A method and apparatus for reconstructing a multi-sampling rate seismic data volume
By establishing a regular observation system in the reconstruction of irregular seismic data, performing small data volume Fourier transform and three-dimensional curvelet iterative transform, and combining iterative thresholds and data correction, the problems of large memory consumption and low efficiency in the reconstruction of irregular seismic data are solved, and efficient and high-fidelity data reconstruction is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2022-06-30
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, irregular seismic data reconstruction methods suffer from problems such as large memory consumption and low computational efficiency, which is particularly evident in 3D data reconstruction.
A multi-sampling-rate seismic data volume reconstruction method is adopted. By establishing a regular observation system, small data volumes are divided for one-dimensional Fourier transform and three-dimensional curvelet iterative transform. Combined with iterative threshold and data correction techniques, the computational efficiency and fidelity are improved.
This solves the problem of large memory usage in 3D curve transform of large data volumes, improves computational efficiency, and ensures high fidelity and quality of reconstructed data.
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Figure CN117368972B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum geophysical exploration data processing technology, and in particular to a method and apparatus for reconstructing multi-sampling-rate seismic data volumes. Background Technology
[0002] In eastern my country, rapid urban development and the proliferation of residential, industrial, and agricultural facilities have severely impacted the quality of seismic data acquisition. In western China, the complex surface and geological structures significantly affect the placement of seismic excitation and reception points. Therefore, conventional regular seismic acquisition methods are no longer sufficient to guarantee high-quality seismic signal acquisition. It is imperative to leverage the sparse optimization characteristics of irregular seismic acquisition and employ multi-rate sampling methods for economical and efficient data acquisition. However, the reconstruction method for irregular seismic data is a key constraint, and currently, no mature and effective 3D data reconstruction method has been applied in practical work areas.
[0003] Sparse transform-based reconstruction methods offer high computational efficiency and stable numerical results, making them an important research direction for reconstructing irregular seismic data. This method utilizes the sparsity of the signal in the transform domain as a constraint to establish an inversion model, and then solves it using a sparse inversion method. The properties of sparse transform are the primary factor determining the quality of this type of method. The sparser the representation of the seismic signal in the transform domain, the better the reconstruction quality. Classic sparse transforms in seismic signal processing include Fourier transform and Radon-type transforms. Recently, multi-scale transforms such as curvelet transform have been used for seismic data reconstruction and have demonstrated excellent numerical results. Multi-scale transforms can reveal more information than single-scale transforms, thus serving as powerful tools for signal analysis. Curvelet transform overcomes the limitations of traditional Fourier transform and wavelet transform, which can only represent single-scale signals or cannot sparsely represent multi-scale signals. It can represent curved or surface-shaped signals in multiple directions and with anisotropy, providing a near-optimal representation for targets with discontinuous edges. Zhang Hua et al. proposed using curvelet transform to reconstruct time slices, thereby achieving 3D seismic data reconstruction; Wang Benfeng et al. performed 2D reconstruction of effective frequency slices in the frequency domain, ultimately achieving 3D seismic data reconstruction. Cao Jingjie et al. proposed a 3D low-redundancy curvelet transform seismic data reconstruction method. Summary of the Invention
[0004] The inventors discovered that although the curvelet transform has advantages such as multi-scale capability, multi-directionality, and no need to divide the data into time windows, its redundancy (redundancy refers to the ratio of the dimension of the transform domain coefficients to the dimension of the original signal coefficients) is very high. The redundancy of the original curvelet transform in the two-dimensional case is approximately 8, while the redundancy of the three-dimensional data transform is 24–32, requiring significant memory and computation time, thus affecting the efficiency of large-scale data computation. In view of the above problems, this invention is proposed to provide a method and apparatus for reconstructing multi-sampling-rate seismic data volumes that overcomes or at least partially solves the above problems, enabling high-fidelity and high-efficiency reconstruction of multi-sampling-rate seismic data volumes.
[0005] In a first aspect, embodiments of the present invention provide a method for reconstructing seismic data volumes at multiple sampling rates, including:
[0006] Establish an empty data volume based on the observation system rules of the multi-sampling rate seismic data volume;
[0007] The traces in the empty data volume are assigned data matching the traces in the seismic data volume or initial values are set to form the initial data volume;
[0008] The initial data volume is divided into multiple smaller data volumes, and a one-dimensional Fourier transform of the time variable is performed on each smaller data volume to obtain the data volume in the fxy domain.
[0009] For each data volume in the fxy domain, the transformed data volume is obtained through three-dimensional curvilinear iterative transformation;
[0010] The transformed data volume is combined to obtain the initial reconstructed data volume;
[0011] The initial reconstructed data volume is corrected based on the earthquake data volume to obtain the reconstructed data volume.
