A method and apparatus for estimating a seismic source wavelet

Abstract

The application provides an estimation method and device of a seismic wavelet, which comprises the following steps: constructing a Ricker wavelet with the same main frequency as the obtained observation data; obtaining seismic wave forward simulation shot gather prediction data through forward simulation of the Ricker wavelet; filtering the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data; constructing a smooth spectrum linear phase short wavelet based on the spectrum characteristics of the filtered wavelet; intercepting the waveform of the smooth spectrum linear phase short wavelet in the main energy interval in the filtered wavelet and performing smoothing processing; calculating the phase spectrum corresponding to the waveform after the smoothing processing, and constructing a target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet. Thus, the resolution and accuracy of the subsequent processing result are improved, and the problem that conventional land data is difficult to stably estimate a high-precision and high-resolution wavelet is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration and development, and particularly relates to a method and device for estimating a seismic source wavelet. BACKGROUND

[0002] The existing methods for estimating a seismic source wavelet include a direct wave back-propagation or filtering method, and a method of using shallow layer reflection waves to deconvolve reflection coefficients. However, for complex land data, it is difficult to separate the direct wave or shallow layer reflection wave energy from the observation data, and it is also difficult to accurately estimate the shallow layer reflection coefficients, so that the wavelet estimated by these conventional methods is inaccurate, the side lobe is long and the energy is strong, and the accuracy and resolution of the subsequent processing results are reduced.

[0003] Specifically, the conventional direct wave back-propagation method needs to accurately estimate the near-surface velocity and separate the direct wave energy from the seismic record; the conventional filtering method needs to design a linear filter according to the assumed wavelet prediction data and the actual observation data, and then filter the direct wave of the observation data to estimate the wavelet, which also needs to separate the direct wave energy from the seismic record. Due to the influence of the surface undulation and the high complexity of the near-surface velocity variation of the land data, it is difficult to accurately model the near-surface or separate the energy of the land data; the conventional shallow layer reflection wave reflection coefficient deconvolution method needs to separate the near-surface reflection wave energy from the seismic record and accurately model the near-surface reflection coefficient sequence, which is difficult to apply to the wavelet estimation of complex land data. SUMMARY

[0004] The present application provides a method and device for estimating a seismic source wavelet, which is used to solve the technical problem that conventional land data is difficult to stably estimate a high-precision and high-resolution wavelet.

[0005] In a first aspect, the present application provides a method for estimating a seismic source wavelet, comprising:

[0006] constructing a Ricker wavelet with a main frequency same as that of the obtained observation data;

[0007] obtaining seismic wave forward modeling shot gather prediction data through forward modeling of the Ricker wavelet;

[0008] filtering the Ricker wavelet based on the observation data and the seismic wave forward modeling shot gather prediction data;

[0009] constructing a smooth spectrum linear phase short wavelet based on the spectral characteristics of the filtered wavelet;

[0010] extracting the waveform of the smooth spectrum linear phase short wavelet in the main energy interval from the filtered wavelet and performing smoothing processing;

[0011] After the smoothing processing, a phase spectrum corresponding to the waveform is calculated, and a target wavelet is constructed based on the phase spectrum and the smoothed frequency spectrum linear phase short wavelet.

[0012] Optionally, the Ricker wavelet is filtered based on the observation data and the seismic wave forward modeling shot gather prediction data, including:

[0013] Linear filter coefficients are calculated based on the seismic wave forward modeling shot gather prediction data and the observation data.

[0014] The Ricker wavelet is filtered by using the filter coefficients.

[0015] Optionally, a smoothed frequency spectrum linear phase short wavelet is constructed based on a frequency spectrum feature of the filtered wavelet, including:

[0016] An amplitude spectrum is extracted from the filtered wavelet, and the amplitude spectrum is fitted by a bell-shaped curve to obtain a corresponding target bell-shaped curve.

[0017] A linear phase spectrum of the seismic wave forward modeling shot gather prediction data is inverted by using the target bell-shaped curve to construct the smoothed frequency spectrum linear phase short wavelet.

[0018] Optionally, a phase spectrum corresponding to the waveform is calculated after the smoothing processing, and a target wavelet is constructed based on the phase spectrum and the smoothed frequency spectrum linear phase short wavelet, including:

[0019] The target wavelet is constructed based on the smoothed frequency spectrum linear phase short wavelet and the phase spectrum and a target wavelet determination expression preset.

[0020] The target wavelet determination expression is specifically:

[0021]

[0022] W3(t) is a target wavelet time domain function, t is time, A(f) is an amplitude spectrum, is a phase spectrum, i is an imaginary unit, and f is a frequency variable.

[0023] In a second aspect, the present application provides an estimation device of a seismic source wavelet, including:

[0024] A Ricker wavelet construction module is configured to construct a Ricker wavelet with a main frequency same as a main frequency of acquired observation data.

[0025] A forward modeling module is configured to perform forward modeling by using the Ricker wavelet to obtain seismic wave forward modeling shot gather prediction data.

[0026] A filtering module is configured to filter the Ricker wavelet based on the observation data and the seismic wave forward modeling shot gather prediction data.

