CN115980845A - Wavelet extraction method, system and equipment based on excitation phase prior constraint - Google Patents
Wavelet extraction method, system and equipment based on excitation phase prior constraint Download PDFInfo
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Abstract
The invention belongs to the technical field of oil and gas field exploration information processing, and discloses a wavelet extraction method, a system and equipment based on excitation phase prior constraint. Inputting wavelets to extract post-stack seismic data in corresponding space and time window ranges; carrying out autocorrelation on the input seismic traces; carrying out Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum; inputting far-field wavelets excited by an air gun seismic source during field acquisition; simulating seismic source ghost waves of the far-field wavelets according to the sea surface reflection coefficient; calculating the delay of the seismic source ghost waves according to the seismic source sinking depth, and fusing the far-field wavelet and the seismic source ghost waves to obtain an excitation wavelet; time shifting the excitation wavelet to make the peak and the trough symmetrical about the zero moment; fourier transform is carried out on the excited wavelet after time shift, and a wavelet phase spectrum is obtained; and fusing the wavelet amplitude spectrum and the wavelet phase spectrum, and performing inverse Fourier transform to obtain the seismic wavelet. The invention solves the technical problem that the conventional statistical wavelet extraction method cannot realize accurate phase estimation.
Description
Technical Field
The invention belongs to the technical field of oil and gas field exploration information processing, and particularly relates to a wavelet extraction method, a wavelet extraction system and wavelet extraction equipment based on excitation phase prior constraint.
Background
The extraction of the seismic wavelets is a very key problem in seismic exploration, and the accuracy of wavelet extraction has great influence on forward modeling, processing, inversion and explanation of the earthquake. For the forward modeling of the earthquake, the seismic wavelet is the basis for forward modeling based on a convolution model or a wave equation; for seismic processing, in the splicing processing of seismic data of different work areas, the accurate extraction of seismic wavelets is the premise of realizing the fine matching of amplitude, phase and frequency of different work areas; for seismic inversion and interpretation, different seismic wavelets tend to have different effects on the inversion and interpretation results.
At present, seismic wavelet extraction methods mainly include two categories, namely deterministic seismic wavelet extraction and statistical seismic wavelet extraction. If the well is drilled in the research area, a deterministic seismic wavelet extraction method is generally adopted, and the method firstly utilizes logging data to calculate a reflection coefficient sequence and then combines well-side seismic channels to obtain the seismic wavelets according to a convolution theory. If the area of interest has not been drilled, then a statistical seismic wavelet extraction method is used, which is based on the assumptions that the seismic wavelets are time invariant, the subsurface reflection coefficients are random sequences with white noise spectra, the seismic traces are free of noise, etc., then the autocorrelation of the observed seismic traces gives an estimate of the seismic wavelet autocorrelation, i.e., the amplitude spectrum of the seismic wavelets is known, but for the phase spectrum of the seismic wavelets, an assumption must be given, such as minimum phase, zero phase, or maximum phase, whereas seismic wavelets tend to be mixed-phase regardless of terrestrial or marine seismic data, so conventional phase-hypothesis-based extraction of seismic wavelets is generally inaccurate.
When marine seismic data are collected, wavelets excited by an air gun source can be accurately obtained, so that a novel statistical seismic wavelet extraction method is developed, the excitation wavelet phase is used as prior information to constrain the seismic wavelet extraction process, and the technical problem that the conventional statistical wavelet extraction method cannot realize accurate phase estimation can be effectively solved.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) The conventional statistical wavelet extraction technology generally assumes the wavelet phase, and cannot realize accurate phase estimation, so that the seismic wavelet is inaccurate in extraction, the seismic forward modeling, processing, inversion and interpretation precision is influenced, and the seismic exploration effect is reduced.
(2) The existing statistical wavelet extraction technology can not realize the extraction of mixed phase wavelets, the practical range is narrow.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiments of the present disclosure provide a wavelet extraction method, system, and device based on excitation phase prior constraint.
