Combined regularization processing method and device for continuous data, electronic equipment and medium

Abstract

The invention discloses a combined regularization processing method and device for contiguous data, electronic equipment and a medium. The method comprises the following steps: sorting data to a common offset domain, and outputting empty channel data; performing three-dimensional interpolation on the empty channel data by adopting a matching pursuit Fourier transform interpolation technology, combining output data with common offset domain data, and restarting CMP domain sorting to obtain new sorted data; reconstructing an observation system based on the new sorting data, redesigning system parameters, and outputting trace header data; according to the newly sorted data and the trace header data, adopting a matching pursuit Fourier transform interpolation technology to realize five-dimensional spatial interpolation; and processing and offsetting the gather data after five-dimensional interpolation, and outputting final gather data. The method is applied to contiguous research areas of acquisition, direction angles, coverage times and observation systems in different years, the phenomenon of non-uniform pre-stack gather energy is solved, offset arc drawing is reduced, pre-stack offset imaging is more facilitated, and a high-quality data foundation is laid for subsequent offset.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration seismic data processing, and more specifically, to a combined regularized processing method, device, electronic equipment and medium for contiguous data. Background Art

[0002] With the increasing difficulty of exploration and development, as well as the deepening of exploration knowledge and the development of technology, the requirements for the accuracy of reservoir prediction are getting higher and higher. Therefore, it is necessary to further improve the ability to identify fractures and tiny cracks, which requires high quality of seismic data. Now more research areas are composed of two or more three-dimensional blocks. Faced with data from different ages, different acquisition directions, different coverage times, and different observation systems, data processing faces the consistency problems of time difference, energy, frequency, etc. of contiguous data.

[0003] At present, many scholars have proposed many different methods for data regularization: interpolation regularization based on signal analysis theory, data reconstruction method based on compressed sensing theory, etc., which has enabled the regularization technology to continue to develop from three-dimensional data regularization to five-dimensional data regularization.

[0004] There are differences in key parameters such as maximum offset, coverage times, aspect ratio and facets between contiguous work areas, which will cause the diffraction energy to not interfere well with each other during the migration process, resulting in migration arcs and energy unevenness. Therefore, how to eliminate the differences in key parameters between contiguous work areas based on such complex contiguous data is a difficult point.

[0005] At present, it is still necessary to develop a combined regularization processing method based on complex contiguous data.

[0006] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0007] The present invention proposes a combined regular processing method, device, electronic device and medium for contiguous data, which can solve the energy consistency problem of complex contiguous pieces, reduce offset arcing, obtain a track gather with spatial sampling rules, and improve the signal-to-noise ratio of offset stacking records.

[0008] In a first aspect, an embodiment of the present disclosure provides a combined regularized processing method for contiguous data, including:

[0009] Sort the data into the common offset domain and output the empty track data;

[0010] Using matching pursuit Fourier transform interpolation technology to perform three-dimensional interpolation on the empty channel data, merging the output data with the input common offset domain data, restarting CMP domain sorting, and obtaining new sorting data;

[0011] Reconstruct the observation system based on the new sorting data, redesign the system parameters, and output the trace header data;

[0012] According to the newly sorted data and the header data, a matching pursuit Fourier transform interpolation technique is adopted to realize five-dimensional space interpolation;

[0013] The gather data after five-dimensional interpolation are processed and offset, and the final gather data are output.

[0014] As a specific implementation of the embodiment of the present disclosure, outputting the empty channel data includes:

[0015] Statistical input common offset data x i ,y i The coordinate trajectory of the vacant data is interpolated and the empty track data is output on a regular grid.

[0016] As a specific implementation of the embodiment of the present disclosure, the empty channel data is:

[0017]

[0018] Where W is the amplitude weighting factor and D′(x,y,t) is the empty channel data.

[0019] As a specific implementation of the embodiment of the present disclosure, the system parameters include the number of coverage times and the coordinate positions of the shot inspection points.

[0020] As a specific implementation manner of the embodiment of the present disclosure, the five-dimensional space is a line domain, a track domain, an offset domain, an azimuth domain and a time domain.

