Method and device for regularizing combination of continuous data, electronic equipment and medium

Abstract

The application discloses a kind of combination regular processing method, device, electronic equipment and medium of piece data.The method comprises: data is sorted into common offset domain, and output empty channel data;Three-dimensional interpolation is carried out to empty channel data using matching pursuit fourier transform interpolation technology, and output data is combined with common offset domain data, and re-start CMP field sorting, obtain new sorting data;Based on new sorting data, reconstruct observation system, redesign system parameter, and output trace head data;According to new sorting data and trace head data, using matching pursuit fourier transform interpolation technology, five-dimensional space interpolation is realized;For five-dimensional interpolation after trace gather data, processing, migration is carried out, and the final trace gather data is output.The application is applied to different years acquisition, direction angle, coverage times, observation system's piece research area, solve the uneven phenomenon of prestack trace gather energy, reduce migration arc, more conducive to prestack migration imaging, lay high-quality data foundation for subsequent migration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical exploration seismic data processing, and more particularly, to a combined regularization processing method and device for continuous data, electronic equipment and medium. BACKGROUND

[0002] With the increasing difficulty of exploration and development, and the deepening of exploration understanding and the development of technology, the accuracy requirement for reservoir prediction is higher and higher, so it is necessary to further improve the identification ability of fracture-cave bodies and micro cracks, which requires high quality of seismic data. Now more and more research areas are composed of two or more three-dimensional blocks, and in the face of data of different years, different acquisition directions, different fold numbers and different observation systems, the consistency of time difference, energy and frequency of data processing is faced with the problem of continuous 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 compression sensing theory, etc., so that the regularization technology is continuously developed from three-dimensional data regularization to five-dimensional data regularization.

[0004] The key parameters such as maximum offset, fold number, aspect ratio and bin of the continuous work area differ, which will lead to the problem that the diffracted energy cannot interfere with each other well in the migration process, produce migration arc, and cause uneven energy. Therefore, how to eliminate the difference of key parameters between continuous work areas on the basis of such complex continuous data is a difficulty.

[0005] At present, a combined regularization processing method based on complex continuous data needs to be developed.

[0006] The information disclosed in the background section of this application is only intended to deepen the understanding of the general background of the application, and should not be regarded as acknowledging or implying in any form that the information constitutes prior art known to those skilled in the art. SUMMARY

[0007] The present application provides a combined regularization processing method and device for continuous data, electronic equipment and medium, which can solve the energy consistency problem of complex continuous data, reduce the migration arc, obtain spatially sampled gather, and improve the signal-to-noise ratio of migration stacking records.

[0008] In a first aspect, the present application provides a combined regularization processing method for continuous data, comprising:

[0009] sorting the data into common offset domain and outputting empty channel data;

[0010] The matching pursuit Fourier transform interpolation technology is used to interpolate the empty trace data in three dimensions, the output data is combined with the input common offset domain data, the CMP domain sorting is restarted, and new sorted data is obtained.

[0011] The observation system is reconstructed based on the new sorted data, the system parameters are redesigned, and trace header data is output.

[0012] Based on the new sorted data and the trace header data, the matching pursuit Fourier transform interpolation technology is used to realize five-dimensional space interpolation.

[0013] The five-dimensional interpolated trace gather data is processed and migrated, and the final trace gather data is output.

[0014] As a specific implementation manner of the embodiment of the present disclosure, the output empty trace data comprises:

[0015] The coordinate tracks of the input common offset data x i ,y i are counted, the missing data tracks are interpolated, and the empty trace data is output on a regular grid.

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

[0017]

[0018] Wherein, W is an amplitude weighting factor, and D'(x, y, t) is the empty trace data.

[0019] As a specific implementation manner of the embodiment of the present disclosure, the system parameters comprise the number of coverages and the coordinate position of shot-receiver.

[0020] As a specific implementation manner of the 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.