The technical scheme is as follows: the wavelet extraction method based on the excitation phase prior constraint comprises the following steps:
s1, inputting wavelets to extract post-stack seismic data in a corresponding space and time window range;
s2, performing autocorrelation on the input seismic channels;
s3, carrying out Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum;
s4, inputting far-field wavelets excited by an air gun seismic source during field acquisition;
s5, simulating seismic source ghost waves of the far-field wavelets according to the sea surface reflection coefficient;
s6, when the delay of the seismic source ghost waves is calculated according to the seismic source sinking depth, the far-field wavelets and the seismic source ghost waves are fused to obtain excitation wavelets;
s7, time shifting is carried out on the excited wavelets, and the wave crests and the wave troughs are symmetrical about the zero moment;
s8, carrying out Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum;
and S9, fusing the wavelet amplitude spectrum obtained in the step S3 and the wavelet phase spectrum obtained in the step S8, and performing inverse Fourier transform to obtain the seismic wavelet.
In the step S1, determining the input space range of the seismic channels according to the space position extracted by the wavelet, and selecting 5-7 seismic channels from the space position extracted by the wavelet; and determining the input time window range of the seismic channel according to the time window range extracted by the wavelets and the time length of the wavelets.
In step S2, the autocorrelation is calculated as:
in the formula, N represents a sequence length, tau represents a sampling point, tau =0,1,2 \8230, N-1, t represents a time delay, x (tau) represents an original sequence, x (tau-t) represents a sequence at the time of tau-t, and y (t) represents a post-autocorrelation sequence.
In step S3, performing fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum includes: carrying out Fourier transform on the autocorrelation result, and according to the assumption that the autocorrelation of the seismic channel is equal to the autocorrelation of the wavelet, the energy spectrum of the seismic channel is equal to the energy spectrum of the wavelet, wherein the expression is as follows:
ES wav ＝FFT[Autocor(Seis)]
in the formula, ES wav Representing the energy spectrum of the wavelet, FFT representing fast Fourier transform, autocor representing autocorrelation, and Seis representing a seismic trace;
according to the relation between the energy spectrum and the amplitude spectrum, squaring the energy spectrum to obtain the amplitude spectrum of the wavelet, wherein the expression is as follows:
in the formula, AS wav Representing the amplitude spectrum of the sub-waves.
In step S5, in the seismic source ghost simulating the far-field wavelet according to the sea surface reflection coefficient, since polarity inversion occurs when the seismic source ghost propagates to the sea surface, the seismic source ghost can be represented as:
W ghost ＝-1·RC sea ·W far
in the formula, W ghost Representing seismic ghost, RC sea Representing sea surface reflection coefficient, W far Representing the far-field wavelet.
In step S6, when the delay of the seismic source ghost is calculated according to the seismic source sinking depth, the expression is:
in the formula, t delay Representing the delay of ghost waves from the seismic source, S depth Indicating the depth of seismic source subsidence, V sea Representing the propagation velocity of seismic waves in sea water, based on the calculated delay time t delay Fusing the far-field wavelet input in the step S4 with the seismic source ghost wave obtained by simulating in the step S5 to obtain an excitation wavelet W fire 。
At step S8, the wavelet phase spectrum is expressed as:
in the formula, PS wav Representing the phase spectrum of the wavelet, the FFT represents the fast fourier transform,
representing the time-shifted excitation wavelet.
In step S9, fusing the wavelet amplitude spectrum obtained in step S3 and the wavelet phase spectrum obtained in step S8, and performing inverse Fourier transform to obtain a seismic wavelet; the method specifically comprises the following steps:
carrying out inverse Fourier transform on the frequency spectrum of the wavelet to obtain a seismic wavelet, wherein the expression is as follows:
W seis ＝IFFT[FS wav ]
in the formula, W seis Representing seismic wavelets, IFFT representing the inverse fast Fourier transform, FS wav Representing the spectrum of the wavelet.
