively. offset, convolution kernel Represented as:

[0020] in Let Variance be the variance, and let Amplitude Consistency Correction Coefficient be the coefficient used to obtain the amplitude consistency. Smoothing data is required. Perform inverse gain:

[0021] Taking a specific road inspection data as the analysis object (i.e., the position coordinates z and x are constant values), the amplitude consistency correction coefficient is introduced into the phase-corrected land inspection data. Land inspection data after amplitude consistency correction were obtained. : .

[0022] Preferably, in step S5, the amplitude consistency-corrected land inspection data obtained in step S4 are... Selecting an appropriate window function to calculate multichannel joint coefficients :

[0023] Applying each joint coefficient to its respective adjacent channels yields the joint land surveillance data for each channel. The solution equation is:

[0024] Where i is the current channel number. The spatiotemporal influence factor of neighboring multichannel data on the joint coefficients is calculated, and the final result is used to determine the impact of this factor on the joint coefficients. The sensitivity is low, different The difference in values ​​will ultimately be reflected in the multichannel joint coefficient. On the value, the data from each land inspection station The solution equations were rearranged into matrix form to obtain multi-channel combined land inspection data. : .

[0025] Preferably, in step S6, due to the spatiotemporal variation characteristics of the actual land surveillance data wavelet, it is necessary to process the multichannel joint land surveillance data obtained in step S5. Perform pattern matching:

[0026] in, For the matched land-based seismic data, The coefficients represent the autoregressive pattern matching coefficients, and N represents the filter length in the land inspection data. It consists of multiple combined land-based seismic data; Combine data from multiple land inspection stations The combined effect of autoregressive pattern matching:

[0027] in, To accommodate the number of multi-channel joint filters, the pattern matching process is expanded into an equation matrix:

[0028] Since the data is spatiotemporally variable, a regularization constraint operator needs to be introduced when solving the problem. Then perform pattern matching:

[0029] in:

[0030] To obtain the autoregressive pattern matching coefficients The following least squares problems can be solved:

[0031] in, For water testing data, For the multi-channel joint land inspection data that needs to be matched, the autoregressive pattern matching coefficients can be obtained by using the conjugate gradient method or the steepest descent method.

[0032] Preferably, the obtained autoregressive pattern matching coefficients are convolved with the land inspection data and then matched with the water inspection data to obtain combined land and water inspection data:

[0033] in, This represents the uplink data obtained by pattern matching and double detection merging. This indicates the downlink wave data obtained by pattern matching and double detection merging.

[0034] Compared with existing technologies, the advantages of this invention are: it can obtain high-precision combined water and land detection data, providing excellent data for subsequent imaging processing. Compared with traditional dual-detection merging methods, this invention achieves high-precision separation of uplink and downlink wavefield data, with higher accuracy than traditional methods, especially for high-frequency seismic records. The quality of the spectrum after dual-detection merging is significantly improved, and the reduction of notch effect is more obvious. Attached Figure Description

[0035] Figure 1 This is a flowchart of the dual-detection merging method for marine seismic data based on multichannel pattern matching according to the present invention; Figure 2 This is a map of the water seismic data input in the dual-detection merging method for marine seismic data based on multi-channel pattern matching in this invention. Figure 3 This is a map of land-based seismic data input into the dual-detection merging method for marine seismic data based on multichannel pattern matching in this invention. Figure 4 This is a diagram of the land-based seismic data after pattern matching in the dual-detection merging method for marine seismic data based on multichannel pattern matching in this invention. Figure 5 This is the upflow wave field diagram after merging land and sea inspections in the dual inspection merging method for marine seismic data based on multichannel mode matching in this invention; Figure 6 This is the downlink wavefield diagram after merging land and sea inspections in the dual inspection merging method for marine seismic data based on multichannel mode matching in this invention; Figure 7 This is a spectrum diagram of the combined land and sea seismic data in the dual-detection merging method for marine seismic data based on multichannel pattern matching, as described in this invention. Figure 8 This is a water detection data diagram of a single channel extracted from the dual detection merging method of marine seismic data based on multichannel pattern matching in this invention. Figure 9 This is a land inspection data diagram of a single channel data extracted in the dual inspection merging method of marine seismic data based on multi-channel pattern matching of the present invention. Figure 10This is a diagram of the instrument correction factor for single-channel data extracted in the dual-detection merging method for marine seismic data based on multichannel pattern matching in this invention. Figure 11 This is a diagram of the filter coefficients of a single channel data extracted in the dual-detection merging method for marine seismic data based on multichannel pattern matching of the present invention. Figure 12 This is an uplink wavefield diagram of a single channel data extracted from the dual-detection merging method of marine seismic data based on multichannel pattern matching in this invention. Figure 13 This is a downlink wavefield diagram of a single channel data extracted in the dual-detection merging method for marine seismic data based on multichannel pattern matching of the present invention. Detailed Implementation

[0036] The present invention will be further described below with reference to specific embodiments.