[0012] In a second aspect, embodiments of the present invention provide a multi-sampling-rate seismic data volume reconstruction apparatus, comprising:
[0013] The empty data volume creation module is used to create an empty data volume based on the observation system rules according to the multi-sampling rate seismic data volume;
[0014] The initial data volume creation module is used to assign data matching the traces in the seismic data volume to the traces in the empty data volume or to set initial values to form the initial data volume.
[0015] The fxy domain data volume establishment module is used to divide the initial data volume into multiple smaller data volumes, perform a one-dimensional Fourier transform of the time variable on each smaller data volume, and obtain the fxy domain data volume.
[0016] The transformed data volume creation module is used to obtain the transformed data volume for each data volume in the fxy domain through three-dimensional curvilinear iterative transformation.
[0017] The initial reconstructed data body creation module is used to combine the transformed data bodies to obtain the initial reconstructed data body;
[0018] The reconstructed data volume creation module is used to correct the initial reconstructed data volume based on the seismic data volume to obtain the reconstructed data volume.
[0019] Thirdly, embodiments of the present invention provide a computer program product, including a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the above-described multi-sampling-rate seismic data volume reconstruction method.
[0020] Fourthly, embodiments of this disclosure provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described multi-sampling-rate seismic data volume reconstruction method.
[0021] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:
[0022] (1) The multi-sampling-rate seismic data volume reconstruction method provided in this embodiment of the invention first establishes an initial data volume from the multi-sampling-rate seismic data volume; the initial data volume is divided into multiple smaller data volumes, and a one-dimensional Fourier transform of the time variable is performed on each smaller data volume to obtain the data volume in the fxy domain; for each data volume in the fxy domain, a curvelet iterative transform is performed to obtain the transformed data volume; the transformed data volumes are combined to obtain the initial reconstructed data volume; the initial reconstructed data volume is corrected by the original multi-sampling-rate seismic data volume to obtain the final reconstructed data volume. The split-beam method performs a three-dimensional curvelet transform on the three-dimensional data volume in the fxy domain, solving the problem of large memory usage for three-dimensional curvelet transform on large data volumes. Simultaneously, the split-beam method also provides a basis for parallel computing, improving computational efficiency.
[0023] (2) The multi-sampling-rate seismic data volume reconstruction method provided in this embodiment of the invention, for each trace in the initial reconstructed data volume, if the trace is not to be reconstructed, the data of the trace is replaced with the data of the matching trace in the seismic data volume; if the trace is to be reconstructed, the data of the trace is corrected according to the data of the relevant trace in the seismic data volume, thus obtaining the reconstructed data volume. This ensures the fidelity of the reconstructed data.
[0024] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.
[0025] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0027] Figure 1 This is a flowchart of the multi-sampling-rate seismic data volume reconstruction method in Embodiment 1 of the present invention;
[0028] Figure 2 for Figure 1 The detailed implementation flowchart of step S12 is shown below;
[0029] Figure 3 for Figure 1 The detailed implementation flowchart of step S13 is shown below;
[0030] Figure 4 for Figure 1 The detailed implementation flowchart of step S14 is shown below;
[0031] Figure 5 This is a schematic diagram of the irregular observation system in Embodiment 2 of the present invention;
[0032] Figure 6 This is a schematic diagram of a certain arrangement in the multi-sampling-rate seismic data volume in Embodiment 2 of the present invention;
[0033] Figure 7 This is a schematic diagram of the rule-based observation system in Embodiment 2 of the present invention;
[0034] Figure 8 The data is the seismic data reconstructed using conventional methods;
[0035] Figure 9 The data is the reconstructed seismic data obtained by the method in Embodiment 2 of the present invention;
[0036] Figure 10 This is a schematic diagram of the structure of the multi-sampling-rate seismic data volume reconstruction device in an embodiment of the present invention. Detailed Implementation
[0037] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
[0038] To address the issues of high memory consumption and low efficiency in existing methods for reconstructing irregular seismic data using curve transform, this invention provides a method and apparatus for reconstructing multi-sampling-rate seismic data volumes, which can reconstruct multi-sampling-rate seismic data volumes with high fidelity and high efficiency.
[0039] Example 1
[0040] Embodiment 1 of the present invention provides a method for reconstructing seismic data volumes with multiple sampling rates, the process of which is as follows: Figure 1 As shown, it includes the following steps:
[0041] Step S11: Establish an empty data volume of the observation system rules based on the multi-sampling rate seismic data volume.
[0042] In some embodiments, a null data volume with observation system rules is established based on the line spacing, number of lines, and number of sampling points per trace of the seismic data volume with set trace spacing and multi-sampling rates. Further, the following steps may be included:
[0043] (1) Design a new regular observation system based on the irregular observation system of multi-sampling rate seismic data volume.
