Wavelet extraction method, system and device based on excitation phase prior constraint

Abstract

The present application belongs to the technical field of oil and gas field exploration information processing, and discloses a wavelet extraction method, system and device based on excitation phase priori constraint. The post-stack seismic data in the corresponding space and time window range of wavelet extraction is inputted; autocorrelation is performed on the input seismic trace; Fourier transform is performed on the autocorrelation result to obtain a wavelet amplitude spectrum; the far-field wavelet excited by the air gun source in field acquisition is inputted; the source ghost wave of the far-field wavelet is simulated according to the sea surface reflection coefficient; the delay time of the source ghost wave is calculated according to the source sinking depth, and the excitation wavelet is obtained by fusing the far-field wavelet and the source ghost wave; time shift is performed on the excitation wavelet to make the wave peak and the wave trough symmetrical about zero time; Fourier transform is performed on the time-shifted excitation wavelet to obtain a wavelet phase spectrum; the wavelet amplitude spectrum and the wavelet phase spectrum are fused, and inverse Fourier transform is performed to obtain a seismic wavelet. The present application solves the technical problem that the conventional statistical wavelet extraction method cannot realize accurate phase estimation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas field exploration information processing, and particularly relates to a wavelet extraction method, system and device based on excitation phase priori constraint. BACKGROUND

[0002] Seismic wavelet extraction is a very key problem in seismic exploration, and the precision of wavelet extraction has a great influence on seismic forward modeling, processing, inversion and interpretation. For seismic forward modeling, seismic wavelet is the basis for forward modeling based on convolution model or wave equation; for seismic processing, the accurate extraction of seismic wavelet is the premise for realizing fine matching of amplitude, phase and frequency in different work areas; for seismic inversion and interpretation, different seismic wavelets often have different influences on inversion and interpretation results.

[0003] At present, seismic wavelet extraction methods are mainly divided into two categories: deterministic seismic wavelet extraction and statistical seismic wavelet extraction. If there is a drilled well in the study area, the deterministic seismic wavelet extraction method is usually used, which firstly calculates the reflection coefficient sequence by using logging data, and then calculates the seismic wavelet by combining the well seismic trace according to the convolution theory. If there is no drilled well in the study area, the statistical seismic wavelet extraction method needs to be used, which is based on the assumptions that seismic wavelet is time-invariant, underground reflection coefficient is a random sequence with white noise spectrum, and seismic trace does not contain noise. The autocorrelation of the observed seismic trace gives an estimate of the autocorrelation of the seismic wavelet, that is, the amplitude spectrum of the seismic wavelet is known. However, for the phase spectrum of the seismic wavelet, an assumption must be made, such as minimum phase, zero phase or maximum phase. However, whether on land or at sea, the seismic wavelet is often of mixed phase, so the conventional seismic wavelet extracted based on the phase assumption is usually inaccurate.

[0004] When marine seismic data is collected, the wavelet excited by the air gun source can be accurately calculated, so a new statistical seismic wavelet extraction method is developed to use the excitation wavelet phase as prior information to constrain the seismic wavelet extraction process, which can effectively solve the technical problem that the conventional statistical seismic wavelet extraction method cannot accurately estimate the phase.

[0005] Through the above analysis, the problems and defects of the prior art are as follows:

[0006] (1) The existing statistical seismic wavelet extraction technology usually assumes the phase of the wavelet, which cannot accurately estimate the phase, resulting in inaccurate seismic wavelet extraction and affecting the precision of seismic forward modeling, processing, inversion and interpretation, and reducing the effectiveness of seismic exploration.

[0007] (2) The existing statistical seismic wavelet extraction technology cannot extract mixed phase wavelet, and the practical range is narrow. SUMMARY

[0008] To overcome the problems in the related art, the embodiment of the present application provides a wavelet extraction method and system based on excitation phase priori constraint.