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

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

[0023] F(k, ω) = Φ H f(x, ω)

[0024] The Fourier inverse transform is:

[0025] f(x, ω) = ΦF(k, ω)

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

[0027] Discrete Fourier transform is performed on the data in the time-frequency domain into the wave number domain, a maximum Fourier coefficient is found to estimate a sparse spectrum, a frequency component with the maximum energy is selected, the component data is subtracted to obtain a new data body, and iteration is repeatedly performed until the residual value is negligible, and finally obtained sparse spectrum is inverse Fourier transformed and output to an expected spatial position.

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

[0029]

[0030] A new data body is obtained through iterative updating by the following formula:

[0031]

[0032] Wherein, p is an element with the maximum inner product, F represents a spectrum, and j is an iteration number.

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

[0034] A sorting module sorts data into a common offset domain and outputs empty channel data;

[0035] A secondary sorting module performs three-dimensional interpolation on the empty channel data by using a matching pursuit Fourier transform interpolation technology, combines the output data with the input common offset domain data, re-starts CMP domain sorting, and obtains new sorting data;

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

[0037] A five-dimensional space interpolation module uses a matching pursuit Fourier transform interpolation technology to realize five-dimensional space interpolation according to the new sorting data and the trace header data;

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

[0039] As a specific implementation manner of the embodiment of the present disclosure, the output empty channel data comprises:

[0040] The coordinate trajectories of the input common offset data x i ,y i are counted, the missing data trajectories are interpolated, and the empty channel data is output on a regular grid.

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

[0042]

[0043] Wherein, W is an amplitude weighting factor, and D'(x, y, t) is the empty channel data.

[0044] As a specific implementation manner of the embodiment of the present disclosure, the system parameters include the number of coverages and the coordinates of the shot points.

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

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

[0047] For the irregular observation system condition, the Fourier forward transform is:

[0048] F(k, ω) = Φ H f(x, ω)

[0049] The Fourier inverse transform is:

[0050] f(x, ω) = ΦF(k, ω)

[0051] Wherein, x is a time variable, k is a wave number, ω is an instantaneous angular frequency, f(x, ω) is time-frequency domain data, and F(k, ω) is wave number-frequency domain data.

[0052] The data in the time-frequency domain is subjected to a discrete Fourier transform into a wave number domain, a maximum Fourier coefficient is found to estimate a sparse spectrum, a frequency component with the maximum energy is selected, a new data body is obtained by subtracting the component data, and through repeated iteration until the residual value is negligible, finally the sparse spectrum is output to the expected spatial position through inverse Fourier transform.

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

[0054]

[0055] A new data body is obtained through iterative updating by the following formula:

[0056]

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

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

[0059] a memory storing executable instructions;

[0060] a processor running the executable instructions in the memory to implement the combination regularization processing method of the continuous data.

[0061] In a fourth aspect, the present disclosure also provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the combination regularization processing method of the continuous data.

[0062] The beneficial effects are as follows:

[0063] The present application is aimed at continuous three-dimensional exploration area, based on the data with large differences in acquisition span, coverage times, aspect ratio, azimuth, etc., through combination regularization technology, the problems of energy difference and migration drawing arc are solved, and the signal-to-noise ratio of data is improved. The current exploration target develops from clastic rock to Ordovician system to Cambrian system. For super-deep layer processing, especially in low signal-to-noise ratio and low coverage area, after data regularization by combination regularization technology, the signal-to-noise ratio of data is improved, the inside of super-deep layer is clearer, and the profile phase axis is improved, which is more conducive to subsequent exploration and development research.

[0064] The method and device of the present application have other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent detailed description incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0065] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the figures, and wherein:

[0066] Figure 1 A flow chart showing the steps of the combination regularization processing method of continuous data according to one embodiment of the present application is shown.

[0067] Figure 2 A data regularization schematic diagram based on observation system reconstruction according to one embodiment of the present application is shown.

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

[0069] Figure 4a and Figure 4b show a schematic diagram of a comparison of a hole regularization, an observation system regularization, a migration profile regularization, respectively, according to an embodiment of the present application.

