CN112241024A - Method for improving signal-to-noise ratio of seismic data, computer storage medium and system - Google Patents
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Abstract
The invention discloses a method for improving the signal-to-noise ratio of seismic data, a computer storage medium and a system, wherein the method comprises the following steps: determining a filter target function corresponding to the minimum value of a preset least square fitting expression according to a plurality of data points on the corresponding channel of the obtained source data and target data; performing wavelet matching on the source data points one by one according to a filter target function to obtain a plurality of target data points which are subjected to wavelet matching with the source data points one by one; sequencing the obtained target data points after the multiple wavelets are matched to obtain a target data track; the computer storage medium has stored therein a computer program which, when executed by a processor, performs the method, the system comprising a processor and a memory, the processor performing the method when executing the computer program stored in the memory. The filtering factor obtained by single-channel calculation is more reliable, the difference caused by the factor wave inconsistency among different data is solved, and the signal-to-noise ratio of the seismic data is further improved.
Description
Technical Field
The invention relates to the technical field of seismic exploration data processing, in particular to a method for improving the signal-to-noise ratio of seismic data, a computer storage medium and a system.
Background
In seismic exploration, due to the fact that the earth surface is complex, field acquisition adopts mixed construction of multiple types of seismic sources, different excitation modes cause large differences of amplitude, phase and frequency among seismic source records, and inconsistency of seismic data is caused, and due to the differences caused by the inconsistency, when the multiple data are used for migration imaging at the same time, differences of energy among channels and the like exist in an obtained seismic section, the inconsistency of the seismic data is caused, and the signal-to-noise ratio of the seismic records is reduced seriously due to the inconsistency.
In seismic data processing, data acquired from adjacent blocks or the same block in different years need to be processed frequently, the data are more and more complex and more refined at present based on geophysical research, and sometimes, when the data are processed identically, differences in amplitude, phase or frequency spectrum exist among the data, and the data need to be processed among different data.
At present, the main methods for solving the problem of consistency of different data in actual production are a time shifting method, a deconvolution parameter adjusting method and a matched filtering method. The time shifting method is characterized in that two or more groups of data to be spliced are processed by different processes respectively, then time difference change of an overlapped part is analyzed, and overall time shifting correction is carried out on one group of data relative to the other group of data so as to realize data splicing. The method is simple to realize, but can only partially improve the consistency of two groups of seismic data, is difficult to consider the change of the time difference of shallow, medium and deep layers, and has poor application effect. The method for adjusting the deconvolution parameters improves the consistency of seismic data by reasonably selecting the deconvolution parameters, can better eliminate frequency difference between different data, improves the signal-to-noise ratio of the data, has a certain effect, but is difficult to accurately select the deconvolution parameters in practical application. Several phase correction techniques in actual production have limitations in their application such that the corrected recordings cannot be restored to the optimal form. The wavelet matching technology directly utilizes repeated seismic channels (which can be respectively called a source data channel set and a target data channel set) to design a matched filter, and then performs matched filtering on the source data channel set to enable the source data channel set to be close to the target data channel set to the maximum extent. In the field of geophysics at present, wavelet matching usually designs a filter operator to achieve consistency of amplitude, phase or frequency spectrum between data.
However, when wavelet matching is performed in the prior art, several recording channels with strong contrast are sometimes selected in a recording time window to obtain a matching factor between a target data channel set and a source data channel set when a filtering factor is obtained, and the matching factor obtained based on the multiple channel sets has a problem of low reliability, which results in a low signal-to-noise ratio of processed seismic data.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in the prior art, the matching factor obtained based on a multi-channel set has the problem of low signal-to-noise ratio of seismic data due to low reliability.
To solve the above technical problems, the present invention provides a method, a computer storage medium and a system for improving the signal-to-noise ratio of seismic data.