Another object of the present invention is to provide an extraction system for implementing the wavelet extraction method based on excitation phase prior constraint, the extraction system comprising:
the post-stack seismic data extraction module is used for inputting the wavelets to extract post-stack seismic data in a corresponding space and time window range;
the autocorrelation module is used for performing autocorrelation on the input seismic traces;
the wavelet amplitude spectrum acquisition module is used for carrying out Fourier transform on the autocorrelation result to acquire a wavelet amplitude spectrum;
the far-field wavelet input module is used for inputting the far-field wavelets excited by the air gun seismic source during field acquisition;
the seismic source ghost simulation module is used for simulating seismic source ghost of the far-field wavelet according to the sea surface reflection coefficient;
the excitation wavelet obtaining module is used for fusing the far-field wavelet and the seismic source ghost wave to obtain an excitation wavelet when the delay of the seismic source ghost wave is calculated according to the seismic source sinking depth;
the zero moment symmetrical module is used for time shifting the excitation wavelet, so that the wave crest and the wave trough are symmetrical about the zero moment;
the wavelet phase spectrum module is used for carrying out Fourier transform on the excitation wavelets after time shift to obtain a wavelet phase spectrum;
and the seismic wavelet obtaining module is used for fusing the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum and carrying out inverse Fourier transform to obtain the seismic wavelet.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the excitation phase prior constraint based wavelet extraction method.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the excitation phase prior constraint based wavelet extraction method.
By combining all the technical schemes, the invention has the advantages and positive effects that:
first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with results, data and the like in the research and development process, and how to solve the technical scheme of the present invention is deeply analyzed in detail, and some creative technical effects brought by the solution of the problems are specifically described as follows:
the invention provides a wavelet extraction method based on excitation phase prior constraint, which is used for extracting post-stack seismic data in a corresponding space and time window range by inputting wavelets; carrying out autocorrelation on input seismic traces; carrying out Fourier transformation on the autocorrelation result to obtain an amplitude spectrum of the wavelet; inputting far-field wavelets excited by an air gun seismic source; simulating seismic source ghost waves of the far-field wavelets; fusing the far-field wavelet and the seismic source ghost wave to obtain an excited wavelet; time shifting the excitation wavelet to make the peak and the trough symmetrical about the zero moment; carrying out Fourier transform on the time-shifted excitation wavelets to obtain phase spectrums of the wavelets; and fusing the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum, and performing inverse Fourier transform to obtain the seismic wavelet. By adopting the scheme, the invention takes the excitation wavelet phase as the prior information to constrain the seismic wavelet extraction process, and solves the technical problem that the conventional statistical wavelet extraction method cannot realize accurate phase estimation.
Secondly, regarding the technical solution as a whole or from the perspective of products, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows:
the invention is particularly suitable for the condition that deterministic wavelet extraction cannot be carried out and only statistical wavelet extraction can be carried out due to the fact that no well is drilled in a research area or the logging curve is incomplete.
Thirdly, the inventive auxiliary proof as the claimed invention is also embodied in the following important aspects:
(1) The technical scheme of the invention improves and perfects the technical defects of the conventional statistical wavelet extraction method, does not need to assume the phase, and solves the technical problem that the statistical wavelet extraction method cannot realize accurate phase estimation.