[0070] Figure 5a and Figure 5b show a schematic diagram of a comparison of a single offset before and after regularization, respectively, according to an embodiment of the present application.

[0071] Figure 6a and Figure 6b show a schematic diagram of a comparison of a migration profile before and after regularization, respectively, according to an embodiment of the present application.

[0072] Figure 7 shows a block diagram of a combined regularization processing device for patch data according to an embodiment of the present application.

[0073] BRIEF DESCRIPTION OF DRAWINGS

[0074] 201, sorting module; 202, secondary sorting module; 203, reconstruction module; 204, five-dimensional space interpolation module; 205, processing module. DETAILED DESCRIPTION

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

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

[0077] Example 1

[0078] Figure 1 shows a flow chart of the steps of a combined regularization processing method for patch data according to an embodiment of the present application.

[0079] As Figure 1As shown, the combination regular processing method of the continuous data includes: step 101, sorting the data into the common offset distance domain, and outputting the empty channel data; step 102, using the matching pursuit Fourier transform interpolation technology to perform three-dimensional interpolation on the empty channel data, combining the output data with the input common offset distance domain data, re-sorting the CMP domain, and obtaining new sorting data; step 103, reconstructing the observation system based on the new sorting data, redesigning the system parameters, and outputting the channel head data; step 104, using the matching pursuit Fourier transform interpolation technology to realize five-dimensional space interpolation according to the new sorting data and the channel head data; and step 105, processing and migrating the five-dimensional interpolated trace gather data, and outputting the final trace gather data.

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

[0081] Statistics of the coordinate track of the input common offset distance data x i ,y i Interpolation of the missing data track, and output of the empty channel data on the regular grid.

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

[0083]

[0084] Wherein, W is an amplitude weighting factor, and D'(x, y, t) is the empty channel data.

[0085] In one example, the system parameters include the number of coverages and the coordinate position of the shot point.

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

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

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

[0089] F(k, ω) = Φ H f(x, ω)

[0090] The Fourier inverse transform is:

[0091] f(x, ω) = ΦF(k, ω)

[0092] Wherein, x is a time variable, k is a wave number, ω is an instantaneous angular frequency, f(x, ω) is time-frequency domain data, and F(k, ω) is wave number-frequency domain data.

[0093] Discrete Fourier transform is performed on the data in time-frequency domain into wave number domain, to find the maximum Fourier coefficient to estimate the sparse spectrum, to select the frequency component with the maximum energy, to subtract the component data to obtain a new data volume, to iterate repeatedly until the residual value is negligible, and finally to obtain the sparse spectrum which is inverse Fourier transformed and output to the expected spatial position.

[0094] In one example, the maximum Fourier coefficient is found to estimate the sparse spectrum by the following formula:

[0095]

[0096] The new data volume is obtained by iterative updating by the following formula:

[0097]

[0098] Wherein, p is the element with the maximum inner product, F represents the frequency spectrum, and j is the iteration number.

[0099] Specifically, the data regularization is to fill in the missing reflection point information by interpolation based on the existing data. The difference in the time-distance characteristics of the events in each gather determines the effect of the interpolation and regularization method. Therefore, different methods and combinations of different gathers are investigated to achieve the best combination effect of seismic data interpolation and regularization, and to play a proper denoising role.

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

[0101] The data regularization technology based on the reconstruction of the observation system is to redesign the observation system, and to realize data interpolation and regularization based on the anti-aliasing Fourier transform. This method is suitable for any irregular observation system, is suitable for severe aliasing data, is suitable for complex steep dip angle data, and can realize spatial interpolation in the line domain, point domain, time domain, offset domain and azimuth domain, to achieve the purpose of data regularization.

[0102] The combined regularization method fully utilizes the five-dimensional information of the three-dimensional seismic data in the line domain, point domain, offset domain, azimuth domain and time domain, solves the problems of uneven position of shot and receiver points and uneven coverage caused by acquisition in each block, fills in the hole positions, improves the data consistency, and provides a good data basis for subsequent processing.