[0009] The technical solution is as follows: the wavelet extraction method based on excitation phase priori constraint comprises the following steps:

[0010] S1, input the post-stack seismic data in the spatial range and time window range corresponding to wavelet extraction;

[0011] S2, autocorrelate the input seismic trace;

[0012] S3, perform Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum;

[0013] S4, input a far-field wavelet excited by a gun seismic source in field acquisition;

[0014] S5, simulate a source ghost wave of the far-field wavelet according to the sea surface reflection coefficient;

[0015] S6, calculate the delay time of the source ghost wave according to the source sinking depth, and fuse the far-field wavelet and the source ghost wave to obtain an excitation wavelet;

[0016] S7, perform time shift on the excitation wavelet to make the wave peak and the wave trough symmetric about zero time;

[0017] S8, perform Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum;

[0018] S9, fuse the wavelet amplitude spectrum obtained in step S3 and the wavelet phase spectrum obtained in step S8, and perform inverse Fourier transform to obtain a seismic wavelet.

[0019] In step S1, the input spatial range of the seismic trace is determined according to the spatial position of wavelet extraction, and 5-7 seismic traces away from the spatial position of wavelet extraction are selected; the input time window range of the seismic trace is determined according to the time window range of wavelet extraction and the time length of the wavelet.

[0020] In step S2, the calculation formula of autocorrelation is as follows:

[0021]

[0022] In the formula, N represents the sequence length, τ represents the sampling point, τ=0, 1, 2…N-1, t represents the time delay, x(τ) represents the original sequence, x(τ-t) represents the sequence at τ-t time, and y(t) represents the sequence after autocorrelation.

[0023] In step S3, the Fourier transform is performed on the autocorrelation result to obtain the wavelet amplitude spectrum, including: performing the Fourier transform on the autocorrelation result, and 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:

[0024] ES wav = FFT [Autocor (Seis)]

[0025] In the formula, ES wav represents the energy spectrum of the wavelet, FFT represents the fast Fourier transform, Autocor represents the autocorrelation, and Seis represents the seismic trace;

[0026] According to the relationship between the energy spectrum and the amplitude spectrum, the energy spectrum is squared to obtain the amplitude spectrum of the wavelet, and the expression is:

[0027]

[0028] In the formula, AS wav represents the amplitude spectrum of the wavelet.

[0029] In step S5, the source ghost wave of the far-field wavelet is simulated according to the sea surface reflection coefficient, and since the source ghost wave is inverted in polarity when it propagates to the sea surface, the source ghost wave can be expressed as:

[0030] W ghost = -1 · RC sea · W far

[0031] In the formula, W ghost represents the source ghost wave, RC sea represents the sea surface reflection coefficient, and W far represents the far-field wavelet.

[0032] In step S6, the delay time of the source ghost wave is calculated according to the source setting depth, and the expression is:

[0033]

[0034] In the formula, t delay represents the delay time of the source ghost wave, S depth represents the source setting depth, V sea represents the propagation speed of the seismic wave in seawater, and according to the calculated delay time t delay The far-field wavelet input in step S4 is fused with the source ghost wave simulated in step S5 to obtain the excitation wavelet W fire .

[0035] In step S8, the expression of the wavelet phase spectrum is:

[0036]

[0037] In the formula, PS wav represents the phase spectrum of the wavelet, and FFT represents a fast Fourier transform, represents the time-shifted excitation wavelet.

[0038] In step S9, the wavelet amplitude spectrum obtained in step S3 and the wavelet phase spectrum obtained in step S8 are fused, and an inverse Fourier transform is performed to obtain a seismic wavelet; specifically including:

[0039] The seismic wavelet is obtained by performing an inverse Fourier transform on the spectrum of the wavelet, and the expression is:

[0040] W seis = IFFT [FS wav ]

[0041] In the formula, W seis represents the seismic wavelet, IFFT represents a fast inverse Fourier transform, and FS wav represents the spectrum of the wavelet.