According to a first aspect of the present invention, there is provided a method for improving signal-to-noise ratio based on seismic data, comprising the steps of:
obtaining a plurality of groups of corresponding track data points of source data and target data;
aiming at each group of corresponding channel data points, determining a filter target function corresponding to the corresponding channel data point and the minimum value of a preset least square fitting expression;
performing wavelet matching on the source data point of the corresponding channel data point according to the filter target function to obtain a source data point which is subjected to wavelet matching with the source data point;
and sequencing the source data points after wavelet matching obtained by the multiple groups of corresponding channel data points to obtain source data after wavelet matching, so as to eliminate the difference caused by the inconsistency of wavelets and improve the signal-to-noise ratio of the seismic data.
Preferably, the method for improving the signal-to-noise ratio of the seismic data further includes constructing the least square fitting expression, which includes:
filtering the source data to obtain filtered source data;
performing inverse filtering processing on the target data to obtain inverse filtered target data;
taking the square of the difference between the target data and the filtered source data as a first expression;
taking the square of the difference between the source data and the target data after inverse filtering as a second expression;
and constructing the sum of the first expression and the second expression as the least square fitting expression.
Preferably, the least squares fit expression satisfies:
E＝(F·X-Y)2+(X-F-1·Y)2；
where F is the filter objective function in the frequency domain, X is the source data, and Y is the target data.
Preferably, the filter corresponding to the filter objective function is a constant phase filter.
Preferably, the filter objective function satisfies:
F＝A0·exp(iP0)；
wherein, P0Is a phase coefficient, A0For the amplitude coefficient, i is in imaginary units.
Preferably, the phase coefficient is calculated by a first preset expression, where the first preset expression is:
P0＝arctan(-hilbert(xcor(Y,X))/xcor(Y,X))；
xcor (Y, X) is a cross-correlation function of the time-shifted source data and the target data estimated at the zero-delay time, and hilbert (xcor (Y, X)) is a hilbert transform function of the cross-correlation of the time-shifted source data and the target data estimated at the zero-delay time.
Preferably, the amplitude coefficient is calculated by a second preset expression, where the second preset expression is:
acor (x) is the source data autocorrelation function at zero delay time, and acor (y) is the target data autocorrelation function at zero delay time.
Preferably, wavelet matching is achieved by convolution of the filter objective function with the source data points.
According to a second aspect of the present invention, there is provided a computer storage medium having stored therein a computer program which, when executed by one or more processors, implements a method of seismic data signal to noise ratio enhancement as described above.
According to a third aspect of the present invention, there is provided a computer system comprising a processor and a memory, the memory having stored therein a computer program which, when executed by the processor, implements a method of seismic data signal to noise ratio enhancement as described above.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
by applying the method for improving the signal-to-noise ratio of the seismic data, the filtering factor obtained by single-channel calculation is more reliable, the difference caused by the inconsistency of amplitude, phase and frequency spectrum among wavelets is solved, the signal-to-noise ratio of the seismic data is further improved, and the method can be applied to matched filtering of the seismic data under different excitation, receiving and other conditions.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. Wherein the included drawings are:
FIG. 1 is a flowchart of an overall method of an embodiment of the present invention;
FIG. 2 is a graph showing the results corresponding to FIG. 1;
FIG. 3 is a flow chart of a method for constructing a least squares fit expression according to the present invention;
FIG. 4 is a diagram of source data for a probe region in the northwest;
FIG. 5 is a schematic illustration of the target data of FIG. 4;
FIG. 6 is a schematic diagram of source data after applying the method for improving signal-to-noise ratio of seismic data according to the present invention;
FIG. 7 is a cross-sectional diagram illustrating the difference between the target data in FIG. 5 and the source data in FIG. 4;
FIG. 8 is a cross-sectional view of the difference between the target data in FIG. 5 and the source data in FIG. 6 after applying the method for improving the signal-to-noise ratio of seismic data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the following will describe in detail an implementation method of the present invention with reference to the accompanying drawings and embodiments, so that how to apply technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
In the prior art, the matching factor obtained based on the multi-channel set has the problem that the signal-to-noise ratio of seismic data is low due to low reliability.
Example one
The present embodiment provides a method for improving the signal-to-noise ratio of seismic data, which mainly comprises the following steps S101 to S105.