[0044] Based on the geological task, new regular observation system parameters were determined. An irregular observation system, W0, was established with a receiver line spacing of L meters, S receiver lines, and single-shot seismic data acquisition and recording, with N sampling points per channel. Building upon the irregular observation system W0, a new regular observation system, W1, was designed with a receiver line spacing of L meters, S receiver lines, R channel spacing, M channels per receiver line, and N sampling points per channel.
[0045] The track spacing R is set according to specific geological requirements, for example, it can be set to half of the average track spacing in W0.
[0046] (2) For each receiving line and each channel in the rule observation system W1, construct a three-dimensional array to store the reconstructed data, initialize it to 0, and obtain the empty data body of the observation system rule.
[0047] In the rule-based observation system W1, each data point is constructed into a three-dimensional array A[s][m][n]. Here, s is the s-th receiving line, where s is greater than or equal to 1 and less than or equal to S; m is the m-th receiving point on the s-th receiving line, where m is greater than or equal to 1 and less than or equal to M; and n is the number of samples collected by the m-th receiving point on the s-th receiving line.
[0048] Step S12: Assign data matching the traces in the seismic data volume to the traces in the empty data volume or set initial values to form the initial data volume.
[0049] See Figure 2 As shown, perform the following steps for each pass in the empty data body:
[0050] Step S121: Determine whether the distance between the trace and the nearest trace in the seismic data volume meets the set distance threshold.
[0051] The distance threshold can be predetermined in the following ways:
[0052] (1) Determine the sampling coefficient of the trace data in the seismic data volume with multiple sampling rates.
[0053] The coefficient used to determine whether a trace in the regular observation system W1 can use the value of the nearest trace in the irregular observation system W0 is the sampling coefficient mentioned above. The smaller the value, the more traces need to be reconstructed in the regular observation system W1; the default value is 0.5.
[0054] (2) Determine the distance threshold based on the adopted coefficient and the channel spacing of the empty data volume.
[0055] The distance threshold is determined by multiplying the coefficient and the channel spacing of the empty data volume.
[0056] Determining whether the distance between a trace and the nearest trace in the seismic data volume meets a set distance threshold can be done by determining whether the distance between the trace and the nearest trace in the seismic data volume is not greater than the set distance threshold.
[0057] If step S121 is determined to be yes, proceed to step S122; if step S121 is determined to be no, proceed to step S123.
[0058] Step S122: Assign the data of the nearest trace in the seismic data volume to that trace in the empty data volume.
[0059] Assign the values of the matching traces in the irregular observation system W0 to the corresponding three-dimensional array A[s][m][n] in the regular observation system W1.
[0060] Step S123: Assign values to this channel in the empty data body according to the set initial value.
[0061] The value of the matching trace in the rule observation system W1 can be set to 0, that is, the value of the three-dimensional array A[s][m][n] of the corresponding trace in the empty data volume is 0.
[0062] Furthermore, the paths assigned initial values are used as paths to be reconstructed.
[0063] Step S13: Divide the initial data volume into multiple smaller data volumes, and perform a one-dimensional Fourier transform of the time variable on each smaller data volume to obtain the data volume in the fxy domain.
[0064] See Figure 3 As shown, dividing the initial data volume into multiple smaller data volumes according to the wire harness can include the following sub-steps:
[0065] Step S131: Divide the initial data volume into multiple smaller data volumes according to the set number of lines to form the first set.
[0066] Specifically, it can include starting from the first receiving line of the initial data body, with each H lines forming a small data body, and each shot being divided into S / H (S is the number of lines in the initial data body) small data bodies, forming the first set.
[0067] In the above case, H is greater than 1 and less than S, where S is an integer multiple of H.
[0068] Step S132: For two adjacent small data volumes in the first set, take the adjacent boundary of the two small data volumes as the center, divide a small data volume with a set number of lines from the initial data volume, and fill the resulting small data volume into the second set.
[0069] For two adjacent small data volumes in the first set, H small data volumes with receiving lines are also constructed, centered on the boundary line where the two adjacent small data volumes intersect. This results in (S / H)-1 small data volumes, forming the second set. The two sets together contain a total of (2·S / H)-1 data volumes.
[0070] Step S133: The first set and the second set constitute a small data volume set.
[0071] Perform a one-dimensional Fourier transform on each path in the small data volume using the time variable t to obtain the data volume in the fxy domain.
[0072] Step S14: For each data volume in the fxy domain, obtain the transformed data volume through three-dimensional curved wave iterative transformation.
[0073] See Figure 4 As shown, the following steps are performed on the data body for each fxy field:
[0074] Step S141: Perform a three-dimensional curvilinear transformation on the data volume in the fxy domain to obtain a set of curvilinear coefficients. Determine the maximum and minimum values of the iteration threshold based on the maximum and average curvilinear coefficients in the set of curvilinear coefficients.