[0021] As a specific implementation of the embodiment of the present disclosure, the matching pursuit Fourier transform interpolation technology is:

[0022] Under the condition of irregular observation system, the Fourier forward transform is:

[0023] F(k,ω)=Φ H f(x,ω)

[0024] The inverse Fourier transform is:

[0025] f(x,ω)=ΦF(k,ω)

[0026] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data;

[0027] The data in the time-frequency domain is discretely Fourier transformed into the wavenumber domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, and this component data is subtracted to obtain a new data body. Through repeated iterations until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0028] As a specific implementation method of the embodiment of the present disclosure, the maximum Fourier coefficient is found by the following formula to estimate the sparse spectrum:

[0029]

[0030] The new data body is obtained by iterative update:

[0031]

[0032] Among them, p is the element with the largest inner product, F is the spectrum, and j is the number of iterations.

[0033] In a second aspect, the embodiment of the present disclosure further provides a combined regularization processing device for contiguous data, comprising:

[0034] The sorting module sorts the data into the common offset domain and outputs the air channel data;

[0035] A secondary sorting module uses a matching pursuit Fourier transform interpolation technique to perform three-dimensional interpolation on the air channel data, merges the output data with the input common offset domain data, restarts CMP domain sorting, and obtains new sorting data;

[0036] A reconstruction module, which reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs the trace header data;

[0037] A five-dimensional space interpolation module, which realizes five-dimensional space interpolation by using matching pursuit Fourier transform interpolation technology according to the newly sorted data and the header data;

[0038] The processing module processes and offsets the gather data after five-dimensional interpolation and outputs the final gather data.

[0039] As a specific implementation of the embodiment of the present disclosure, outputting the empty channel data includes:

[0040] Count the input common offset data x i ,y i The coordinate trajectory of the vacant data is interpolated and the empty track data is output on a regular grid.

[0041] As a specific implementation manner of an embodiment of the present disclosure, the empty trace data is:

[0042]

[0043] where W is the amplitude weighting factor and D′(x, y, t) is the empty trace data.

[0044] As a specific implementation manner of an embodiment of the present disclosure, the system parameters include the number of coverage times and the coordinate positions of shot points and receivers.

[0045] As a specific implementation manner of an embodiment of the present disclosure, the five-dimensional space is the line domain, the trace domain, the offset domain, the azimuth domain, and the time domain.

[0046] As a specific implementation manner of an embodiment of the present disclosure, the matching pursuit Fourier transform interpolation technique is:

[0047] For the case of an irregular acquisition system, the forward Fourier transform is:

[0048] F(k, ω) = Φ H f(x, ω)

[0049] The inverse Fourier transform is:

[0050] f(x, ω) = ΦF(k, ω)

[0051] where x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x, ω) is the time-frequency domain data, and F(k, ω) is the wave number-frequency domain data;

[0052] The data in the time-frequency domain is discretely Fourier transformed into the wave number domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, the data of this component is subtracted to obtain a new data volume, and through repeated iteration until the remaining value is negligible, the finally obtained sparse spectrum is inverse Fourier transformed and output to the expected spatial position.

[0053] As a specific implementation manner of an embodiment of the present disclosure, the maximum Fourier coefficient is found by the following formula to estimate the sparse spectrum:

[0054]

[0055] The new data volume is obtained by iterative update through the following formula:

[0056]

[0057] where p is the element with the maximum inner product, F represents the spectrum, and j is the number of iterations.

[0058] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0059] A memory storing executable instructions;

[0060] A processor runs the executable instructions in the memory to implement the combined regularization processing method of the contiguous data.

[0061] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for regularizing the combination of contiguous data is implemented.

[0062] Its beneficial effects are:

[0063] The present invention is aimed at the continuous three-dimensional exploration area. Based on the data with large acquisition span, coverage times, aspect ratio, azimuth and other differences, the energy difference and offset arcing problems are solved through the combination of regularization technology to improve the data signal-to-noise ratio. The current exploration target has developed from clastic rocks to the Ordovician and then to the Cambrian. For ultra-deep layer processing, especially in low signal-to-noise ratio and low coverage areas, after data regularization through the combination of regularization technology, the signal-to-noise ratio of the data will be improved, the inside story of the ultra-deep layer will be clearer, and the profile phase axis will be improved, which is more conducive to subsequent exploration and development research.