Fig. 1 shows a flow chart of the overall method of an embodiment of the invention, fig. 2 shows a result diagram corresponding to fig. 1, the source data and the target data shown in fig. 2 are obtained in step S101, the design of the cross-correlation operator shown corresponds to step S102 and step S103, the application of the operator shown to the source data corresponds to step S104, and the output shown corresponds to step S105.
Specifically, as shown in fig. 1 and 2:
in step S101, a plurality of sets of corresponding channel data points of source data and target data are obtained, a first dimension of the three-dimensional data is a survey line, a second dimension of the three-dimensional data is each channel along the survey line, a third dimension of the three-dimensional data can be a time domain or a depth domain according to different domains, the domain where the invention is located is a time domain, the source data in the application refers to a source data channel set, the target data refers to a target data channel set, and are seismic channels, a certain corresponding data point of the source data channel set and the target data channel set of the second dimension needs to be read to perform single channel calculation on the seismic channels, and the single channel calculation is to perform wavelet matching work by recalculating corresponding filter objective functions for each set of the source data channel set and the target data channel set corresponding to each set.
Further, the method for improving the signal-to-noise ratio of the seismic data further includes step S102, fig. 3 shows a flow chart of a method for constructing the least square fitting expression, and as shown in fig. 3, the method for constructing the least square fitting expression mainly includes the following steps S1021 to S1025.
In step S1021, the source data is filtered to obtain filtered source data.
In step S1022, the target data is inverse-filtered to obtain inverse-filtered target data, and the actual seismic record is affected by absorption, so that the high-frequency component of the seismic source pulse is lost, the duration is prolonged, the time length of the seismic wave can be shortened by inverse-filtering the target data, and the resolution of the seismic data is improved.
In step S1023, the square of the difference between the target data and the filtered source data is taken as a first expression.
In step S1024, the square of the difference between the source data and the target data after inverse filtering is taken as a second expression.
In step S1035, the sum of the first expression and the second expression is constructed as a least square fit expression.
Then the least squares fit expression satisfies:
E＝(F·X-Y)2+(X-F-1·Y)2。
where F is the filter objective function in the frequency domain, X is the source data, and Y is the target data.
Further, the filter corresponding to the filter objective function is a constant phase filter, and F is a constant phase filter, that is, an expression about the phase spectrum and the amplitude spectrum.
Further, the filter objective function satisfies:
F＝A0·exp(iP0)。
wherein, P0Is a phase coefficient, A0For the amplitude coefficient, i is in imaginary units.
Specifically, the phase coefficient is calculated by a first preset expression, where the first preset expression is:
P0＝arctan(-hilbert(xcor(Y,X))/xcor(Y,X))。
xcor (Y, X) is a cross-correlation function of the time-shifted source data and the target data estimated at the zero-delay time, and hilbert (xcor (Y, X)) is a hilbert transform function of the cross-correlation of the time-shifted source data and the target data estimated at the zero-delay time.
The relation between the amplitude frequency and the phase frequency, and the real part and the imaginary part of the Fourier transform can be established through a Hilbert transform function; the sampling rate of the seismic signal can be reduced by constructing the corresponding analytic signal to contain only positive frequency components.
Calculating the amplitude coefficient through a second preset expression, wherein the second preset expression is as follows:
acor (x) is the source data autocorrelation function at zero delay time, and acor (y) is the target data autocorrelation function at zero delay time.
The phase coefficient P can be utilized0And amplitude coefficient A0Characterizing the filter objective function F, and then relating the phase coefficient P to the filter objective function0And amplitude coefficient A0And the functions are substituted into a least square fitting expression to perform subsequent calculation, and zero delay time is selected for the functions, so that the calculation process can be further simplified.
In step S103, for each set of corresponding channel data points, a filter objective function corresponding to the set of corresponding channel data points and a minimum value of a preset least square fitting expression is determined, so that the wavelet can be processed by the filter objective function, and then wavelet matching work is performed.