(2) The technical scheme of the invention can be applied to a plurality of fields of seismic forward modeling, seismic data fine splicing processing, seismic inversion and interpretation and the like, supports reservoir stratum and hydrocarbon detection research of oil and gas fields, and has great popularization and application and commercial value in oil and gas field exploration.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure;
FIG. 1 is a flowchart of a wavelet extraction method based on excitation phase prior constraint according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a wavelet extraction method based on excitation phase prior constraint according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a wavelet extraction system based on excitation phase prior constraint according to an embodiment of the present invention;
FIG. 4 is a graph of wavelets having a time length of 120 milliseconds and input seismic traces with a time window ranging from 1300 to 1750 milliseconds, according to an embodiment of the present invention;
FIG. 5 is an amplitude spectrum of a sub-wave provided by an embodiment of the present invention;
FIG. 6 is a far field wavelet plot of air gun seismic source excitation during field acquisition as provided by embodiments of the present invention;
FIG. 7 shows a seismic ghost image obtained by simulation, with a sea surface reflection coefficient of 0.9 according to an embodiment of the present invention;
FIG. 8 illustrates an embodiment of the present invention that fuses the input far-field wavelet with the seismic source ghost obtained through simulation according to the calculated 8 ms delay to obtain an excitation wavelet W fire A drawing;
FIG. 9 is a time-shifted diagram of an excitation wavelet after shifting the excitation wavelet to a negative time by 7 milliseconds, where peaks and troughs of the excitation wavelet are symmetric with respect to a zero time;
FIG. 10 is a graph of a phase spectrum of wavelets provided by an embodiment of the present invention;
FIG. 11 illustrates the resulting seismic wavelets provided by embodiments of the present invention;
in the figure: 1. a post-stack seismic data extraction module; 2. an autocorrelation module; 3. a wavelet amplitude spectrum obtaining module; 4. a far-field wavelet input module; 5. a seismic source ghost simulation module; 6. an excitation wavelet obtaining module; 7. a zero time symmetry module; 8. a wavelet phase spectrum module; 9. and a seismic wavelet obtaining module.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
1. Illustrative examples are illustrated:
example 1
As shown in fig. 1, the wavelet extraction method based on excitation phase prior constraint provided in the embodiment of the present invention includes the following steps:
s1, inputting wavelets to extract post-stack seismic data in a corresponding space and time window range;
s2, performing autocorrelation on the input seismic channels;
s3, carrying out Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum;
s4, inputting far-field wavelets excited by an air gun seismic source during field acquisition;
s5, simulating seismic source ghost waves of the far-field wavelets according to the sea surface reflection coefficient;
s6, when the delay of the seismic source ghost waves is calculated according to the seismic source sinking depth, far-field wavelets and the seismic source ghost waves are fused to obtain excitation wavelets;
s7, time shifting is carried out on the excited wavelets, and the wave crests and the wave troughs are symmetrical about the zero moment;
s8, carrying out Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum;
and S9, fusing the wavelet amplitude spectrum obtained in the step S3 and the wavelet phase spectrum obtained in the step S8, and performing inverse Fourier transform to obtain the seismic wavelet.
In the embodiment of the present invention, fig. 2 is a schematic diagram of a wavelet extraction method based on excitation phase prior constraint.
Example 2
Based on the wavelet extraction method based on the excitation phase prior constraint provided by the embodiment 1, further, in the step S1, an input spatial range of seismic channels is determined according to a spatial position of wavelet extraction, and 5 to 7 seismic channels which are closest to the wavelet extraction spatial position and have higher signal-to-noise ratio are selected; and determining the input time window range of the seismic channel according to the time window range extracted by the wavelets and the time length of the wavelets, wherein the time length of the wavelets is 100-200 milliseconds, and the input time window range of the seismic channel is 3-5 times of the time length of the wavelets.
Example 3
Based on the wavelet extraction method based on the excitation phase prior constraint provided in embodiment 1, further, in step S2, autocorrelation is performed on the input seismic traces, and a calculation formula of the autocorrelation is as follows:
in the formula, N represents a sequence length, tau represents a sampling point, tau =0,1,2 \8230, N-1, t represents a time delay, x (tau) represents an original sequence, x (tau-t) represents a sequence at the time of tau-t, and y (t) represents a post-autocorrelation sequence.
Example 4
Based on the wavelet extraction method based on the excitation phase prior constraint provided in embodiment 1, further, in step S3, fourier transform is performed on the autocorrelation result, and according to the assumption that the autocorrelation of a seismic trace is equal to the autocorrelation of a wavelet, the energy spectrum of the seismic trace is equal to the energy spectrum of the wavelet, and the expression is as follows:
ES wav ＝FFT[Autocor(Seis)]
in the formula, ES wav Representing the energy spectrum of the wavelet, FFT representing fast Fourier transform, autocor representing autocorrelation, and Seis representing a seismic trace;
according to the relation between the energy spectrum and the amplitude spectrum, squaring the energy spectrum to obtain the amplitude spectrum of the wavelet, wherein the expression is as follows:
in the formula, AS wav Representing the amplitude spectrum of the wavelet.