[0064] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0066] Figure 1 A flowchart showing the steps of a method for combining and regularizing contiguous data according to an embodiment of the present invention.

[0067] Figure 2 A schematic diagram of data regularization of a common offset hole filling type according to an embodiment of the present invention is shown.

[0068] Figure 3 A schematic diagram of data regularization based on observation system reconstruction according to an embodiment of the present invention is shown.

[0069] Figure 4a and Figure 4b Schematic diagrams showing comparison of regularized migration profiles of hole filling and regularized reconstruction of an observation system according to an embodiment of the present invention are shown respectively.

[0070] Figure 5a and Figure 5b Schematic diagrams of regularized front and rear single offset comparisons according to an embodiment of the present invention are respectively shown.

[0071] Figure 6a and Figure 6b Schematic diagrams showing comparison of regularized front and rear offset profiles according to an embodiment of the present invention are shown respectively.

[0072] Figure 7 A block diagram of a combined regularization processing device for contiguous data according to an embodiment of the present invention is shown.

[0073] Description of reference numerals:

[0074] 201. Sorting module; 202. Secondary sorting module; 203. Reconstruction module; 204. Five-dimensional space interpolation module; 205. Processing module. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0076] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0077] Example 1

[0078] Figure 1 A flowchart showing the steps of a method for combining and regularizing contiguous data according to an embodiment of the present invention.

[0079] like Figure 1As shown, the combined regularized processing method of the contiguous data includes: step 101, sorting the data into a common offset domain and outputting empty track data; step 102, using matching pursuit Fourier transform interpolation technology to perform three-dimensional interpolation on the empty track data, merging the output data with the input common offset domain data, restarting CMP domain sorting, and obtaining new sorted data; step 103, reconstructing the observation system based on the new sorted data, redesigning the system parameters, and outputting the header data; step 104, using matching pursuit Fourier transform interpolation technology according to the new sorted data and the header data to achieve five-dimensional space interpolation; step 105, processing and offsetting the five-dimensional interpolated track data, and outputting the final track data.

[0080] In one example, outputting empty channel data includes:

[0081] Count the input common offset data x i ,y i The coordinate trajectory of the vacant data is interpolated and the empty track data is output on a regular grid.

[0082] In one example, the empty channel data is:

[0083]

[0084] Where W is the amplitude weighting factor and D′(x,y,t) is the empty channel data.

[0085] In one example, the system parameters include coverage times and shot check point coordinate positions.

[0086] In one example, the five-dimensional space is line domain, track domain, offset domain, azimuth domain, and time domain.

[0087] In one example, the matching pursuit Fourier transform interpolation technique is:

[0088] Under the condition of irregular observation system, the Fourier forward transform is:

[0089] F(k,ω)=Φ H f(x,ω)

[0090] The inverse Fourier transform is:

[0091] f(x,ω)=ΦF(k,ω)

[0092] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data;

[0093] The data in the time-frequency domain is discretely Fourier transformed into the wavenumber domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, and this component data is subtracted to obtain a new data body. Through repeated iterations until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0094] In one example, the sparse spectrum is estimated by finding the maximum Fourier coefficient:

[0095]

[0096] The new data body is obtained by iterative update:

[0097]

[0098] Among them, p is the element with the largest inner product, F is the spectrum, and j is the number of iterations.

[0099] Specifically, data regularization is to fill in the missing reflection point information through interpolation based on the existing data. The difference in the time-distance characteristics of the event axis in each gather determines the effect of the interpolation and regularization methods. Therefore, it is necessary to examine the combination of different methods and different gathers to achieve the best combination effect of seismic data interpolation and regularization, while also achieving appropriate denoising.

[0100] The data regularization of common offset hole filling is to count the information of missing CMP reflection points and fill only the positions where CMP reflection points are missing in the bin through interpolation. This method can effectively solve the hole problem caused by irregular spatial sampling and improve the signal-to-noise ratio of the data.