[0037] Example 1 like Figure 1 As shown, a dual-detection merging method for marine seismic data based on multi-channel pattern matching includes the following steps: S1. Input water inspection data and land inspection data; S2. Introduce an instrument calibration factor to perform frequency matching on the input land inspection data to eliminate the influence of differences between the land inspection data and the water inspection data and obtain the land inspection data after instrument calibration. S3. The phase matching factor is obtained in the Hilbert transform domain of the land detection data after instrument calibration in step S2 using the characteristic equation of the phase matching factor. The phase matching factor is then applied to the land detection data to obtain phase-corrected land detection data. S4. Envelope and smooth the phase-corrected land detection data to achieve amplitude energy balance and obtain amplitude consistency-corrected land detection data. S5. Calculate the multichannel joint coefficients for the amplitude consistency-corrected land inspection data obtained in step S4, and apply each joint coefficient to its respective adjacent multichannel data to obtain multichannel joint land inspection data. S6. Perform pattern matching on the multi-channel combined land inspection data obtained in step S5, and then merge it with the water inspection data to obtain the dual inspection merged result.

[0038] It should be noted that in step S1, the P-component common detection point data received by the water pressure detector and the Z-component common detection point data received by the land velocity detector must first be input. The input water detection data is as follows: Figure 2 As shown, the input land inspection data is as follows: Figure 3As shown. In step S2, to address the inconsistency in frequency response caused by the differences in the working principles of land and sea inspections, Fourier transform is used to apply an instrument correction factor to each land inspection data point in the frequency domain to achieve frequency matching. The instrument correction factor is as follows: Figure 10 As shown, the land inspection data after instrument calibration was obtained.

[0039] Example 2 The difference from Embodiment 1 is that, in this embodiment, in step S3, the Hilbert seismic trace sequence is obtained by using the Blackman window Hilbert factor transformation on the instrument-calibrated land detection data, the phase matching factor is obtained by using the characteristic equation coefficients of the phase matching factor, and the obtained phase matching factor is convolved with the instrument-calibrated land detection data to obtain the phase-calibrated land detection data.

[0040] Among them, the phase-corrected land detection data is :

[0041] Among them, the Hilbert seismic trace sequence Land inspection data calibrated by the instrument and Blackman window Hilbert factor sequence Convolution yields:

[0042] Constant phase matching is represented as:

[0043] Where θ is the phase correction angle, and a, b, and c are the coefficients of the characteristic equation of the phase matching factor.

[0044] Additionally, the Blackman-Hilbert factor sequence :

[0045] Where N is the length of the factor sequence to be solved.

[0046] Furthermore, the coefficients of the characteristic equation for the phase matching factor are:

[0047] Where p represents the water test data, I and J represent the size of the selected data window in two directions, and i and j represent the position of the data within the window.

[0048] In step S4, the phase-corrected land detection data is subjected to Hilbert transform to obtain a data envelopment. The data envelopment is then subjected to a quasi-Gaussian linear weighted average smoothing filter to obtain an amplitude consistency correction coefficient. The obtained correction coefficient is then applied to the phase-corrected land detection data to obtain amplitude consistency corrected land detection data.

[0049] In addition, in step S4, in order to reduce the influence of strong reflected signals on nearby signals, it is necessary to perform phase correction on the land surveillance data. Perform amplitude consistency correction on the data. Perform a Hilbert transform to obtain the transformed sequence. Combined into analytical signals :

[0050]

[0051] Therefore, land inspection data are obtained in the Hilbert transform domain. Envelope:

[0052] By obtaining the envelope for each seismic data point, we can obtain... Then, perform a quasi-Gaussian smoothing filter on it to obtain smoothed data. z and x represent the location coordinates of the corresponding seismic trace:

[0053] in, and Relative to the point in the z and x directions, respectively. offset, convolution kernel Represented as:

[0054] in Let Variance be the variance, and let Amplitude Consistency Correction Coefficient be the coefficient used to obtain the amplitude consistency. Smoothing data is required. Perform inverse gain:

[0055] Taking a specific land inspection data point as the analysis object (i.e., the position coordinates z and x are constant values), the amplitude consistency correction coefficient is introduced into the phase-corrected land inspection data. Land inspection data after amplitude consistency correction were obtained. : .