Example 5
Based on the wavelet extraction method based on the excitation phase prior constraint provided in embodiment 1, further, in step S5, the seismic source ghost of the far-field wavelet is simulated according to the sea surface reflection coefficient, and because the polarity of the seismic source ghost is reversed when the seismic source ghost propagates to the sea surface, the seismic source ghost can be represented as:
W ghost ＝-1·RC sea ·W far
in the formula, W ghost Representing seismic ghost, RC sea Representing sea surface reflection coefficient, W far Representing the far-field wavelet.
Example 6
Based on the excitation phase prior constraint-based wavelet extraction method provided in embodiment 1, further, in step S6, when the delay of the seismic source ghost is calculated according to the seismic source sinking depth, the expression is as follows:
in the formula, t delay Representing the delay of ghost waves from the seismic source, S depth Indicating the depth of seismic source subsidence, V sea Representing the propagation velocity of seismic waves in the sea, based on the calculated delay time t delay Fusing the far-field wavelet input in the step S4 with the seismic source ghost wave obtained by simulating in the step S5 to obtain an excitation wavelet W fire 。
Example 7
Based on the wavelet extraction method based on the excitation phase prior constraint provided in embodiment 1, further, in step S8, fourier transform is performed on the time-shifted excitation wavelet to obtain a phase spectrum of the wavelet, where the expression is:
in the formula, PS wav Representing the phase spectrum of the wavelet, the FFT representing the fast fourier transform,
representing the time-shifted excitation wavelet.
Example 8
Based on the wavelet extraction method based on the excitation phase prior constraint provided in embodiment 1, further, in the step S9, the wavelet amplitude spectrum AS obtained in the step S3 is fused wav And the wavelet phase spectrum PS obtained in the eighth step wav Obtaining the frequency spectrum FS of the wavelet wav Performing inverse Fourier transform on the frequency spectrum of the wavelet to obtain a seismic wavelet, wherein the expression is as follows:
W seis ＝IFFT[FS wav ]
in the formula, W seis Representing seismic wavelets, IFFT representing the inverse fast Fourier transform, FS wav Representing the spectrum of the wavelet.
Example 9
As shown in fig. 3, an embodiment of the present invention provides a wavelet extraction system based on excitation phase prior constraint, including:
the post-stack seismic data extraction module 1 is used for inputting wavelets to extract post-stack seismic data in a corresponding space and time window range;
the autocorrelation module 2 is used for performing autocorrelation on the input seismic traces;
the wavelet amplitude spectrum obtaining module 3 is used for carrying out Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum;
the far-field wavelet input module 4 is used for inputting the far-field wavelets excited by the air gun seismic source during field acquisition;
the seismic source ghost simulation module 5 is used for simulating seismic source ghost of the far-field wavelet according to the sea surface reflection coefficient;
the excitation wavelet obtaining module 6 is used for fusing the far-field wavelet and the seismic source ghost wave to obtain an excitation wavelet when the delay of the seismic source ghost wave is calculated according to the seismic source sinking depth;
a zero moment symmetry module 7, configured to perform time shifting on the excitation wavelet, so that a peak and a trough are symmetric with respect to a zero moment;
the wavelet phase spectrum module 8 is used for carrying out Fourier transform on the excitation wavelets after time shift to obtain a wavelet phase spectrum;
and the seismic wavelet obtaining module 9 is used for fusing the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum and performing inverse Fourier transform to obtain the seismic wavelet.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
For the information interaction, execution process and other contents between the above devices/units, the specific functions and technical effects brought by the method embodiments of the present invention based on the same concept can be referred to the method embodiments, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments.