[0101] The data regularization technology based on observation system reconstruction is to redesign the observation system and realize data interpolation and regularization based on anti-aliasing Fourier transform. The advantages of this method are that it is applicable to any irregular observation system, severe aliasing data, and complex steep-angle data. At the same time, it can realize spatial interpolation in line domain, point domain, time domain, shot offset domain, and azimuth domain to achieve the purpose of data regularization.

[0102] A combined regularization method is adopted to make full use of the five dimensions of information of three-dimensional seismic data, namely line domain, point domain, offset domain, azimuth domain and time domain, to solve the problems of uneven position of shot check points and uneven coverage times caused by acquisition and other reasons among blocks, fill the void positions, improve data consistency, and provide a good data foundation for subsequent processing.

[0103] The interpolation principle of these two data regularization methods is based on the matching pursuit Fourier transform interpolation algorithm. Under the constraint of irregular sampling input wave field, this method uses an iterative method to reconstruct the wave field in the Fourier domain, estimate the sparse spectrum, and perform inverse Fourier transform output on the final estimated sparse spectrum to reconstruct new seismic trajectories at any desired spatial position. Its advantage is that it can effectively prevent spatial aliasing and can realize interpolation in five-dimensional space of line domain, point domain, time domain, shot offset domain, and azimuth domain, so as to achieve the purpose of data regularization and improve the signal-to-noise ratio of the data.

[0104] Under the condition of irregular observation system, the Fourier forward transform is:

[0105] F(k,ω)=Φ H f(x,ω)

[0106] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data.

[0107] Then the inverse Fourier transform can be expressed as:

[0108] f(x,ω)=ΦF(k,ω)

[0109] Perform discrete Fourier transform on the time-frequency domain data to the wavenumber domain, find the maximum Fourier coefficient and estimate the sparse spectrum by the following formula, and select the frequency component with the maximum energy:

[0110]

[0111] Subtract this component data to obtain a new data body by the following formula:

[0112]

[0113] By repeating this process until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0114] All data are sorted into the common offset domain, and the input common offset data x is counted according to the following formula i ,y i The coordinate trajectory of , interpolates the missing data trajectory, and outputs the missing data on a regular grid;

[0115]

[0116] Where W is the amplitude weighting factor, the output data D′(x, y, t) is calculated from adjacent data by adaptive interpolation method, and the information is counted into the header attributes.

[0117] According to the empty trace data obtained from the previous step, use the matching pursuit Fourier transform interpolation technique to perform three-dimensional interpolation on the hole data, merge the output data with the input data, and restart a new round of sorting in the CMP domain;

[0118] Reconstruct a regular acquisition system, which needs to be able to meet the original acquisition system of each work area, redesign the fold, and the coordinates of shot points and receiver points, etc., and output the trace header data;

[0119] Based on the CMP domain data and the trace header data, use the matching pursuit Fourier transform interpolation technique to achieve five-dimensional interpolation in the line domain, trace domain, offset domain, azimuth domain, and time domain;

[0120] Perform subsequent processing and migration on the CMP gather data after five-dimensional interpolation.

[0121] Example 2

[0122] The present invention also provides a device for combined regularization processing of contiguous data, including:

[0123] A sorting module that sorts the data into the common offset domain and outputs empty trace data;

[0124] A secondary sorting module that uses the matching pursuit Fourier transform interpolation technique to perform three-dimensional interpolation on the empty trace data, merges the output data with the input common offset domain data, restarts the sorting in the CMP domain, and obtains new sorted data;

[0125] A reconstruction module that reconstructs the acquisition system based on the new sorted data, redesigns the system parameters, and outputs the trace header data;

[0126] A five-dimensional space interpolation module that, according to the new sorted data and the trace header data, uses the matching pursuit Fourier transform interpolation technique to achieve five-dimensional space interpolation;

[0127] A processing module that processes and migrates the gather data after five-dimensional interpolation and outputs the final gather data.

[0128] In one example, the output of the empty trace data includes:

[0129] Statistically analyze the coordinates of the input common offset data x i , y i of the coordinate trajectory, interpolate the missing data trajectory, and output the empty trace data on a regular grid.