[0075] Perform a three-dimensional curvature transformation on the data volume in the fxy domain, calculate the curvature coefficients at each scale and angle, and obtain the curvature coefficient set.
[0076] Based on the maximum curvature coefficient C in the curvature coefficient set maxo and average curvature coefficient The maximum value C of the iteration threshold is determined by the following formula (1). max and minimum value C min :
[0077]
[0078] In formula (1), the coefficient α is set according to the signal-to-noise ratio of the seismic data volume. The higher the signal-to-noise ratio, the smaller the value of α is set, and the lower the signal-to-noise ratio, the larger the value of α is set. α is greater than 0 and less than or equal to 1, and can be set to 0.95 by default.
[0079] Step S142: Determine the iteration threshold parameter based on the current iteration number and the maximum and minimum values of the iteration threshold. Replace the curve coefficients in the current curve coefficient set that are less than the iteration threshold parameter with the set value to obtain a new curve coefficient set. Perform inverse curve transformation on the new curve coefficient set to obtain the shot collection data.
[0080] Based on the current iteration number d and the maximum value of the iteration threshold Cmax and minimum value C min The iteration threshold parameter σ is determined by the following formula (2):
[0081]
[0082] In formula (2), media(·) is the median function, s is the decomposition scale of the multiscale transformation, D is the total number of iterations, d = 1, 2, ..., D.
[0083] The total number of iterations D is set according to the signal-to-noise ratio of the data. For data with a high signal-to-noise ratio, the number of iterations is less, and vice versa. The default value is 30 iterations.
[0084] Replace the curve coefficients in the current curve coefficient set that are less than the iteration threshold parameter with a set value. Furthermore, the curve coefficients in the current curve coefficient set that are less than the iteration threshold parameter can be replaced with 0.
[0085] Step S143: Determine whether the ratio of the root mean square amplitude of the shot gather data to the root mean square amplitude of the seismic data volume is greater than the set ratio threshold.
[0086] The ratio threshold is set according to the required accuracy, for example, it can be set to 0.95 to ensure that the reconstructed data has a high fidelity.
[0087] If step S143 is determined to be yes, proceed to step S145; if step S143 is determined to be no, proceed to step S144.
[0088] Step S144: Increment the current iteration number by 1, perform a three-dimensional curvelet transform on the current shot gather data, and obtain a new set of curvelet coefficients.
[0089] After step S144, return to step S142 and continue until the set conditions are met.
[0090] The set condition can be that step S143 determines "yes", the number of iterations reaches the total number of iterations, or other conditions.
[0091] Step S145: Determine the current gun gathering data as the transformed data volume of the fxy domain.
[0092] Step S15: Combine the transformed data volumes to obtain the initial reconstructed data volume.
[0093] Obtain the data of each path of each small data volume in the first set except for the adjacent boundaries, and the data of the adjacent boundaries in the second set, and combine them into the initial reconstructed data volume.
[0094] Furthermore, in the first set of small data volumes, except for the first and last ones which need to retain the reconstructed data on the top and bottom sides, the two receiving lines of the remaining boundaries all use the data reconstructed from the small data volumes in the second set, thus combining them into the initial reconstructed data volume.
[0095] Step S16: Correct the initial reconstruction data volume based on the seismic data volume to obtain the reconstruction data volume.
[0096] In some embodiments, the method may include replacing the data of non-traces to be reconstructed in the initial reconstruction data volume with the data of matching traces in the seismic data volume; and for each trace to be reconstructed in the initial reconstruction data volume, correcting the trace data based on the data of the relevant traces in the seismic data volume.
[0097] That is, for traces not to be reconstructed, the original seismic data is retained; for traces to be reconstructed, the final reconstructed data volume is obtained based on the reconstructed data and constrained by the original seismic data.
[0098] Furthermore, the data in the relevant trace is corrected based on the data in the seismic data volume, specifically including:
[0099] Step S161: Determine the search range based on the location of the track to be reconstructed, and determine the non-tracks to be reconstructed within the search range.
[0100] The search range is defined by a circle centered on the location of the track to be reconstructed and with a set radius, which is determined based on the line spacing.
[0101] Furthermore, the position of the track is its projection onto the horizontal plane. The radius can typically be set to two receiver line distances.
[0102] Step S162: Based on the maximum absolute value of the amplitude of each non-reconstructable trace within the search range, determine the maximum absolute value of the amplitude at the location of the trace to be reconstructed by distance interpolation.
[0103] Step S163: Determine whether the ratio of the determined maximum amplitude absolute value to the maximum amplitude absolute value of the channel to be reconstructed meets the set conditions.