[0103] The interpolation principle of the two data regularization methods is based on the matched pursuit Fourier transform interpolation algorithm. The method reconstructs the wave field in the Fourier domain under the constraint of irregularly sampled input wave field, estimates the sparse spectrum, and outputs the final estimated sparse spectrum by inverse Fourier transform to reconstruct new seismic traces at any desired spatial position. Its advantages are that it can effectively prevent spatial aliasing phenomenon, can realize interpolation in five-dimensional space of line domain, point domain, time domain, offset domain and azimuth domain, achieve the purpose of data regularization, and improve the signal-to-noise ratio of data.

[0104] For irregular observation system conditions, the Fourier forward transform is:

[0105] F(k,ω)=Φ H f(x,ω)

[0106] Wherein, 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 Fourier inverse transform can be expressed as:

[0108] f(x,ω)=ΦF(k,ω)

[0109] Discrete Fourier transform of the time-frequency domain data into the wave number domain, find the maximum Fourier coefficient to estimate the sparse spectrum by the following formula, and select the frequency component with the maximum energy:

[0110]

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

[0112]

[0113] Repeat the iteration in this way until the remaining value is negligible. The final obtained sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0114] Sort all data into the common offset domain, according to the following formula, count the coordinate traces of the input common offset data x i ,y i , interpolate the missing data traces, and output the missing data on the regular grid;

[0115]

[0116] Where W is the amplitude weighting factor, the output data D'(x,y,t) is calculated from the adjacent data by the adaptive interpolation method, and the information is counted into the trace header attribute.

[0117] According to the empty channel data counted in the last step, the three-dimensional interpolation of the empty channel data is carried out by using the matching pursuit Fourier transform interpolation technology, the output data is combined with the input data, and a new round of CMP field sorting is started again;

[0118] The observation system is reconstructed, and the observation system needs to meet the original observation system of each work area, the number of coverages is redesigned, and the coordinates of shot points are outputted.

[0119] By using the matching pursuit Fourier transform interpolation technology, the five-dimensional space interpolation in the line domain, the trace domain, the offset distance domain, the azimuth domain and the time domain is realized by the CMP field data and the trace header data.

[0120] After the five-dimensional interpolation of the CMP gather data is obtained, subsequent processing and migration are carried out.

[0121] Example 2

[0122] The application also provides a combined regular processing device of the piece data, which comprises:

[0123] The sorting module sorts the data into the common offset distance domain, and outputs the empty channel data.

[0124] The secondary sorting module carries out three-dimensional interpolation on the empty channel data by using the matching pursuit Fourier transform interpolation technology, combines the output data with the input common offset distance domain data, starts the CMP field sorting again, and obtains new sorting data.

[0125] The reconstruction module reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs the trace header data.

[0126] The five-dimensional space interpolation module realizes the five-dimensional space interpolation according to the new sorting data and the trace header data by using the matching pursuit Fourier transform interpolation technology.

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

[0128] In one example, the output empty channel data comprises:

[0129] The coordinates of the input common offset distance data x i ,y i are counted, the missing data track is interpolated, and the empty channel data is outputted on the regular grid.

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

[0131]

[0132] Wherein, W is an amplitude weighting factor, and D'(x, y, t) is the empty channel data.

[0133] In one example, the system parameters include the number of coverages, the coordinate positions of the shot points.

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

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

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

[0137] F(k, ω) = Φ H f(x, ω)

[0138] The Fourier inverse transform is:

[0139] f(x, ω) = ΦF(k, ω)

[0140] wherein, x is a time variable, k is a wave number, ω is an instantaneous angular frequency, f(x, ω) is time-frequency domain data, and F(k, ω) is wave number-frequency domain data.

[0141] The data in the time-frequency domain is subjected to a discrete Fourier transform into the wave number domain, a maximum Fourier coefficient is found to estimate a sparse spectrum, a frequency component with the maximum energy is selected, the component data is subtracted to obtain a new data body, and through repeated iteration until the residual value is negligible, and finally the sparse spectrum is output to the expected spatial position through inverse Fourier transform.