[0130] In one example, the empty trace data is:

[0131]

[0132] where W is the amplitude weighting factor and D′(x, y, t) is the empty trace data.

[0133] In one example, the system parameters include coverage times and shot check point coordinate positions.

[0134] In one example, the five-dimensional space is line domain, track domain, offset domain, azimuth domain, and time domain.

[0135] In one example, the matching pursuit Fourier transform interpolation technique is:

[0136] Under the condition of irregular observation system, the Fourier forward transform is:

[0137] F(k,ω)=Φ H f(x,ω)

[0138] The inverse Fourier transform is:

[0139] f(x,ω)=ΦF(k,ω)

[0140] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data;

[0141] The data in the time-frequency domain is discretely Fourier transformed into the wavenumber domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, and this component data is subtracted to obtain a new data body. Through repeated iterations until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0142] In one example, the sparse spectrum is estimated by finding the maximum Fourier coefficient:

[0143]

[0144] The new data body is obtained by iterative update:

[0145]

[0146] Among them, p is the element with the largest inner product, F is the spectrum, and j is the number of iterations.

[0147] Specifically, data regularization is to fill in the missing reflection point information through interpolation based on the existing data. The difference in the time-distance characteristics of the event axis in each gather determines the effect of the interpolation and regularization methods. Therefore, it is necessary to examine the combination of different methods and different gathers to achieve the best combination effect of seismic data interpolation and regularization, while also achieving appropriate denoising.

[0148] The data regularization of common offset hole filling is to count the information of missing CMP reflection points and fill only the positions where CMP reflection points are missing in the bin through interpolation. This method can effectively solve the hole problem caused by irregular spatial sampling and improve the signal-to-noise ratio of the data.

[0149] The data regularization technology based on observation system reconstruction is to redesign the observation system and realize data interpolation and regularization based on anti-aliasing Fourier transform. The advantages of this method are that it is applicable to any irregular observation system, severe aliasing data, and complex steep-angle data. At the same time, it can realize spatial interpolation in line domain, point domain, time domain, shot offset domain, and azimuth domain to achieve the purpose of data regularization.

[0150] A combined regularization method is adopted to make full use of the five dimensions of information of three-dimensional seismic data, namely line domain, point domain, offset domain, azimuth domain and time domain, to solve the problems of uneven position of shot check points and uneven coverage times caused by acquisition and other reasons among blocks, fill the void positions, improve data consistency, and provide a good data foundation for subsequent processing.

[0151] The interpolation principle of these two data regularization methods is based on the matching pursuit Fourier transform interpolation algorithm. Under the constraint of irregular sampling input wave field, this method uses an iterative method to reconstruct the wave field in the Fourier domain, estimate the sparse spectrum, and perform inverse Fourier transform output on the final estimated sparse spectrum to reconstruct new seismic trajectories at any desired spatial position. Its advantage is that it can effectively prevent spatial aliasing and can realize interpolation in five-dimensional space of line domain, point domain, time domain, shot offset domain, and azimuth domain, so as to achieve the purpose of data regularization and improve the signal-to-noise ratio of the data.

[0152] Under the condition of irregular observation system, the Fourier forward transform is:

[0153] F(k,ω)=Φ H f(x,ω)

[0154] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data.

[0155] Then the inverse Fourier transform can be expressed as:

[0156] f(x,ω)=ΦF(k,ω)

[0157] Perform discrete Fourier transform on the time-frequency domain data to the wavenumber domain, find the maximum Fourier coefficient and estimate the sparse spectrum by the following formula, and select the frequency component with the maximum energy:

[0158]

[0159] Subtract this component data to obtain a new data body by the following formula:

[0160]

[0161] By repeating this process until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0162] All data are sorted into the common offset domain, and the input common offset data x is counted according to the following formula i ,y i The coordinate trajectory of , interpolates the missing data trajectory, and outputs the missing data on a regular grid;

[0163]

[0164] Where W is the amplitude weighting factor, the output data D′(x, y, t) is calculated from adjacent data by adaptive interpolation method, and the information is counted into the header attributes.