[0042] Another object of the present application is to provide an extraction system for realizing the wavelet extraction method based on the excitation phase prior constraint, which comprises:

[0043] A post-stack seismic data extraction module is configured to input post-stack seismic data in a corresponding space and time window range for wavelet extraction;

[0044] A self-correlation module is configured to perform self-correlation on the input seismic trace;

[0045] A wavelet amplitude spectrum acquisition module is configured to perform a Fourier transform on the self-correlation result to obtain a wavelet amplitude spectrum;

[0046] A far-field wavelet input module is configured to input a far-field wavelet excited by a gun seismic source in field acquisition;

[0047] A seismic source ghost wave simulation module is configured to simulate a seismic source ghost wave of the far-field wavelet according to a sea surface reflection coefficient;

[0048] An excitation wavelet acquisition module is configured to calculate a delay time of the seismic source ghost wave according to a depth of a seismic source sink, and fuse the far-field wavelet and the seismic source ghost wave to obtain an excitation wavelet;

[0049] A zero-time symmetry module is configured to time-shift the excitation wavelet to make a wave peak and a wave trough symmetrical about zero time;

[0050] A wavelet phase spectrum module is configured to perform a Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum;

[0051] A seismic wavelet acquisition module is configured to fuse the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum, and perform an inverse Fourier transform to obtain a seismic wavelet.

[0052] Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to enable the processor to execute the method for extracting a seismic wavelet based on excitation phase priori constraint.

[0053] Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to enable the processor to execute the method for extracting a seismic wavelet based on excitation phase priori constraint.

[0054] In combination with all the above technical solutions, the present application has the following advantages and positive effects:

[0055] Firstly, in view of the technical problems existing in the prior art and the difficulty in solving the problems, the present application is closely combined with the technical solutions to be protected, the results and data in the research and development process, and the like, and the technical problems solved by the technical solutions and some creative technical effects brought by the solutions are analyzed in detail and profoundly, and are described as follows:

[0056] The present application provides a method for extracting a seismic wavelet based on excitation phase priori constraint, which inputs the post-stack seismic data in the corresponding space and time window range of wavelet extraction; performs autocorrelation on the input seismic trace; performs Fourier transform on the autocorrelation result to obtain the amplitude spectrum of the wavelet; inputs the far-field wavelet excited by the air gun source; simulates the source ghost wave of the far-field wavelet; fuses the far-field wavelet and the source ghost wave to obtain an excitation wavelet; performs time shift on the excitation wavelet to make the wave peak and the wave trough symmetric about the zero time; performs Fourier transform on the time-shifted excitation wavelet to obtain the phase spectrum of the wavelet; fuses the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum, and performs inverse Fourier transform to obtain the seismic wavelet. Through the above scheme, the present application uses the excitation wavelet phase as 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.

[0057] Secondly, from the perspective of the product as a whole, the technical solutions to be protected by the present application have the following technical effects and advantages:

[0058] The present application is particularly suitable for the case where the statistical wavelet extraction can only be carried out due to the absence of drilled wells or incomplete well logging curves in the study area, and the deterministic wavelet extraction cannot be carried out.

[0059] Thirdly, the creativity of the claims of the present application is also reflected in the following important aspects:

[0060] (1) The technical scheme of the present application improves and perfects the technical defects of the conventional statistical wavelet extraction method, and no longer needs to make assumptions on the phase, thereby solving the technical problem that the statistical wavelet extraction method cannot realize accurate phase estimation.

[0061] (2) The technical scheme of the present application can be applied to multiple fields such as seismic forward modeling, fine splicing processing of seismic data, seismic inversion and interpretation, and supports reservoir and hydrocarbon detection research of oil and gas fields, and has great popularization and application and commercial value in oil and gas field exploration. BRIEF DESCRIPTION OF DRAWINGS

[0062] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure;

[0063] Figure 1 is a wavelet extraction method flowchart based on excitation phase priori constraint provided by the embodiment of the present application;

[0064] Figure 2 is a wavelet extraction method principle diagram based on excitation phase priori constraint provided by the embodiment of the present application;