[0104] Based on the determined absolute value of the maximum amplitude Max mo The maximum absolute value of the amplitude of the track to be reconstructed (Max) m ratio The judgment coefficient η is determined by satisfying the following formula (3):
[0105]
[0106] Determine if η is less than a set threshold value. Set a threshold value, for example, 0.1.
[0107] If yes, determine that the amplitude data of the channel to be reconstructed does not need to be corrected; otherwise, proceed to step S164.
[0108] Step S164: Correct the amplitude of the channel to be reconstructed using the ratio as a correction coefficient.
[0109] The corrected amplitude can be obtained by multiplying the ratio by the amplitude of the channel to be reconstructed.
[0110] The multi-sampling-rate seismic data volume reconstruction method provided in this invention first establishes an initial data volume from multi-sampling-rate seismic data volumes; the initial data volume is divided into multiple smaller data volumes, and a one-dimensional Fourier transform of the time variable is performed on each smaller data volume to obtain a data volume in the fxy domain; for each data volume in the fxy domain, a curvelet iterative transform is performed to obtain a transformed data volume; the transformed data volumes are combined to obtain an initial reconstructed data volume; the initial reconstructed data volume is then corrected using the original multi-sampling-rate seismic data volume to obtain the final reconstructed data volume. The method uses a split-beam approach to perform a three-dimensional curvelet transform on the three-dimensional data volume in the fxy domain, solving the problem of large memory usage for three-dimensional curvelet transforms on large data volumes. Simultaneously, the split-beam approach provides a basis for parallel computing, improving computational efficiency.
[0111] For each trace in the initial reconstructed data volume, if the trace is not to be reconstructed, its data is replaced with data from a matching trace in the seismic data volume; if the trace is to be reconstructed, its data is corrected based on data from relevant traces in the seismic data volume, thus obtaining the reconstructed data volume. This ensures the fidelity of the reconstructed data.
[0112] By constructing iterative threshold parameters using the combined maximum and average curvature coefficients, noise can be accurately suppressed to a certain extent, improving the quality of reconstructed data. The completion of reconstruction is determined based on the root mean square amplitude relationship between the data before and after reconstruction, ensuring the reconstructed data remains undistorted. The divided data volumes are re-integrated, removing traces with low reliability at the reconstructed data boundaries, further improving the quality of the reconstructed data. The amplitude values of the reconstructed data are judged using the original data from adjacent traces, further ensuring the fidelity of the reconstructed data. Practical testing has demonstrated that this algorithm has good reliability and practicality, meeting the needs of exploration and production.
[0113] Example 2
[0114] Embodiment 2 of the present invention provides an application example of a multi-sampling-rate seismic data volume reconstruction method.
[0115] The core of this invention is to first design a new regular observation system based on the irregular observation system. The parameters of the new regular observation system are determined according to the geological task. A three-dimensional array is constructed for each receiving line and each trace in the new regular observation system. Based on the distance between the nearest receiving point in the irregular observation system and the new regular observation system, it is determined whether to assign a value to the corresponding receiving point in the new observation system. Secondly, the new regular observation system is divided according to line bundles, constructing multiple three-dimensional data volumes. A one-dimensional Fourier transform of the time variable t is performed on each trace in each three-dimensional volume data to obtain the volume data in the fxy domain. Then, the new regular observation... The system performs a three-dimensional curvelet transform on the three-dimensional data volume in the fxy domain, constructs an iteration threshold parameter, and performs a three-dimensional curvelet transform on each small data volume. Coefficients whose curvelet transform coefficients are less than the iteration threshold parameter are set to 0. The newly set curvelet coefficients are then subjected to an inverse transform to obtain the reconstructed data. The reconstruction is then terminated based on the number of iterations and the ratio of the root mean square amplitude of the seismic traces before and after reconstruction. The reconstructed small data volumes are then recombined, and data at the boundaries of the reconstructed data volumes are removed. Finally, the amplitude values of the reconstructed data are evaluated using the original data from adjacent traces, and abnormal amplitudes are corrected to improve the fidelity of the reconstructed data.
[0116] 1) Based on the geological task, design an irregular observation system W0, such as... Figure 5 As shown, the receiver line spacing is 160m, and the number of receiver lines is 42. The trace spacing of this observation system ranges from 10 to 79m, with an average trace spacing of 40m. It meets the μ value irregular layout requirement. Single-shot seismic data was excited and acquired, and the file number is 11. The shot gather data contains 9538 traces. Figure 6 This is the 21st permutation, with 1750 sampling points per track and a sampling time of 4ms. Based on the designed irregular observation system W0, a new regular observation system W1 is designed, such as... Figure 7The observation system W0 has a receiver line spacing of 160 meters, 42 receiver lines, a channel spacing of 20 meters, 480 channels per receiver line, and 1750 sampling points per channel.