[0165] According to the empty channel data counted in the previous step, the matching pursuit Fourier transform interpolation technology is used to perform three-dimensional interpolation on the hole data, and the output data is merged with the input data to start a new round of CMP domain sorting;

[0166] Reconstruct the regular observation system, which needs to be able to meet the original observation system of each work area, redesign the coverage times, the coordinates of the gun inspection points, etc., and output the trace header data;

[0167] Through CMP domain data and trace header data, the matching pursuit Fourier transform interpolation technology is adopted to realize the interpolation of five-dimensional space in line domain, trace domain, offset domain, azimuth domain and time domain.

[0168] After obtaining the five-dimensional interpolation, the CMP gather data is subsequently processed and migrated.

[0169] Example 3

[0170] Figure 2 A schematic diagram of data regularization of a common offset hole filling type according to an embodiment of the present invention is shown. In the common offset data, the actual data is not changed, and only the hole position is interpolated.

[0171] Figure 3 A schematic diagram of data regularization based on observation system reconstruction according to an embodiment of the present invention is shown. Under the condition of an irregular observation system, combined with the analysis of the observation system conditions of each work area, the spatial positions of the uniform distribution of the observation system's gun inspection points are redesigned, and the observation system's gun inspection points are evenly distributed after data regularization.

[0172] Figure 4a and Figure 4b The schematic diagrams of the comparison of the regularization of hole filling and the regularization of migration profile reconstructed by the observation system according to an embodiment of the present invention are respectively shown. Comparing the effects of the two methods, the beaded imaging of the data regularization profile of the common offset hole filling method is clearer, and the overall energy consistency of the data regularization profile reconstructed by the observation system is higher.

[0173] Figure 5a and Figure 5b Schematic diagrams of regularized front and rear single offset comparisons according to an embodiment of the present invention are respectively shown. Figure 5a You can see that there are many holes in this offset. Figure 5b It can be seen that the voids are completely filled and the data quality is significantly improved.

[0174] Figure 6a and Figure 6b Schematic diagrams showing comparison of regularized front and rear offset profiles according to an embodiment of the present invention are shown respectively. Figure 6a It can be seen that the arcing of the migration before regularization is serious and the target layer information is messy; Figure 6b It can be seen that the offset arc drawing is greatly improved and the signal-to-noise ratio of the Ordovician interior is improved.

[0175] Example 4

[0176] Figure 7 A block diagram of a combined regularization processing device for contiguous data according to an embodiment of the present invention is shown.

[0177] like Figure 7 As shown, the combined regularization processing device of the contiguous data includes:

[0178] A sorting module 201 sorts the data into a common offset domain and outputs the empty track data;

[0179] The secondary sorting module 202 uses the matching pursuit Fourier transform interpolation technology to perform three-dimensional interpolation on the air channel data, merges the output data with the input common offset domain data, restarts the CMP domain sorting, and obtains new sorting data;

[0180] Reconstruction module 203, reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs the trace header data;

[0181] The five-dimensional space interpolation module 204 uses the matching pursuit Fourier transform interpolation technology to realize five-dimensional space interpolation according to the newly sorted data and the trace header data;

[0182] The processing module 205 processes and offsets the gather data after the five-dimensional interpolation and outputs the final gather data.

[0183] As an option, output air channel data includes:

[0184] Statistical input common offset data x i ,y i The coordinate trajectory of the vacant data is interpolated and the empty track data is output on a regular grid.

[0185] As an optional solution, the empty channel data is:

[0186]

[0187] Where W is the amplitude weighting factor and D′(x,y,t) is the empty channel data.

[0188] As an optional solution, the system parameters include the number of coverage times and the coordinate positions of the shot check points.

[0189] As an optional solution, the five-dimensional space is line domain, track domain, offset domain, azimuth domain and time domain.