[0142] In one example, the maximum Fourier coefficient is found to estimate the sparse spectrum through:

[0143]

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

[0145]

[0146] wherein, p is an element with the maximum inner product, F represents a spectrum, and j is an iteration number.

[0147] Specifically, the 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 events in each gather determines the use effect of the interpolation and regularization method. Therefore, different methods and different combinations of gathers are investigated to make the combination effect of the seismic data interpolation and regularization optimal, and to play a proper denoising role.

[0148] The common offset hole-filling data regularization is to statistically lack the CMP reflection point information and to fill only the missing CMP reflection point position in the bin by interpolation. The method can effectively solve the problem of the spatial sampling irregularity and improve the signal-to-noise ratio of the data.

[0149] The data regularization technology based on the reconstruction of the observation system is to redesign the observation system, to realize the data interpolation and regularization based on the anti-aliasing Fourier transform. The method has the advantages of being applicable to any irregular observation system, being suitable for the severe aliasing data, being applicable to the complex steep dip angle data, being capable of realizing the spatial interpolation in the line domain, the point domain, the time domain, the offset domain and the azimuth domain, and achieving the purpose of the data regularization.

[0150] The combined regularization method is used to fully utilize the five-dimensional information of the three-dimensional seismic data in the line domain, the point domain, the offset domain, the azimuth domain and the time domain, to solve the problems of the uneven position of the shot-receiver points and the uneven fold caused by the acquisition in each block, to fill the hole position and to improve the data consistency, thereby providing a good data basis for the subsequent processing.

[0151] The interpolation principle basis of the two data regularization methods is the matching pursuit Fourier transform interpolation algorithm. The method is used to reconstruct the wave field in the Fourier domain under the constraint of the irregular sampling input wave field, to estimate the sparse spectrum, to output the final estimated sparse spectrum, and to reconstruct the new seismic track at any desired spatial position. The method has the advantages of being capable of effectively preventing the spatial aliasing phenomenon, being capable of realizing the interpolation in the five-dimensional space of the line domain, the point domain, the time domain, the offset domain and the azimuth domain, achieving the purpose of the data regularization, and improving the signal-to-noise ratio of the data.

[0152] For the irregular observation system, the Fourier forward transform is:

[0153] F(k,ω)=Φ H f(x,ω)

[0154] wherein, 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] The Fourier inverse transform can be expressed as:

[0156] f(x,ω)=ΦF(k,ω)

[0157] The discrete Fourier transform of the time-frequency domain data into the wave number domain is performed, the maximum Fourier coefficient is found to estimate the sparse spectrum, and the frequency component with the maximum energy is selected:

[0158]

[0159] The new data volume is obtained by subtracting this component data by the following formula:

[0160]

[0161] This iteration is repeated until the residual value is negligible. The final sparse spectrum will be inverse Fourier transformed to output the expected spatial position.

[0162] All data is sorted into the common offset domain, and the input common offset data x i ,y i is sorted according to the following formula, the coordinate trajectory of the input data is interpolated to fill the missing data trajectory, and the missing data is output on a regular grid;

[0163]

[0164] where W is the amplitude weighting factor, the output data D'(x, y, t) is calculated from the adjacent data by an adaptive interpolation method, and the information is counted into the trace header attribute.

[0165] Based on the missing trace data counted in the previous step, a matching pursuit Fourier transform interpolation technique is used to perform three-dimensional interpolation on the missing data, the output data is combined with the input data, and a new round of CMP domain sorting is started;

[0166] The regular observation system is reconstructed, which needs to be able to meet the original observation system of each work area, and the number of coverages and shot-receiver coordinate positions are redesigned, and the trace header data is output;

[0167] Based on the CMP domain data and the trace header data, a matching pursuit Fourier transform interpolation technique is used to realize five-dimensional interpolation in the line domain, the trace domain, the offset domain, the azimuth domain, and the time domain;

[0168] After obtaining the five-dimensional interpolated CMP gather data, subsequent processing and migration are performed.