[0056] In step S5, the amplitude consistency-corrected land inspection data obtained in step S4 are processed. Selecting an appropriate window function to calculate multichannel joint coefficients :

[0057] Applying each joint coefficient to its respective adjacent channels yields the joint land surveillance data for each channel. The solution equation is:

[0058] Where i is the current channel number. The spatiotemporal influence factor of neighboring multichannel data on the joint coefficients is calculated, and the final result is used to determine the impact of this factor on the joint coefficients. The sensitivity is low, different The difference in values ​​will ultimately be reflected in the multichannel joint coefficient. On the value, the data from each land inspection station The solution equations were rearranged into matrix form to obtain multi-channel combined land inspection data. : .

[0059] Example 3 The difference between this embodiment 3 and embodiment 2 is that, in this embodiment 3, in step S6, due to the spatiotemporal variation characteristics of the actual land surveillance data wavelet, it is necessary to process the multichannel joint land surveillance data obtained in step S5. Perform pattern matching:

[0060] in, For the matched land-based seismic data, such as Figure 4 As shown, The coefficients represent the autoregressive pattern matching coefficients, and N represents the filter length in the land inspection data. It consists of multiple combined land-based seismic data; Combine data from multiple land inspection stations The combined effect of autoregressive pattern matching:

[0061] in, To accommodate the number of multi-channel joint filters, the pattern matching process is expanded into an equation matrix:

[0062] Since the data is spatiotemporally variable, a regularization constraint operator needs to be introduced when solving the problem. Then perform pattern matching:

[0063] in:

[0064] To obtain the autoregressive pattern matching coefficients The following least squares problems can be solved:

[0065] in, For water testing data, For multi-channel joint land inspection data that needs matching, the autoregressive pattern matching coefficients can be obtained using the conjugate gradient method or the steepest descent method, such as... Figure 11 As shown.

[0066] The obtained autoregressive pattern matching coefficients are convolved with the land inspection data and then matched with the water inspection data to obtain the combined land and water inspection data. The input single-channel water inspection data is as follows: Figure 8 As shown, the single-lane land inspection data is as follows: Figure 9 As shown:

[0067] in, This represents the uplink data obtained by pattern matching and double detection merging. This indicates that the downlink data obtained through pattern matching and dual detection merging is as follows: The output uplink data is as follows: Figure 5 As shown, the downwave data is as follows Figure 6 As shown, the merged spectrum results are as follows: Figure 7 As shown, the extracted single-channel up-wave data is as follows: Figure 12 As shown, the extracted single-channel downwave data is as follows: Figure 13 As shown.

[0068] In the specific implementation of the above embodiments, the technical features can be combined in any non-contradictory way. For the sake of brevity, not all possible combinations of the above technical features are described. However, as long as the combination of these technical features is not contradictory, it should be considered to be within the scope of this specification.

[0069] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A dual-detection merging method for marine seismic data based on multi-channel pattern matching, characterized in that, Includes the following steps: S1. Input water inspection data and land inspection data; S2. Introduce an instrument calibration factor to perform frequency matching on the input land inspection data to eliminate the influence of differences between the land inspection data and the water inspection data and obtain the land inspection data after instrument calibration. S3. The phase matching factor is obtained in the Hilbert transform domain of the land detection data after instrument calibration in step S2 using the characteristic equation of the phase matching factor. The phase matching factor is then applied to the land detection data to obtain phase-corrected land detection data. S4. Envelope and smooth the phase-corrected land detection data to achieve amplitude energy balance and obtain amplitude consistency-corrected land detection data. S5. Calculate the multichannel joint coefficients for the amplitude consistency-corrected land inspection data obtained in step S4, and apply each joint coefficient to its respective adjacent multichannel data to obtain multichannel joint land inspection data. S6. Perform pattern matching on the multi-channel combined land inspection data obtained in step S5, and then merge it with the water inspection data to obtain the dual inspection merged result.

2. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 1, characterized in that, In step S3, the Hilbert seismic trace sequence is obtained by using the Blackman window Hilbert factor transformation on the instrument-calibrated land detection data. The phase matching factor is obtained by using the characteristic equation coefficients of the phase matching factor. The obtained phase matching factor is convolved with the instrument-calibrated land detection data to obtain the phase-calibrated land detection data.

3. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 2, characterized in that, The phase-corrected land inspection data is : Among them, the Hilbert seismic trace sequence Land inspection data calibrated by the instrument and Blackman window Hilbert factor sequence Convolution yields: Constant phase matching is represented as: Where θ is the phase correction angle, and a, b, and c are the coefficients of the characteristic equation of the phase matching factor.

4. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 3, characterized in that... It lies in, The Blackman-Hilbert factor sequence : Where N is the length of the factor sequence to be solved.

5. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 4, characterized in that... It lies in, The coefficients of the characteristic equation for the phase matching factor are: Where p represents the water test data, I and J represent the size of the selected data window in two directions, and i and j represent the position of the data within the window.

6. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 5, characterized in that: In step S4, a Hilbert transform is performed on the phase-corrected land detection data to obtain a data envelopment. The data envelopment is then subjected to a quasi-Gaussian linear weighted average smoothing filter to obtain amplitude consistency correction coefficients. These correction coefficients are then applied to the phase-corrected land detection data to obtain amplitude consistency corrected land detection data.

7. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 6, characterized in that: In step S4, in order to reduce the influence of strong reflected signals on nearby signals, it is necessary to perform phase correction on the land surveillance data. Perform amplitude consistency correction on the data. Perform a Hilbert transform to obtain the transformed sequence. Combined into analytical signals : Therefore, land inspection data are obtained in the Hilbert transform domain. Envelope: By obtaining the envelope for each seismic data point, we can obtain... Then, perform a quasi-Gaussian smoothing filter on it to obtain smoothed data. z and x represent the location coordinates of the corresponding seismic trace: in, and Relative to the point in the z and x directions, respectively. offset, convolution kernel Represented as: in Let Variance be the variance, and let Amplitude Consistency Correction Coefficient be the coefficient used to obtain the amplitude consistency. Smoothing data is required. Perform inverse gain: Taking a specific road inspection data as the analysis object (i.e., the position coordinates z and x are constant values), the amplitude consistency correction coefficient is introduced into the phase-corrected land inspection data. Land inspection data after amplitude consistency correction were obtained. : 。 8. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 7, characterized in that, In step S5, the amplitude consistency-corrected land inspection data obtained in step S4 are processed. Selecting an appropriate window function to calculate multichannel joint coefficients : Applying each joint coefficient to its respective adjacent channels yields the joint land surveillance data for each channel. The solution equation is: Where i is the current channel number. The spatiotemporal influence factor of neighboring multichannel data on the joint coefficients is calculated, and the final result is used to determine the impact of this factor on the joint coefficients. The sensitivity is low, different The difference in values ​​will ultimately be reflected in the multichannel joint coefficient. On the value, the data from each land inspection station The solution equations were rearranged into matrix form to obtain multi-channel combined land inspection data. : 。 9. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 8, characterized in that: In step S6, due to the spatiotemporal variation characteristics of the actual land surveillance data wavelet, it is necessary to process the multichannel joint land surveillance data obtained in step S5. Perform pattern matching: in, For the matched land-based seismic data, The coefficients represent the autoregressive pattern matching coefficients, and N represents the filter length in the land inspection data. It consists of multiple combined land-based seismic data; Combine data from multiple land inspection stations The combined effect of autoregressive pattern matching: in, To accommodate the number of multi-channel joint filters, the pattern matching process is expanded into an equation matrix: Since the data is spatiotemporally variable, a regularization constraint operator needs to be introduced when solving the problem. Then perform pattern matching: in: To obtain the autoregressive pattern matching coefficients The following least squares problems can be solved: in, For water testing data, For the multi-channel joint land inspection data that needs to be matched, the autoregressive pattern matching coefficients are obtained by using the conjugate gradient method or the steepest descent method.

10. The method for dual-detection merging of marine seismic data based on multi-channel pattern matching according to claim 9, characterized in that: The obtained autoregressive pattern matching coefficients are convolved with the land inspection data and then matched with the water inspection data to obtain the combined land and water inspection data: in, This represents the uplink data obtained by pattern matching and double detection merging. This indicates the downlink wave data obtained by pattern matching and double detection merging.

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