[0065] Figure 3 is a wavelet extraction system schematic diagram based on excitation phase priori constraint provided by the embodiment of the present application;

[0066] Figure 4 is a diagram of a wavelet with a time length of 120 milliseconds and a time window range of 1300-1750 milliseconds of an input seismic trace provided by the embodiment of the present application;

[0067] Figure 5 is an amplitude spectrum diagram of a wavelet provided by the embodiment of the present application;

[0068] Figure 6 is a far-field wavelet diagram of a gas gun excitation in field acquisition provided by the embodiment of the present application;

[0069] Figure 7 is a source ghost wave diagram simulated by the embodiment of the present application with a sea surface reflection coefficient of 0.9;

[0070] Figure 8 is a diagram of a wavelet provided by the embodiment of the present application, which is obtained by fusing the input far-field wavelet with the simulated source ghost wave according to a calculated 8-millisecond delay fire Figure;

[0071] Figure 9 is a diagram of a wavelet provided by the embodiment of the present application, which is obtained by fusing the input far-field wavelet with the simulated source ghost wave according to a calculated 8-millisecond delay

[0072] Figure 10 The phase spectrum of the wavelet provided in this embodiment of the invention is shown in the figure;

[0073] Figure 11 The final seismic wavelet obtained according to the embodiments of the present invention is shown in the figure;

[0074] In the figure: 1. Post-stack seismic data extraction module; 2. Autocorrelation module; 3. Wavelet amplitude spectrum acquisition module; 4. Far-field wavelet input module; 5. Source ghost wave simulation module; 6. Excitation wavelet acquisition module; 7. Zero-time symmetry module; 8. Wavelet phase spectrum module; 9. Seismic wavelet acquisition module. Detailed Implementation

[0075] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0076] I. Explanation of the Implementation Example:

[0077] Example 1

[0078] like Figure 1 As shown in the figure, the wavelet extraction method based on excitation phase prior constraints provided in this embodiment of the invention includes the following steps:

[0079] S1, input wavelet to extract post-stack seismic data within the corresponding spatial and temporal window range;

[0080] S2, perform autocorrelation on the input seismic traces;

[0081] S3. Perform a Fourier transform on the autocorrelation results to obtain the wavelet amplitude spectrum;

[0082] S4, input the far-field wavelet generated by the air gun source during field data collection;

[0083] S5, simulates the source ghost wave of the far-field wavelet based on the sea surface reflection coefficient;

[0084] S6, when calculating the delay of the source ghost wave based on the source immersion depth, the far-field wavelet and the source ghost wavelet are fused to obtain the excitation wavelet;

[0085] S7, the excitation wavelet is time-shifted to make the peaks and troughs symmetrical about time zero;

[0086] S8, perform Fourier transform on the time-shifted excitation wavelet to obtain the wavelet phase spectrum;

[0087] 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.

[0088] In the embodiments of the present application, Figure 2 The wavelet extraction method based on the excitation phase prior constraint is provided.

[0089] Embodiment 2

[0090] Based on the wavelet extraction method based on the excitation phase prior constraint provided in Embodiment 1, further, in step S1, the input spatial range of the seismic trace is determined according to the spatial position of the wavelet extraction, and 5-7 seismic traces closest to the spatial position of the wavelet extraction and having a high signal-to-noise ratio are selected; the input time window range of the seismic trace is determined according to the time window range of the wavelet extraction and the time length of the wavelet, and the time length of the wavelet is 100-200 milliseconds, and the input time window range of the seismic trace is 3-5 times the time length of the wavelet.

[0091] Embodiment 3

[0092] 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 trace, and the calculation formula of the autocorrelation is:

[0093]

[0094] In the formula, N represents the sequence length, τ represents the sampling point, τ=0, 1, 2…N-1, t represents the time delay, x(τ) represents the original sequence, x(τ-t) represents the sequence at time τ-t, and y(t) represents the sequence after autocorrelation.