[0117] 2) In the rule-based observation system W1, each data point is constructed into a three-dimensional array A[s][m][n]. Here, s is the s-th receiving line, s is greater than or equal to 1 and less than or equal to 42, m is the m-th receiving point on the s-th receiving line, m is greater than or equal to 1 and less than or equal to 480, and n is the number of sample points collected by the m-th receiving point on the s-th receiving line, n is equal to 1750.
[0118] 3) Determine the corresponding trace of the irregular observation system W0 for each trace in the regular observation system W1 according to the actual position coordinates. If the distance difference between the two is less than or equal to 0.5·R = 10 meters, then assign the trace of the irregular observation system W0 to the corresponding three-dimensional array A[s][m][n] in the regular observation system W1. If the distance difference between the two is greater than 10 meters, then assign the value of the three-dimensional array A[s][m][n] of the corresponding trace in the regular observation system W1 to zero, which is the trace to be reconstructed. Calculate the root mean square amplitude of the existing value receiving point as E0 = 100058.
[0119] 4) Starting from the first receiving line, each H = 21 lines constitute a data volume. Each shot is divided into S / H = 2 data volumes, and the resulting set is denoted as Ω0. For each boundary line where two data volumes intersect, H data volumes for receiving lines are also constructed centered on the boundary line, resulting in S / H-1 = 1 data volume, denoted as Ω1. The two sets together contain a total of 2 × S / H-1 = 3 data volumes.
[0120] 5) Perform a one-dimensional Fourier transform on each of the three-dimensional data volumes in step 3) using the time variable t to obtain the three-dimensional volume data in the fxy domain.
[0121] 6) Perform a three-dimensional curvature transform on the fxy domain three-dimensional data volume from step 5), calculate the curvature coefficients for each scale and angle, and obtain the maximum and minimum curvature coefficients as C. maxo =939113 and C mino =0.07, average all obtained curve coefficients.
[0122] 7) Based on the average value of the curve coefficients in step 6). The maximum and minimum values of the design iteration threshold are C. max =α·C maxo and Here, α = 0.95.
[0123] 8) Based on the design of C in step 7) max and C minConstruct iterative threshold parameters Here, d represents the d-th iteration, where d is greater than or equal to 1 and less than or equal to D, and D is the total number of iterations, which is set to 30. s is the decomposition scale of the multiscale transformation, and median(·) is the median function.
[0124] 9) Perform a three-dimensional curvelet transform on the 2×S / H-1 = 3 data volumes constructed in step 4). Based on the iteration threshold parameter designed in step 8), set the coefficients of the curvelet transform that are less than the iteration threshold parameter to 0 each time. Then, perform an inverse transform on the newly set curvelet coefficients to obtain the reconstructed shot gather data. After the 11th reconstruction, the root mean square amplitude of all traces in each data volume is E. 11 =29277.
[0125] 10) Calculate the ratio of the root mean square amplitude of the reconstructed data volume to the original data volume based on steps 3) and 9), respectively. If the β of a data volume is greater than the given threshold parameter 0.95, the calculation stops; otherwise, the next curvelet transform continues. At the 26th iteration, E... 26 =96155.74, The calculation stops when β exceeds the given threshold parameter of 0.95. Finally, the reconstructed seismic data is obtained.
[0126] 11) For the reconstructed single-shot data, except for the first and last data volumes which need to retain the reconstructed data on the top and bottom sides, the two receiving lines on the other boundaries of the set Ω0 use the data reconstructed from the data volumes in Ω1, thus combining them into the entire reconstructed data.
[0127] 12) Process the reconstructed channels integrated in step 11). Replace the receiver points in the non-reconstructed channel set with the original values. Reconstruct the data in the reconstructed channel set, using channel 127 as the reconstructed data. The absolute value of the maximum amplitude of channel m=127 is Max. m =3702. Construct a circle with the reconstructed track m=127 as the center and radius ρ=320. Calculate the absolute value of the maximum amplitude of each track in the set of tracks not to be reconstructed within the circle. Simultaneously calculate the distance between each track in the set of tracks not to be reconstructed and the reconstructed track m=127. Use spatial interpolation to calculate the maximum absolute amplitude of the reconstructed track m=127. Calculate the ratio of the reconstructed amplitude to the maximum absolute amplitude interpolated in adjacent channels. If η is less than the given threshold value of 0.1, the value of the reconstructed trace m=127 remains unchanged. Each reconstructed trace is processed according to the above method to obtain new processed seismic data. The data is reconstructed using the conventional method with ε set to 0.09. The reconstructed seismic data is as follows: Figure 8As shown in the figure, conventional methods can reconstruct data quite well. The seismic data reconstructed using the technology of this invention is shown in the figure. Figure 9 As shown, with Figure 8 In comparison, the new method reconstructs data with clearer phase axes of reflected waves, better continuity, and wave group relationships, amplitudes, and phases that are almost identical to the actual acquired data. Furthermore, there are no time differences or energy abrupt changes, essentially achieving the effect of actual acquisition.