[0169] Example 3

[0170] Figure 2 A common offset hole filling type data regularization schematic diagram according to one embodiment of the present application is shown. In the common offset data, the actual data is not changed, and only the interpolation process is performed on the hole position.

[0171] Figure 3 A data regularization schematic diagram based on observation system reconstruction according to one embodiment of the present application is shown. Under the condition of an irregular observation system, the spatial positions of the shot-receiver points of the observation system are redesigned in combination with the analysis of the observation system of each work area, and the shot-receiver points of the data regularization observation system are uniformly distributed.

[0172] Figure 4a and Figure 4b respectively show schematic diagrams of hole filling regularization and observation system reconstruction regularization offset profile comparison according to one embodiment of the present application. Comparing the effects of the two methods, the data regularization profile bead imaging of the hole filling type is clearer, and the overall energy consistency of the data regularization profile based on observation system reconstruction is higher.

[0173] Figure 5a and Figure 5b respectively show schematic diagrams of single offset comparison before and after regularization according to one embodiment of the present application. Figure 5a It can be seen that there are many holes in the offset distance, Figure 5b It can be seen that the holes are filled completely, and the data quality is obviously improved

[0174] Figure 6a and Figure 6b respectively show schematic diagrams of offset profile comparison before and after regularization according to one embodiment of the present application. Figure 6a It can be seen that the offset drawing arc is serious before regularization, and the information of the target layer is chaotic; Figure 6b It can be seen that the offset drawing arc is well improved, and the signal-to-noise ratio of the Ordovician inner layer is improved.

[0175] Example 4

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

[0177] As Figure 7 shown, the combined regularization processing device for continuous data includes:

[0178] The sorting module 201 sorts the data into the common offset distance domain and outputs the empty channel data.

[0179] The secondary sorting module 202 performs three-dimensional interpolation on the empty channel data using the matching pursuit Fourier transform interpolation technology, combines the output data with the input common offset distance domain data, re-starts the CMP domain sorting, and obtains new sorting data.

[0180] The reconstruction module 203 reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs the channel head data.

[0181] The five-dimensional space interpolation module 204 performs five-dimensional space interpolation according to the new sorting data and the channel head data using the matching pursuit Fourier transform interpolation technology.

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

[0183] As an option, the output empty channel data comprises:

[0184] Statistics of the input common offset data x i ,y i coordinates track, interpolates the missing data track, and outputs the empty channel data on a regular grid.

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

[0186]

[0187] where W is an amplitude weighting factor, and D'(x, y, t) is the empty channel data.

[0188] As an option, the system parameters include the number of coverages, and the coordinates of the shot points.

[0189] As an option, the five-dimensional space is the line domain, the channel domain, the offset domain, the azimuth domain, and the time domain.

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

[0191] For irregular observation system conditions, the Fourier forward transform is:

[0192] F(k, ω) = Φ H f(x, ω)

[0193] The Fourier inverse transform is:

[0194] f(x, ω) = ΦF(k, ω)

[0195] wherein, 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] Discrete Fourier transform of the time-frequency domain data into the wave number domain to find the maximum Fourier coefficient to estimate the sparse spectrum, select the frequency component with the maximum energy, subtract this component data to obtain a new data body, and through repeated iteration until the remaining value is negligible. The final sparse spectrum will be inverse Fourier transformed and output to the expected spatial position.

[0197] As an option, the maximum Fourier coefficient is found to estimate the sparse spectrum by:

[0198]

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

[0200]

[0201] where p is the element with the largest inner product, F represents the frequency spectrum, and j is the iteration number.

[0202] Example 5

[0203] The embodiment provides an electronic device, which comprises a memory storing executable instructions, and a processor running the executable instructions in the memory to implement the combination regularization processing method of the continuous data.

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

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

[0206] The processor can be a central processing unit (CPU) or other forms of processing units having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is configured 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 a good user experience effect, the embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the present disclosure.