[0095] Embodiment 4

[0096] 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 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:

[0097] ES wav = FFT [Autocor (Seis)]

[0098] In the formula, ES wav represents the energy spectrum of the wavelet, FFT represents the fast Fourier transform, Autocor represents the autocorrelation, and Seis represents the seismic trace.

[0099] According to the relationship between the energy spectrum and the amplitude spectrum, the energy spectrum is squared to obtain the amplitude spectrum of the wavelet, and the expression is:

[0100]

[0101] In the formula, AS wav represents the amplitude spectrum of the wavelet.

[0102] Embodiment 5

[0103] Based on the wavelet extraction method based on the excitation phase prior constraint provided in Embodiment 1, further, in the step S5, the source ghost wave of the far-field wavelet is simulated according to the sea surface reflection coefficient, and since the source ghost wave is inverted in polarity when propagating to the sea surface, the source ghost wave can be expressed as:

[0104] W ghost = -1·RC sea ·W far

[0105] In the formula, W ghost represents the source ghost wave, RC sea represents the sea surface reflection coefficient, and W far represents the far-field wavelet.

[0106] Embodiment 6

[0107] Based on the wavelet extraction method based on the excitation phase prior constraint provided in Embodiment 1, further, in the step S6, the delay time of the source ghost wave is calculated according to the source setting depth, and the expression is:

[0108]

[0109] In the formula, t delay represents the delay time of the source ghost wave, S depth represents the source setting depth, and V sea represents the propagation speed of the seismic wave in seawater, and the delay time t delay is calculated according to the delay time t fire .

[0110] Embodiment 7

[0111] Based on the wavelet extraction method based on the excitation phase prior constraint provided in Embodiment 1, further, in the step S8, the Fourier transform is performed on the time-shifted excitation wavelet to obtain the phase spectrum of the wavelet, and the expression is:

[0112]

[0113] In the formula, PS wav represents the phase spectrum of the wavelet, FFT represents the fast Fourier transform, and W represents the time-shifted excitation wavelet.

[0114] Example 8

[0115] Based on the wavelet extraction method based on excitation phase prior constraints provided in Example 1, further, in step S9, the wavelet amplitude spectrum AS obtained in step S3 is fused. wav The wavelet phase spectrum PS obtained in step 8 wav The spectrum FS of the wavelet is obtained. wav The seismic wavelet is obtained by performing an inverse Fourier transform on the spectrum of the wavelet, and its expression is:

[0116] W seis =IFFT[FS wav ]

[0117] In the formula, W seis Represents the seismic wavelet, IFFT represents the inverse fast Fourier transform, and FS represents the seismic wavelet. wav This represents the spectrum of the wavelet.

[0118] Example 9

[0119] like Figure 3 As shown, the wavelet extraction system based on excitation phase prior constraints provided by this embodiment of the invention includes:

[0120] Post-stack seismic data extraction module 1 is used to extract post-stack seismic data within the corresponding spatial and temporal window range by inputting wavelets;

[0121] Autocorrelation module 2 is used to perform autocorrelation on the input seismic traces;

[0122] Wavelet amplitude spectrum acquisition module 3 is used to perform Fourier transform on the autocorrelation results to obtain the wavelet amplitude spectrum;

[0123] The far-field wavelet input module 4 is used to input the far-field wavelet excited by the air gun source during field data collection.

[0124] Source ghost wave simulation module 5 is used to simulate the source ghost wave of far-field wavelets based on the sea surface reflection coefficient;

[0125] The excitation wavelet acquisition module 6 is used to obtain the excitation wavelet by fusing the far-field wavelet and the source ghost wave when calculating the delay of the source ghost wave based on the source immersion depth.

[0126] Zero-time symmetry module 7 is used to time-shift the excitation wavelet to make the peaks and troughs symmetric about zero time.

[0127] Wavelet phase spectrum module 8 is used to perform Fourier transform on the time-shifted excitation wavelet to obtain the wavelet phase spectrum;

[0128] The seismic wavelet acquisition module 9 is used to fuse the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum, and perform inverse Fourier transform to obtain the seismic wavelet.