[0128] Based on the inventive concept of this invention, embodiments of this invention also provide a multi-sampling-rate seismic data volume reconstruction device, the structure of which is as follows: Figure 10 As shown, it includes:
[0129] The empty data volume creation module 101 is used to create an empty data volume based on the observation system rules according to the multi-sampling rate seismic data volume;
[0130] The initial data volume establishment module 102 is used to assign data matching the traces in the seismic data volume to the traces in the empty data volume or to set initial values to form an initial data volume.
[0131] The fxy domain data volume establishment module 103 is used to divide the initial data volume into multiple small data volumes, perform a one-dimensional Fourier transform of the time variable on each small data volume, and obtain the fxy domain data volume.
[0132] The transformed data volume establishment module 104 is used to obtain the transformed data volume for each data volume in the fxy domain through three-dimensional curvilinear iterative transformation.
[0133] The initial reconstructed data body establishment module 105 is used to combine the transformed data body to obtain the initial reconstructed data body;
[0134] The reconstructed data body establishment module 106 is used to correct the initial reconstructed data body based on the seismic data body to obtain the reconstructed data body.
[0135] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0136] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer program product, including a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the above-described multi-sampling rate seismic data volume reconstruction method.
[0137] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.
[0138] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the specific order or hierarchy described.
[0139] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.
[0140] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.
[0141] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.
[0142] 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. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.
[0143] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first” and “second” are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
Claims
1. A method for reconstructing seismic data volumes at multiple sampling rates, characterized in that, include: Establish an empty data volume based on the observation system rules of the multi-sampling rate seismic data volume; The traces in the empty data volume are assigned data matching the traces in the seismic data volume or initial values are set to form the initial data volume; The initial data volume is divided into multiple smaller data volumes according to a set number of lines, forming a first set. For two adjacent smaller data volumes in the first set, a smaller data volume with the set number of lines is divided from the initial data volume, centered on the adjacent boundary of the two smaller data volumes. The resulting smaller data volume is then filled into a second set. The first set and the second set constitute a set of smaller data volumes. A one-dimensional Fourier transform of the time variable is performed on each smaller data volume to obtain the data volume in the fxy domain. For each data volume in the fxy domain, the transformed data volume is obtained through three-dimensional curvilinear iterative transformation; The transformed data volume is combined to obtain the initial reconstructed data volume; The initial reconstructed data volume is corrected based on the earthquake data volume to obtain the reconstructed data volume.
2. The method as described in claim 1, characterized in that, The empty data volume for establishing observation system rules based on multi-sampling-rate seismic data volume specifically includes: Based on the set trace spacing and the line spacing, number of lines, and number of sampling points per trace of the seismic data volume with multiple sampling rates, a regular empty data volume of the observation system is established.
3. The method as described in claim 1, characterized in that, Assigning data to the traces in the empty data volume that match the data in the seismic data volume or setting initial values specifically includes: For each trace in the empty data volume, determine whether the distance between the trace and the nearest trace in the seismic data volume meets a set distance threshold; If so, assign the data of the nearest trace in the seismic data volume to that trace in the empty data volume; If not, assign the initial value to the channel in the empty data body.
4. The method as described in claim 3, characterized in that, The distance threshold is predetermined in the following manner: Determine the adoption coefficients for trace data in a multi-sampling-rate seismic data volume; The distance threshold is determined based on the adoption coefficient and the channel spacing of the empty data volume.
5. The method as described in claim 1, characterized in that, The process of combining the transformed data volumes to obtain the initial reconstructed data volume specifically includes: Obtain the data of each channel of each small data volume in the first set except for the adjacent boundaries, and the data of the adjacent boundaries in the second set, and combine them into an initial reconstructed data volume.
6. The method as described in claim 1, characterized in that, The transformation of the data volume for each fxy domain is obtained through a three-dimensional curvilinear iterative transformation, specifically including performing the following steps for each fxy domain data volume: A three-dimensional curvature transformation is performed on the data volume in the fxy domain to obtain a set of curvature coefficients. The maximum and minimum values of the iteration threshold are determined based on the maximum and average curvature coefficients in the set of curvature coefficients. Based on the current iteration number and the maximum and minimum values of the iteration threshold, the iteration threshold parameter is determined. Curvature coefficients in the current set of curve coefficients that are less than the iteration threshold parameter are replaced with the set value to obtain a new set of curve coefficients. The shot collection data is obtained by performing inverse curve transformation on the new set of curve coefficients. Determine whether the ratio of the root mean square amplitude of the shot gather data to the root mean square amplitude of the seismic data volume is greater than a set ratio threshold. If so, the current gun gathering data is determined as the transformed data volume of the fxy domain; If not, increment the current iteration count by 1, perform a three-dimensional curvelet transform on the current shot gather data to obtain a new set of curvelet coefficients, and return to the step of determining the iteration threshold based on the current iteration count and the maximum and minimum values of the iteration threshold, until the set conditions are met.