In step S104, wavelet matching is performed on the source data point of the corresponding channel data point according to the filter objective function, so as to obtain a source data point wavelet-matched with the source data point.
Specifically, wavelet matching is realized by convolution of a filter target function and a source data point, and the calculation mode is simple.
In step S105, the wavelet-matched source data points obtained from the multiple sets of corresponding channel data points are sorted, so as to obtain wavelet-matched source data.
Thus, the method utilizes the amplitude coefficient A0And phase coefficient P0The target function F of the filter is characterized and is substituted into a least square fitting expression E, the value of the least square fitting expression E is minimized, and the amplitude coefficient A corresponding to the corresponding data point of the group is obtained0And phase coefficient P0Then, the filter objective function F of the channel is calculated, the filter objective function F and the source data point are convoluted to obtain the source data point after the wavelets in the channel are matched, then other channels are calculated successively, and finally the source data points obtained by calculation of the longitudinal measuring line are sequenced according to the channel sequence, so that the source data after the operator is applied is obtained. Therefore, the filtering factor obtained by single-channel calculation is more reliable, the difference caused by the inconsistency of amplitude, phase and frequency spectrum among wavelets is solved, the signal-to-noise ratio of seismic data is further improved, and the method can be applied to matched filtering of seismic data under different excitation and receiving conditions.
The embodiment performs calculation on actual data to verify the correctness of the operator in the method.
Fig. 4 shows a source data diagram of a northwest probe area, and fig. 5 shows a target data diagram of fig. 4, and the present invention aims to match the source data and the target data of the northwest probe area in amplitude and phase.
According to step S101, a first X on a horizontal line corresponding to a vertical line of source data is read1Reading the first Y on the transverse line corresponding to one longitudinal measuring line of the target data1I.e. the current first source data point X for the source data and the target data1And target data point Y1。
According to step S102, the source data point X is processed1And target data point Y1Fitting expression E by least square method, using amplitude coefficient A0And phase coefficient P0Characterizing the filter objective function F1Fitting expression E by least square method1In the middle, order E1The minimum value of (A) is obtained, and the amplitude coefficient A corresponding to the first path is obtained0And phase coefficient P0Then the first pass filter objective function F is calculated1。
According to step S104, the filter objective function F is set1And source data point X1Performing convolution to obtain a source data point Z after wavelet matching1Then, the second and third … nth paths are calculated successively, and in the subsequent calculation, for each pair of source data point X and target data point Y, the corresponding target filter function F needs to be recalculated to perform wavelet matching work, that is, based on the single path calculation, the reliability of the calculated filter factor is higher.
According to step S105, the wavelet-matched source data points Z obtained by calculation of the longitudinal line are sorted according to the channel order, and source data to which an operator is applied, that is, a source data schematic diagram of the exploration area to which the seismic data signal-to-noise ratio improvement method of the present invention is applied, shown in fig. 6, is obtained.
The source data in fig. 4 and the target data in fig. 5 are subtracted to obtain a difference between the source data and the target data, which is a schematic cross-sectional view of the difference between the target data in fig. 5 and the source data in fig. 4 shown in fig. 7.
Then, the source data obtained by applying the method for improving the signal-to-noise ratio of seismic data in fig. 6 and the target data in fig. 5 are subtracted to obtain the difference between the source data and the target data after matching, that is, the schematic cross-sectional diagram of the difference between the target data in fig. 5 shown in fig. 8 and the source data obtained by applying the method for improving the signal-to-noise ratio of seismic data in fig. 6 is obtained.
It can be found from fig. 7 and fig. 8 that the difference between the source data and the target data becomes smaller after the seismic data signal-to-noise ratio improvement method of the invention is applied, and the effectiveness and the correctness of the seismic data signal-to-noise ratio improvement method of the invention in seismic data processing are demonstrated through the actual data of the northwest exploration area.
The effect analysis before and after the actual data processing shows that the seismic data signal-to-noise ratio improving method can realize the consistency adjustment of the amplitude, the phase or the frequency spectrum of the wavelets among different data, and solves the difference among section channels obtained by the inconsistency of the amplitude, the phase and the frequency spectrum among the wavelets.