[0129] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0130] The information interaction, execution process and the like between the above devices / units can be based on the same concept as the method embodiments of the present application, and the specific functions and the brought technical effects can be referred to the method embodiments part, which will not be repeated here.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is taken as an example, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the system can be referred to the corresponding process in the foregoing method embodiments.

[0132] II. Application Embodiments

[0133] Application Example 1

[0134] As shown in the figure, the present application is specifically performed according to the following steps: Figure 2

[0135] First step: input the post-stack seismic data in the corresponding space and time window range of wavelet extraction;

[0136] According to the spatial position of wavelet extraction, the input spatial range of seismic trace is determined, and the seismic trace with the highest signal-to-noise ratio closest to the wavelet extraction spatial position is selected. In this embodiment, the number of seismic traces is 7; according to the time window range of wavelet extraction and the time length of wavelet, the input time window range of seismic trace is determined. In this embodiment, the time length of wavelet is 120 milliseconds, and the time window range of input seismic trace is 1300-1750 milliseconds, as shown in the figure. Figure 4

[0137] Second step: autocorrelation of the input seismic trace;

[0138] The calculation formula of autocorrelation is:

[0139]

[0140] ​​In the formula, N represents sequence length, τ represents sampling point, τ = 0, 1, 2, …, N-1, t represents time delay, x(τ) represents original sequence, x(τ-t) represents sequence at τ-t, and y(t) represents sequence after autocorrelation.

[0141] Step S3: Fourier transform is performed on the autocorrelation result to obtain an amplitude spectrum of the wavelet.

[0142] According to the assumption that autocorrelation of a seismic trace is equal to autocorrelation of a wavelet, energy spectrum of the seismic trace is equal to energy spectrum of the wavelet, and the expression is as follows:

[0143] ES wav = FFT [Autocor (Seis)]

[0144] In the formula, ES wav represents energy spectrum of the wavelet, FFT represents fast Fourier transform, Autocor represents autocorrelation, and Seis represents seismic trace.

[0145] According to the relationship between energy spectrum and amplitude spectrum, the energy spectrum is squared to obtain the amplitude spectrum of the wavelet, and the expression is as follows:

[0146]

[0147] In the formula, AS wav represents amplitude spectrum of the wavelet.

[0148] Step S4: A far-field wavelet excited by a gas gun source in field acquisition is inputted.

[0149] The far-field wavelet excited by the gas gun source in field acquisition in the embodiment is shown in FIG. 2. Figure 6

[0150] Step S5: A source ghost wave of the far-field wavelet is simulated according to a sea surface reflection coefficient.

[0151] Since the source ghost wave undergoes polarity inversion when propagating to the sea surface, the source ghost wave can be expressed as:

[0152] W ghost = -1·RC sea ·W far

[0153] In the formula, W ghost represents the source ghost wave, RC sea represents the sea surface reflection coefficient, and W far represents the far-field wavelet. The sea surface reflection coefficient in the embodiment is 0.9, and the simulated source ghost wave is shown in FIG. 3. Figure 7

[0154] ​​Step 6: Calculate the delay time of the ghost wave according to the depth of the source, fuse the far-field wavelet with the ghost wave to obtain the excited wavelet; the delay time of the ghost wave can be expressed as:

[0155]

[0156] where t delay represents the delay time of the ghost wave, S depth represents the depth of the source, V sea represents the propagation speed of the seismic wave in seawater. In this embodiment, the depth of the source is 6 meters, the propagation speed of the seismic wave in seawater is 1500 m / s, and the delay time t delay of the ghost wave is 8 ms obtained by calculation. Fuse the far-field wavelet input in step S4 with the ghost wave simulated in step S5 according to the calculated delay time of 8 ms to obtain the excited wavelet W fire , as shown in Figure 8 .