2. The application example is as follows:
application example 1
As shown in fig. 2, the present invention specifically proceeds according to the following steps:
the first step is as follows: inputting wavelets to extract post-stack seismic data in corresponding space and time window ranges;
determining the input space range of the seismic channels according to the space position extracted by the wavelet, and selecting the seismic channels which are closest to the wavelet extraction space position and have high signal-to-noise ratio, wherein the number of the seismic channels is 7 in the embodiment; the input time window range of the seismic channel is determined according to the time window range of wavelet extraction and the time length of the wavelet, the time length of the wavelet in the embodiment is 120 milliseconds, and the time window range of the input seismic channel is 1300-1750 milliseconds, as shown in fig. 4.
The second step is that: carrying out autocorrelation on the input seismic traces;
the formula for the autocorrelation is:
in the formula, N represents a sequence length, tau represents a sampling point, tau =0,1,2 \8230, N-1, t represents a time delay, x (tau) represents an original sequence, x (tau-t) represents a sequence at the time of tau-t, and y (t) represents a post-autocorrelation sequence.
And step S3: carrying out Fourier transform on the autocorrelation result to obtain an amplitude spectrum of the wavelet;
according to the assumption that the autocorrelation of the seismic trace is equal to the autocorrelation of the wavelet, the energy spectrum of the seismic trace is equal to the energy spectrum of the wavelet, and the expression is as follows:
ES wav ＝FFT[Autocor(Seis)]
in the formula, ES wav Representing the energy spectrum of the wavelet, FFT representing fast Fourier transform, autocor representing autocorrelation, and Seis representing a seismic trace;
according to the relation between the energy spectrum and the amplitude spectrum, squaring the energy spectrum to obtain the amplitude spectrum of the wavelet, wherein the expression is as follows:
in the formula, AS wav Representing the amplitude spectrum of the sub-waves.
And step S4: inputting far-field wavelets excited by an air gun seismic source during field acquisition;
the far-field wavelet excited by the air gun source during field acquisition in the embodiment is shown in fig. 6.
Step S5: simulating seismic source ghost waves of the far-field wavelets according to the sea surface reflection coefficient;
since the polarity reversal occurs when the seismic ghost propagates to the sea surface, the seismic ghost can be expressed as:
W ghost ＝-1·RC sea ·W far
in the formula, W ghost Representing seismic ghost, RC sea Representing sea surface reflection coefficient, W far Representing the far-field wavelet. In this embodiment, the sea surface reflection coefficient is 0.9, and the seismic source ghost wave obtained by simulation is shown in fig. 7.
And a sixth step: calculating the delay of the seismic source ghost waves according to the seismic source sinking depth, and fusing the far-field wavelet and the seismic source ghost waves to obtain an excitation wavelet; the delay time of the source ghost can be expressed as:
in the formula, t delay Representing the delay of ghost waves from the seismic source, S depth Indicating the depth of seismic source subsidence, V sea Representing the propagation velocity of seismic waves in the sea. In the embodiment, the sinking depth of the seismic source is 6 meters, the propagation speed of seismic waves in seawater is 1500 meters/second, and the delay time t of seismic source ghost waves is obtained through calculation delay Is 8 milliseconds. Fusing the far-field wavelet input in the step S4 with the seismic source ghost wave obtained by simulating in the step S5 according to the calculated 8-millisecond delay time to obtain an excitation wavelet W fire As shown in fig. 8.
The seventh step: time shifting the excitation wavelet to make the peak and the trough symmetrical about the zero moment;
in this embodiment, after the time shift of the exciton to the negative time is 7 milliseconds, the peak and the trough are symmetric about the zero time, and the time-shifted exciton is shown in fig. 9.
Eighth step: carrying out Fourier transform on the time-shifted excitation wavelets to obtain phase spectrums of the wavelets;
in the formula, PS wav Representing the phase spectrum of the wavelet, the FFT representing the fast fourier transform,
representing the time-shifted excitation wavelet, the phase spectrum of the wavelet in this embodiment is shown in FIG. 10.
The ninth step: and fusing the wavelet amplitude spectrum obtained in the step S3 and the wavelet phase spectrum obtained in the eighth step, and performing inverse Fourier transform to obtain the seismic wavelet.