Example two
The present embodiment provides a computer storage medium, in which a computer program is stored, and when the computer program is executed by one or more processors, the method for improving the signal-to-noise ratio of seismic data according to the first embodiment is implemented.
Specifically, the process of the computer program executed by the processor includes executing step a101 to step a105 as follows.
In performing step a101, sets of corresponding trace data points of the source data and the target data are obtained to enable single trace computation for the seismic traces.
Further, the processing method further includes performing step a102, and constructing a least squares fitting expression, which mainly includes performing step a1021 to performing step a 1025.
When step a1021 is executed, the source data is filtered to obtain filtered source data.
When step a1022 is executed, inverse filtering processing is performed on the target data to obtain inverse filtered target data.
In performing step a1023, the square of the difference between the target data and the filtered source data is taken as a first expression.
In executing step a1024, the square of the difference between the source data and the inverse-filtered target data is taken as a second expression.
In executing step a1035, the sum of the first expression and the second expression is constructed as a least squares fit expression.
Then the least squares fit expression satisfies:
E＝(F·X-Y)2+(X-F-1·Y)2。
where F is the filter objective function in the frequency domain, X is the source data, and Y is the target data.
Further, the filter corresponding to the filter objective function is a constant phase filter, and F is a constant phase filter, that is, an expression about the phase spectrum and the amplitude spectrum.
Further, the filter objective function satisfies:
F＝A0·exp(iP0)。
wherein, P0Is a phase coefficient, A0For the amplitude coefficient, i is in imaginary units.
Specifically, the phase coefficient is calculated by a first preset expression, where the first preset expression is:
P0＝arctan(-hilbert(xcor(Y,X))/xcor(Y,X))。
xcor (Y, X) is a cross-correlation function of the time-shifted source data and the target data estimated at the zero-delay time, and hilbert (xcor (Y, X)) is a hilbert transform function of the cross-correlation of the time-shifted source data and the target data estimated at the zero-delay time.
Calculating the amplitude coefficient through a second preset expression, wherein the second preset expression is as follows:
acor (x) is the source data autocorrelation function at zero delay time, and acor (y) is the target data autocorrelation function at zero delay time.
Then the available phase systemNumber P0And amplitude coefficient A0Characterizing the filter objective function F, and then relating the phase coefficient P to the filter objective function0And amplitude coefficient A0And the functions are substituted into a least square fitting expression to perform subsequent calculation, and zero delay time is selected for the functions, so that the calculation process can be further simplified.
When step a103 is executed, for each group of corresponding channel data points, a filter objective function corresponding to the minimum value of the group of corresponding channel data points and a preset least square fitting expression is determined, so that the wavelet can be processed through the filter objective function, and then the wavelet matching work is performed.
When step a104 is executed, wavelet matching is performed on the source data point of the corresponding channel data point according to the filter objective function, and a source data point wavelet-matched with the source data point is obtained.
Specifically, wavelet matching is realized by convolution of a filter target function and a source data point, and the calculation mode is simple.
When step a105 is executed, the source data points after wavelet matching obtained by the multiple sets of corresponding channel data points are sequenced, and source data after wavelet matching can be obtained.
EXAMPLE III
The embodiment provides a computer system, which includes a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the method for improving the signal-to-noise ratio of seismic data according to the first embodiment.
The technical scheme of the invention is explained in detail above, and in consideration of the related art, when wavelet matching is performed, several recording channels with strong contrast are sometimes selected in a recording time window to obtain a matching factor of a target data channel set and a source data channel set when a filtering factor is obtained, and the matching factor obtained based on the multiple channel sets has the problem of low reliability, which results in low signal-to-noise ratio of processed seismic data. The seismic data signal-to-noise ratio improving method, the storage medium and the system provided by the invention utilize the amplitude coefficient A0And phase coefficient P0Characterization ofThe filter target function F is substituted into a least square fitting expression E, the value of the least square fitting expression E is minimized, and the amplitude coefficient A corresponding to the corresponding data point of the group is obtained0And phase coefficient P0Then, the filter objective function F of the channel is calculated, the filter objective function F and the source data point are convoluted to obtain the source data point after the wavelets in the channel are matched, then other channels are calculated successively, and finally the source data points obtained by calculation of the longitudinal measuring line are sequenced according to the channel sequence, so that the source data after the operator is applied is obtained. Therefore, the filtering factor obtained by single-channel calculation is more reliable, the difference caused by the inconsistency of amplitude, phase and frequency spectrum among wavelets is solved, the signal-to-noise ratio of seismic data is further improved, and the method can be applied to matched filtering of seismic data under different excitation and receiving conditions.