[0157] Step 7: Time shift the excited wavelet to make the wave peak and wave trough symmetrical about the zero time;

[0158] In this embodiment, the excited wavelet is time shifted to the negative time by 7 ms, and the wave peak and wave trough are symmetrical about the zero time. The time-shifted excited wavelet is shown in Figure 9 .

[0159] Step 8: Fourier transform the time-shifted excited wavelet to obtain the phase spectrum of the wavelet;

[0160]

[0161] where PS wav represents the phase spectrum of the wavelet, FFT represents the fast Fourier transform, represents the time-shifted excited wavelet, and the phase spectrum of the wavelet in this embodiment is shown in Figure 10 .

[0162] Step 9: Fuse the wavelet amplitude spectrum obtained in step S3 with the wavelet phase spectrum obtained in step 8, and perform inverse Fourier transform to obtain the seismic wavelet.

[0163] Fuse the wavelet amplitude spectrum AS wav shown in Figure 5 and the wavelet phase spectrum PS wav shown in Figure 10 to obtain the frequency spectrum FS wav of the seismic wavelet, and perform inverse Fourier transform on the frequency spectrum of the seismic wavelet to obtain the seismic wavelet, expressed as:

[0164] W seis = IFFT[FS wav ]

[0165] wherein W seis represents a seismic wavelet, IFFT represents a fast inverse Fourier transform, FS wav represents a spectrum of the wavelet, and the final seismic wavelet obtained in the embodiment is shown in FIG. 4. Figure 11

[0166] Application Example 2

[0167] The computer device provided by the embodiment of the present application comprises at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the method embodiments described above when executing the computer program.

[0168] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and the computer program can implement the steps in any of the method embodiments described above when executed by a processor.

[0169] The information data processing terminal provided by the embodiment of the present application is used to provide a user input interface to implement the steps in any of the method embodiments described above when executed on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer, or a switch.

[0170] The server provided by the embodiment of the present application is used to provide a user input interface to implement the steps in any of the method embodiments described above when executed on an electronic device.

[0171] The computer program product provided by the embodiment of the present application, when executed on an electronic device, enables the electronic device to implement the steps in any of the method embodiments described above.

[0172] ​The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc.

[0173] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0174] III. Evidence of the effects of the embodiments

[0175] Bozhong 19-2 oilfield in Bohai Sea spans multiple seismic acquisition work areas. In order to meet the overall exploration and research needs of the oilfield, seismic data in different work areas need to be spliced. Due to different seismic acquisition parameters, there are large differences in amplitude, frequency and phase of seismic data in different work areas. The wavelet extraction method based on excitation phase prior constraint is used to accurately extract the wavelets of seismic data in different acquisition work areas. Further, through fine matching of seismic wavelets, fine matching of amplitude, frequency and phase of seismic data in different work areas is realized, the amplitude preservation of spliced seismic data is improved, and a good data foundation is provided for reservoir and hydrocarbon detection research, which effectively supports the exploration and evaluation of Bozhong 19-2 oilfield.

[0176] The above describes only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any modification, equivalent replacement and improvement made by those skilled in the art within the technical range disclosed by the present application, as long as it is within the spirit and principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A wavelet extraction method based on excitation phase priori constraint, characterized in that, The method The method comprises the following steps: S1, inputting post-stack seismic data in a space range and a time window range corresponding to wavelet extraction; S2, autocorrelating the input seismic trace; S3, performing Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum; S4, inputting a far-field wavelet excited by a gun seismic source in field acquisition; S5, simulating a source ghost wave of the far-field wavelet according to a sea surface reflection coefficient; S6, calculating a delay time of the source ghost wave according to a source sinking depth, and fusing the far-field wavelet and the source ghost wave to obtain an excited wavelet; S7, time-shifting the excited wavelet to make a wave peak and a wave trough symmetrical about zero time; S8, performing Fourier transform on the time-shifted excited wavelet to obtain a wavelet phase spectrum; 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; In step S8, the expression of the wavelet phase spectrum is: where PS wav denotes the phase spectrum of the wavelet, FFT denotes the fast Fourier transform, denotes the time-shifted excitation wavelet; 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; specifically comprising: inverse Fourier transform is performed on the spectrum of the wavelet to obtain the seismic wavelet, and the expression is: W seis = IFFT[FS wav ] where W seis represents the seismic wavelet, IFFT represents the inverse fast Fourier transform, FS wav represents the spectrum of the wavelet.