Fusing the wavelet amplitude spectrum AS shown in FIG. 5 wav And the wavelet phase spectrum PS shown in FIG. 10 wav Obtaining a spectrum FS of seismic wavelets wav Performing inverse Fourier transform on the frequency spectrum of the seismic wavelet to obtain the seismic wavelet, wherein the expression is as follows:
W seis ＝IFFT[FS wav ]
in the formula, W seis Representing seismic wavelets, IFFT representing the inverse fast Fourier transform, FS wav Representing the spectrum of wavelets, the resulting seismic wavelets in this embodiment are shown in FIG. 11.
Application example 2
An embodiment of the present invention provides a computer device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments may be implemented.
The embodiment of the present invention further provides an information data processing terminal, where the information data processing terminal is configured to provide a user input interface to implement the steps in the above method embodiments when implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer, or a switch.
Embodiments of the present invention further provide a server, where the server is configured to provide a user input interface to implement the steps in the foregoing method embodiments when implemented on an electronic device.
Embodiments of the present invention provide a computer program product, which, when running on an electronic device, enables the electronic device to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
3. Evidence of the relevant effects of the examples:
a plurality of seismic acquisition work areas are spanned in 19-2 oil fields in Bohai and Bohai, and seismic data of different work areas need to be spliced in order to meet the overall exploration and research requirements of the oil fields. Due to different seismic acquisition parameters, the amplitude, frequency and phase of seismic data of different work areas have larger difference. By adopting the wavelet extraction method based on excitation phase prior constraint, wavelets of seismic data of different acquisition work areas are accurately extracted. And furthermore, the seismic wavelets are finely matched, so that the fine matching of the amplitude, the frequency and the phase of seismic data of different work areas is realized, the amplitude preservation of spliced seismic data is improved, a good data base is provided for reservoir and hydrocarbon detection research, and the exploration evaluation of a 19-2 oil field in the Bohai is strongly supported.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention should not be limited thereto, and any modifications, equivalents and improvements made by those skilled in the art within the technical scope of the present invention as disclosed in the present invention should be covered thereby.
Claims (10)
1. A wavelet extraction method based on excitation phase prior constraint is characterized by comprising the following steps:
s1, inputting wavelets to extract post-stack seismic data in a corresponding space and time window range;
s2, performing autocorrelation on the input seismic channels;
s3, carrying out Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum;
s4, inputting far-field wavelets excited by an air gun seismic source during field acquisition;
s5, simulating seismic source ghost waves of the far-field wavelets according to the sea surface reflection coefficient;
s6, when the delay of the seismic source ghost waves is calculated according to the seismic source sinking depth, far-field wavelets and the seismic source ghost waves are fused to obtain excitation wavelets;
s7, time shifting is carried out on the excited wavelets, and the wave crests and the wave troughs are symmetrical about the zero moment;
s8, carrying out Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum;
and S9, fusing the wavelet amplitude spectrum obtained in the step S3 and the wavelet phase spectrum obtained in the step S8, and performing inverse Fourier transform to obtain the seismic wavelet.
2. The wavelet extraction method based on excitation phase prior constraint of claim 1, wherein in step S1, the input spatial range of seismic channels is determined according to the spatial position of wavelet extraction, and 5-7 seismic channels are selected from the spatial position of wavelet extraction; and determining the input time window range of the seismic channel according to the time window range extracted by the wavelets and the time length of the wavelets.
3. The wavelet extraction method based on excitation phase prior constraint of claim 1, wherein in step S2, the calculation formula of autocorrelation is:
in the formula, N represents a sequence length, tau represents a sampling point, tau =0,1,2 \8230, N-1, t represents a time delay, x (tau) represents an original sequence, x (tau-t) represents a sequence at the time of tau-t, and y (t) represents a post-autocorrelation sequence.