In several embodiments provided herein, it should be understood that the seismic data signal-to-noise ratio enhancement method of the present invention can be stored in a computer readable storage medium or computer system, which is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium or a computer system and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for improving the signal-to-noise ratio of seismic data is characterized by comprising the following steps:
obtaining a plurality of groups of corresponding track data points of source data and target data;
aiming at each group of corresponding channel data points, determining a filter target function corresponding to the corresponding channel data point and the minimum value of a preset least square fitting expression;
performing wavelet matching on the source data point of the corresponding channel data point according to the filter target function to obtain a source data point which is subjected to wavelet matching with the source data point;
and sequencing the source data points after wavelet matching obtained by the multiple groups of corresponding channel data points to obtain source data after wavelet matching, so as to eliminate the difference caused by the inconsistency of wavelets and improve the signal-to-noise ratio of the seismic data.
2. The method of improving signal-to-noise ratio of seismic data according to claim 1, wherein: further comprising, constructing the least squares fit expression comprising:
filtering the source data to obtain filtered source data;
performing inverse filtering processing on the target data to obtain inverse filtered target data;
taking the square of the difference between the target data and the filtered source data as a first expression;
taking the square of the difference between the source data and the target data after inverse filtering as a second expression;
and constructing the sum of the first expression and the second expression as the least square fitting expression.
3. The method of improving signal-to-noise ratio of seismic data of claim 2, wherein: the least squares fitting expression satisfies:
E＝(F·X-Y)2+(X-F-1·Y)2；
where F is the filter objective function in the frequency domain, X is the source data, and Y is the target data.
4. The method of claim 3 for improving signal-to-noise ratio of seismic data, wherein: and the filter corresponding to the filter objective function is a constant-phase filter.
5. The method of claim 3 or 4 for improving signal-to-noise ratio of seismic data, wherein: the filter objective function satisfies:
F＝A0·exp(iP0)；
wherein, P0Is a phase coefficient, A0For the amplitude coefficient, i is in imaginary units.
6. The method of claim 5 for improving signal-to-noise ratio of seismic data, wherein: calculating the phase coefficient through a first preset expression, wherein the first preset expression is as follows:
P0＝arctan(-hilbert(xcor(Y,X))/xcor(Y,X))；
xcor (Y, X) is a cross-correlation function of the time-shifted source data and the target data estimated at the zero-delay time, and hilbert (xcor (Y, X)) is a hilbert transform function of the cross-correlation of the time-shifted source data and the target data estimated at the zero-delay time.
7. The method of claim 5 for improving signal-to-noise ratio of seismic data, wherein: calculating the amplitude coefficient through a second preset expression, wherein the second preset expression is as follows:
acor (x) is the source data autocorrelation function at zero delay time, and acor (y) is the target data autocorrelation function at zero delay time.
8. The method of improving signal-to-noise ratio of seismic data according to claim 1, wherein: wavelet matching is achieved by convolution of the filter objective function with the source data points.
9. A computer storage medium, characterized in that: the computer storage medium having stored thereon a computer program that, when executed by one or more processors, implements the method for seismic data signal-to-noise ratio enhancement of any of claims 1-8.
10. A computer system, characterized by: comprising a processor and a memory, in which a computer program is stored which, when executed by the processor, implements a method of improving signal-to-noise ratio of seismic data as claimed in any one of claims 1 to 8.
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