[0190] As an alternative, the matching pursuit Fourier transform interpolation technique is:

[0191] Under the condition of irregular observation system, the Fourier forward transform is:

[0192] F(k,ω)=Φ H f(x,ω)

[0193] The inverse Fourier transform is:

[0194] f(x,ω)=ΦF(k,ω)

[0195] in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data;

[0196] The data in the time-frequency domain is discretely Fourier transformed into the wavenumber domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, and this component data is subtracted to obtain a new data body. Through repeated iterations until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0197] As an alternative, the sparse spectrum can be estimated by finding the maximum Fourier coefficient as follows:

[0198]

[0199] The new data body is obtained by iterative update:

[0200]

[0201] Among them, p is the element with the largest inner product, F represents the spectrum, and j is the number of iterations.

[0202] Example 5

[0203] This embodiment provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above-mentioned combined regularization processing method for contiguous data.

[0204] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0205] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0206] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0207] Those skilled in the art should understand that in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present disclosure.

[0208] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0209] Example 6

[0210] This embodiment provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned combined regularization processing method for contiguous data.

[0211] The computer-readable storage medium according to an embodiment of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods in the foregoing embodiments of the present disclosure are executed.

[0212] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0213] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0214] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A combined regularization processing method for contiguous data, It is characterized in that include: Sort the data into the common offset domain and output the empty track data; Using matching pursuit Fourier transform interpolation technology to perform three-dimensional interpolation on the empty channel data, merging the output data with the input common offset domain data, restarting CMP domain sorting, and obtaining new sorting data; Reconstruct the observation system based on the new sorting data, redesign the system parameters, and output the trace header data; According to the newly sorted data and the header data, a matching pursuit Fourier transform interpolation technique is adopted to realize five-dimensional space interpolation; The gather data after five-dimensional interpolation are processed and offset, and the final gather data are output.

2. The combined regularization processing method of contiguous data according to claim 1, in, Output air channel data includes: Count the input common offset data x i ,y i The coordinate trajectory of the vacant data is interpolated and the empty track data is output on a regular grid.

3. The combined regularization processing method of contiguous data according to claim 1, in, The air channel data is: Where W is the amplitude weighting factor, D ′ (x, y, t) is the spatial data.

4. The combined regularization processing method of contiguous data according to claim 1, in, The system parameters include the number of coverage times and the coordinate positions of the shot inspection points.

5. The combined regularization processing method of contiguous data according to claim 1, in, The five-dimensional space is line domain, track domain, offset domain, azimuth domain and time domain.

6. The combined regularization processing method of contiguous data according to claim 1, in, The matching pursuit Fourier transform interpolation technique is: Under the condition of irregular observation system, the Fourier forward transform is: F(k,ω)=Φ H f(x,ω) The inverse Fourier transform is: f(x,ω)=ΦF(k,ω) in, x is the time variable, k is the wave number, ω is the instantaneous angular frequency, f(x,ω) is the time-frequency domain data, and F(k,ω) is the wave number-frequency domain data; The data in the time-frequency domain is discretely Fourier transformed into the wavenumber domain, the maximum Fourier coefficient is found to estimate the sparse spectrum, the frequency component with the maximum energy is selected, and this component data is subtracted to obtain a new data body. Through repeated iterations until the residual value is negligible, the final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

7. The combined regularization processing method of contiguous data according to claim 6, in, The sparse spectrum is estimated by finding the maximum Fourier coefficient: The new data body is obtained by iterative update: Among them, p is the element with the largest inner product, F is the spectrum, and j is the number of iterations.

8. A combined regularization processing device for contiguous data, It is characterized in that include: The sorting module sorts the data into the common offset domain and outputs the air channel data; A secondary sorting module uses a matching pursuit Fourier transform interpolation technique to perform three-dimensional interpolation on the air channel data, merges the output data with the input common offset domain data, restarts CMP domain sorting, and obtains new sorting data; A reconstruction module, which reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs the trace header data; A five-dimensional space interpolation module, which realizes five-dimensional space interpolation by using matching pursuit Fourier transform interpolation technology according to the newly sorted data and the header data; The processing module processes and offsets the gather data after five-dimensional interpolation and outputs the final gather data.

9. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the combined regularized processing method for contiguous data according to any one of claims 1 to 7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for combining and regularizing contiguous data according to any one of claims 1 to 7 is implemented.

Images