4. The wavelet extraction method based on excitation phase prior constraint of claim 1, wherein in step S3, performing fourier transform on the autocorrelation result to obtain wavelet amplitude spectrum comprises: carrying out Fourier transform on the autocorrelation result, and according to the assumption that the autocorrelation of the seismic channel is equal to the autocorrelation of the wavelet, the energy spectrum of the seismic channel is equal to the energy spectrum of the wavelet, wherein the expression is as follows:
ES wav ＝FFT[Autocor(Seis)]
in the formula, ES wav Representing the energy spectrum of the wavelet, FFT representing fast Fourier transform, autocor representing autocorrelation, and Seis representing a seismic trace;
according to the relation between the energy spectrum and the amplitude spectrum, squaring the energy spectrum to obtain the amplitude spectrum of the wavelet, wherein the expression is as follows:
in the formula, AS wav Representing the amplitude spectrum of the wavelet.
5. The excitation phase prior constraint-based wavelet extraction method according to claim 1, wherein in step S5, in the source ghost simulating the far-field wavelet according to the sea surface reflection coefficient, the source ghost can be represented as:
W ghost ＝-1·RC sea ·W far
in the formula, W ghost Representing seismic ghost, RC sea Representing sea surface reflection coefficient, W far Representing the far-field wavelet.
6. The excitation phase prior constraint-based wavelet extraction method of claim 1, wherein in step S6, when the delay of the ghost wave of the seismic source is calculated according to the depth of the seismic source, the expression is:
in the formula, t delay Representing the delay of ghost waves from the seismic source, S depth Indicating the depth of seismic source subsidence, V sea Representing the propagation speed of seismic waves in seawater;
according to the calculated delay time t delay Fusing the far-field wavelet input in the step S4 with the seismic source ghost wave obtained by simulating in the step S5 to obtain an excitation wavelet W fire 。
7. The wavelet extraction method based on excitation phase prior constraint of claim 1, wherein in step S8, the expression of wavelet phase spectrum is:
in the formula, PS wav Representing the phase spectrum of the wavelet, the FFT represents the fast fourier transform,
representing the time-shifted excitation wavelet.
8. The wavelet extraction method based on excitation phase prior constraint of claim 1, wherein in step S9, the wavelet amplitude spectrum obtained in step S3 and the wavelet phase spectrum obtained in step S8 are fused, and inverse fourier transform is performed to obtain a seismic wavelet; the method specifically comprises the following steps:
carrying out inverse Fourier transform on the frequency spectrum of the wavelet to obtain a seismic wavelet, wherein the expression is as follows:
W seis ＝IFFT[FS wav ]
in the formula, W seis Representing seismic wavelets, IFFT representing the inverse fast Fourier transform, FS wav Representing the spectrum of the wavelet.
9. An extraction system for implementing the excitation phase prior constraint-based wavelet extraction method according to any one of claims 1 to 8, wherein the extraction system comprises:
the post-stack seismic data extraction module (1) is used for inputting wavelets to extract post-stack seismic data in a corresponding space and time window range;
the autocorrelation module (2) is used for performing autocorrelation on the input seismic traces;
the wavelet amplitude spectrum acquisition module (3) is used for carrying out Fourier transform on the autocorrelation result to acquire a wavelet amplitude spectrum;
the far-field wavelet input module (4) is used for inputting the far-field wavelet excited by the air gun seismic source during field acquisition;
the seismic source ghost simulation module (5) is used for simulating seismic source ghost of the far-field wavelet according to the sea surface reflection coefficient;
the excitation wavelet obtaining module (6) is used for fusing the far-field wavelet and the seismic source ghost wave to obtain an excitation wavelet when the delay of the seismic source ghost wave is calculated according to the seismic source sinking depth;
a zero moment symmetry module (7) for time shifting the excitation wavelet to make the peak and the trough symmetrical about the zero moment;
the wavelet phase spectrum module (8) is used for carrying out Fourier transform on the excitation wavelets after time shift to obtain a wavelet phase spectrum;
and the seismic wavelet obtaining module (9) is used for fusing the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum and carrying out inverse Fourier transform to obtain the seismic wavelet.
10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the excitation phase prior constraint-based wavelet extraction method of any one of claims 1-7.
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