2. The method of claim 1, wherein, In step S1, the input space range of the seismic trace is determined according to the spatial position of the wavelet extraction, and 5-7 seismic traces away from the spatial position of the wavelet extraction are selected; the input time window range of the seismic trace is determined according to the time window range of the wavelet extraction and the time length of the wavelet.

3. The method of claim 1, wherein, In step S2, the calculation formula of autocorrelation is: In the formula, N represents the sequence length, τ represents the sampling point, τ=0, 1, 2...N-1, t represents the time delay, x(τ) represents the original sequence, x(τ-t) represents the sequence at τ-t time, and y(t) represents the sequence after autocorrelation.

4. The method of claim 1, wherein, In step S3, the Fourier transform is performed on the autocorrelation result to obtain the wavelet amplitude spectrum, which comprises: performing Fourier transform on the autocorrelation result, and 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: ES wav = FFT [Autocor (Seis)] In the formula, ES wav The energy spectrum of the wavelet is represented by FFT, the fast Fourier transform by FFT, the autocorrelation by Auto ... Seis, and the seismic trace by Seis. According to the relationship between the energy spectrum and the amplitude spectrum, the square root of the energy spectrum is obtained to obtain the amplitude spectrum of the wavelet, and the expression is: In the formula, AS wav represents the amplitude spectrum of the wavelet.

5. The method of claim 1, wherein, In step S5, in the simulation of the source ghost wave of the far-field wavelet according to the sea surface reflection coefficient, since the source ghost wave is inverted in polarity when it propagates to the sea surface, the source ghost wave can be represented as: W ghost = -1 · RC sea ·W far where W ghost represents the ghost wave of the source, RC sea represents the reflection coefficient of the sea surface, W far represents the far-field wavelet.

6. The method of claim 1, wherein, In step S6, the delay time of the source ghost wave is calculated according to the source sinking depth, and the expression is: where t delay S represents the delay time of the ghost wave of the seismic source depth V represents the depth of the seismic source sea V represents the propagation speed of the seismic wave in the sea water According to the calculated delay time t delay The far-field wavelet inputted in step S4 is fused with the source ghost wave simulated in step S5 to obtain the excitation wavelet W fire .

7. An extraction system for implementing the method of wavelet extraction based on excitation phase priori constraint according to any one of claims 1-6, characterized in that, The extraction system comprises: A post-stack seismic data extraction module (1) for inputting post-stack seismic data in a space range and a time window range corresponding to wavelet extraction; An autocorrelation module (2) for autocorrelating the input seismic trace; A wavelet amplitude spectrum acquisition module (3) for performing Fourier transform on the autocorrelation result to obtain a wavelet amplitude spectrum; A far-field wavelet input module (4) for inputting a far-field wavelet excited by a gun seismic source in field acquisition; A source ghost wave simulation module (5) for simulating a source ghost wave of the far-field wavelet according to a sea surface reflection coefficient; An excited wavelet acquisition module (6) for calculating a delay time of the source ghost wave according to a source sinking depth, and fusing the far-field wavelet and the source ghost wave to obtain an excited wavelet; a zero-time symmetry module (7) for time-shifting the excitation wavelet to make the wave crest and wave trough symmetric about zero time; a wavelet phase spectrum module (8) for performing Fourier transform on the time-shifted excitation wavelet to obtain a wavelet phase spectrum; a seismic wavelet obtaining module (9) for fusing the obtained wavelet amplitude spectrum and the obtained wavelet phase spectrum and performing inverse Fourier transform to obtain a seismic wavelet.

8. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the wavelet extraction method based on the excitation phase prior constraint in any one of claims 1-6.

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