[0208] Detailed descriptions of the embodiment can refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0209] Example 6

[0210] The embodiment provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the combination regularization processing method of the continuous data.

[0211] The computer-readable storage medium according to the 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 method according to the embodiments of the present disclosure are executed.

[0212] The above computer readable storage medium includes, but is not limited to, an optical storage medium (for example, a CD-ROM and a DVD), a magneto-optical storage medium (for example, an MO), a magnetic storage medium (for example, a magnetic tape or a magnetic hard disk), a medium having a built-in rewritable nonvolatile memory (for example, a memory card), and a medium having a built-in ROM (for example, a ROM cartridge).

[0213] Those skilled in the art will understand that the above description of the embodiments of the present application is given for the purpose of exemplarily illustrating the advantageous effects of the embodiments of the present application, and is not intended to limit the embodiments of the present application to any of the examples given.

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

Claims

1. A method for combining and regularizing contiguous data, characterized in that, include: The data is sorted into the common offset domain, and the air channel data is output. The matching pursuit Fourier transform interpolation technique is used to perform three-dimensional interpolation on the air channel data. The output data is then merged with the input common offset domain data, and the CMP domain sorting is restarted to obtain new sorting data. Based on the new sorting data, the observation system is reconstructed, the system parameters are redesigned, and the track head data is output. Based on the new sorting data and the track head data, a five-dimensional spatial interpolation technique is used to achieve interpolation. The five-dimensional interpolated gather data is processed and offset to output the final gather data; The output air channel data includes: Statistical input co-offset data The coordinate trajectory is interpolated to extract the missing data trajectory, and the empty track data is output on the regular grid. The air channel data is as follows: in, It is the amplitude weighting factor. It is air channel data.

2. The method for combining and regularizing contiguous data according to claim 1, wherein, The system parameters include the number of coverages and the coordinates of the shot receivers.

3. The method for combining and regularizing contiguous data according to claim 1, wherein, The five-dimensional space consists of the line domain, the channel domain, the offset domain, the azimuth domain, and the time domain.

4. The method for combining and regularizing contiguous data according to claim 1, wherein, The matching pursuit Fourier transform interpolation technique is as follows: For irregular observation systems, the Fourier forward transform is: The inverse Fourier transform is: in, It is a time variable. It is the wave number. It is the instantaneous angular frequency. It is time-frequency domain data. It is wavenumber-frequency domain data; The time-frequency domain data is subjected to a discrete Fourier transform to the wavenumber domain. The sparse spectrum is estimated by finding the maximum Fourier coefficients. The frequency component with the highest energy is selected and subtracted to obtain a new data volume. This process is repeated until the remaining value is negligible. Finally, the sparse spectrum is output to the desired spatial location by inverse Fourier transform.

5. The method for combining and regularizing contiguous data according to claim 4, wherein, The sparse spectrum is estimated by finding the maximum Fourier coefficient using the following formula: The new data volume is obtained by iteratively updating using the following formula: in, It is the element with the largest inner product. Represented as the spectrum, This represents the number of iterations.

6. A device for combining and regularizing contiguous data, characterized in that, include: The sorting module sorts the data into the common offset domain and outputs air channel data. The secondary sorting module uses matched 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, and restarts CMP domain sorting to obtain new sorting data. The reconstruction module reconstructs the observation system based on the new sorting data, redesigns the system parameters, and outputs track head data. The five-dimensional space interpolation module uses the matching pursuit Fourier transform interpolation technique to achieve five-dimensional space interpolation based on the new sorting data and the track head data. The processing module processes and offsets the five-dimensional interpolated gather data, and outputs the final gather data. The output air channel data includes: Statistical input co-offset data The coordinate trajectory is interpolated to extract the missing data trajectory, and the empty track data is output on the regular grid. The air channel data is as follows: in, It is the amplitude weighting factor. It is air channel data.

7. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for combining and regularizing contiguous data according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for combining and regularizing contiguous data as described in any one of claims 1-5.

Images