7. The method as described in claim 6, characterized in that, The step of determining the maximum and minimum values of the iteration threshold based on the maximum and average curvelet coefficients in the set of curvelet coefficients specifically includes: Based on the maximum curvelet coefficient in the set of curvelet coefficients and average curvature coefficient The maximum value of the iteration threshold is determined by the following formula (1). and minimum value : （1）； In formula (1), the coefficients The setting is based on the signal-to-noise ratio (SNR) of the seismic data volume; the higher the SNR, the lower the setting. The smaller the value, the lower the signal-to-noise ratio. The larger the value, the better.
8. The method as described in claim 6, characterized in that, The step of determining the iteration threshold parameter based on the current iteration number and the maximum and minimum values of the iteration threshold specifically includes: Based on the current iteration number d and the maximum value of the iteration threshold and minimum value The iteration threshold parameter is determined by the following formula (2). : （2）； In formula (2), , Let S be the median function, S be the decomposition scale of the multiscale transformation, and D be the total number of iterations, where d = 1, 2, ..., D.
9. The method as described in claim 6, characterized in that, The step of replacing the curvelet coefficients in the current curvelet coefficient set that are less than the iteration threshold parameter with a set value specifically includes: Replace the curve coefficients in the current curve coefficient set that are less than the iteration threshold parameter with 0.
10. The method as described in claim 1, characterized in that, After setting initial values for the trace assignments in the empty data body, the method further includes: The path with the initial value set is designated as the path to be reconstructed; correspondingly... The step of correcting the initial reconstructed data volume based on the seismic data volume specifically includes: Replace the data of the non-reconstructable traces in the initial reconstructed data volume with the data of the matching traces in the seismic data volume; For each trace to be reconstructed in the initial reconstructed data volume, the data of that trace is corrected based on the data of the relevant traces in the seismic data volume.
11. The method as described in claim 10, characterized in that, The step of correcting the trace data based on the data of the relevant trace in the seismic data volume specifically includes: The search range is determined based on the location of the channel to be reconstructed, and the channels not to be reconstructed within the search range are then identified. Based on the maximum absolute value of the amplitude of each non-reconstructable channel within the search range, the maximum absolute value of the amplitude at the location of the channel to be reconstructed is determined by distance interpolation. Determine whether the ratio of the determined absolute value of the maximum amplitude to the absolute value of the maximum amplitude of the channel to be reconstructed meets the set conditions; If not, the amplitude of the channel to be reconstructed is corrected using the ratio as a correction factor.
12. The method as described in claim 11, characterized in that, The step of determining the search range based on the location of the channel to be reconstructed specifically includes: The search range is defined by a circle centered on the position of the track to be reconstructed and with a set radius, which is determined based on the line spacing.
13. The method as described in claim 11, characterized in that, Whether the ratio of the determined maximum amplitude absolute value to the maximum amplitude absolute value of the channel to be reconstructed meets the set conditions specifically includes: Based on the determined absolute value of the maximum amplitude The absolute value of the maximum amplitude of the channel to be reconstructed ratio The judgment coefficient is determined by satisfying the following formula (3). : （3）； judge Is it less than the set threshold value? 14. A multi-sampling-rate seismic data volume reconstruction device, characterized in that, include: The empty data volume creation module is used to create an empty data volume based on the observation system rules according to the multi-sampling rate seismic data volume; The initial data volume creation module is used to assign data matching the traces in the seismic data volume to the traces in the empty data volume or to set initial values to form the initial data volume. The fxy domain data volume establishment module is used to divide the initial data volume into multiple smaller data volumes according to a set number of lines, forming a first set; for two adjacent smaller data volumes in the first set, a smaller data volume with the set number of lines is divided from the initial data volume, centered on the adjacent boundary of the two smaller data volumes, and the resulting smaller data volume is filled into a second set; the first set and the second set constitute a set of smaller data volumes; a one-dimensional Fourier transform of the time variable is performed on each smaller data volume to obtain the data volume in the fxy domain; The transformed data volume creation module is used to obtain the transformed data volume for each data volume in the fxy domain through three-dimensional curvilinear iterative transformation. The initial reconstructed data body creation module is used to combine the transformed data bodies to obtain the initial reconstructed data body; The reconstructed data volume creation module is used to correct the initial reconstructed data volume based on the seismic data volume to obtain the reconstructed data volume.
15. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the multi-sampling-rate seismic data volume reconstruction method as described in any one of claims 1 to 13.