[0027] a linear phase short wavelet constructing module, configured to construct a smooth spectrum linear phase short wavelet based on a spectrum feature of the filtered wavelet;

[0028] a truncating module, configured to truncate a waveform of the smooth spectrum linear phase short wavelet in a main energy interval in the filtered wavelet and perform a smoothing process on the waveform;

[0029] a target wavelet constructing module, configured to calculate a phase spectrum corresponding to the waveform after the smoothing process, and construct a target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet.

[0030] Optionally, the filtering module comprises:

[0031] a filter coefficient calculating submodule, configured to calculate linear filter coefficients based on the seismic wave forward modeling shot gather prediction data and the observation data;

[0032] a filtering submodule, configured to filter the slant wavelet by using the filter coefficients.

[0033] Optionally, the linear phase short wavelet constructing module comprises:

[0034] a fitting submodule, configured to extract an amplitude spectrum from the filtered wavelet, and fit the amplitude spectrum by using a bell-shaped curve to obtain a corresponding target bell-shaped curve;

[0035] a first constructing submodule, configured to construct the smooth spectrum linear phase short wavelet by inversely calculating a linear phase spectrum of the seismic wave forward modeling shot gather prediction data through the target bell-shaped curve.

[0036] Optionally, the target wavelet constructing module comprises:

[0037] a second constructing submodule, configured to construct the target wavelet based on the amplitude spectrum and the phase spectrum and in combination with a preset target wavelet determination expression;

[0038] the target wavelet determination expression is specifically:

[0039]

[0040] W3(t) is a target wavelet time domain function, t is time, A(f) is an amplitude spectrum, is a phase spectrum, i is an imaginary unit, and f is a frequency variable.

[0041] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer readable instructions which, when executed by the processor, perform the steps of the method according to the first aspect.

[0042] In a fourth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.

[0043] From the above technical solutions, the present application has the following advantages:

[0044] The present application provides an estimation method and device for a seismic source wavelet, which comprises the following steps: constructing a Ricker wavelet with a main frequency same as that of obtained observation data; obtaining seismic wave forward modeling shot gather prediction data through forward modeling of the Ricker wavelet; filtering the Ricker wavelet based on the observation data and the seismic wave forward modeling shot gather prediction data; constructing a smooth spectrum linear phase short wavelet based on the spectral characteristics of the filtered wavelet; intercepting the waveform of the smooth spectrum linear phase short wavelet in the main energy interval in the filtered wavelet and performing smoothing processing; calculating the phase spectrum corresponding to the smoothed waveform, and constructing a target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet. By intercepting the waveform in the main energy interval in the filtered wavelet, smoothing the waveform and calculating the phase spectrum, and finally constructing the target wavelet by using the smooth spectrum linear phase short wavelet and the phase spectrum, the estimated target wavelet not only retains the spectral characteristics of the wavelet estimated by the conventional method, but also compresses the sidelobe energy, improves the resolution and accuracy of the subsequent processing result, and solves the problem that the conventional land data is difficult to robustly estimate a high-precision and high-resolution wavelet. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0046] Figure 1 A flow chart of the first embodiment of the estimation method for a seismic source wavelet of the present application;

[0047] Figure 2 A flow chart of the second embodiment of the estimation method for a seismic source wavelet of the present application;

[0048] Figure 3 A flow chart of the third embodiment of the estimation method for a seismic source wavelet of the present application;

[0049] Figure 4 Wavelet diagram estimated by a conventional method of the application;

[0050] Figure 5 Wavelet diagram estimated by an estimation method of a seismic wavelet using the application;

[0051] Figure 6 Wavelet waveform comparison diagram between a wavelet waveform of a conventional method of a shot and a wavelet waveform estimated by an estimation method of a seismic wavelet using the application;

[0052] Figure 7 Wavelet spectrum comparison diagram between a wavelet spectrum of a conventional method of a shot and a wavelet spectrum estimated by an estimation method of a seismic wavelet using the application;

[0053] Figure 8 Structure block diagram of an embodiment of an estimation device of a seismic wavelet of the application. DETAILED DESCRIPTION

[0054] The embodiment of the application provides an estimation method and device of a seismic wavelet, and is used for solving the technical problem that conventional land data is difficult to estimate a high-precision and high-resolution wavelet stably.

[0055] In order to make the application purpose, features and advantages of the application more obvious and easy to understand, the technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the following described embodiments are only some of the embodiments of the application, but not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0056] Embodiment one, please refer to Figure 1 , Figure 1 Flow step diagram of an embodiment one of the estimation method of a seismic wavelet of the application, comprising:

[0057] Step S101, constructing a Ricker wavelet with the same main frequency as the main frequency of the obtained observation data;

[0058] It should be noted that the Ricker wavelet is a standardized waveform of a seismic wave, and usually has a wide frequency band.

[0059] Step S102, obtaining seismic wave forward modeling shot gather prediction data through forward modeling of the Ricker wavelet;

[0060] It should be noted that forward modeling refers to obtaining the distribution of a seismic wave field in a subsurface medium through simulation calculation according to the propagation law of a seismic wave.

[0061] Step S103, filtering the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data;

[0062] It should be noted that the filtered wavelet can remove certain frequency components or reduce noise interference.

[0063] Step S104, constructing a smooth spectrum linear phase short wavelet based on the spectrum characteristics of the filtered wavelet;

[0064] It should be noted that the smooth spectrum linear phase short wavelet is a narrow bandwidth seismic waveform with high time resolution.

[0065] Step S105, in the filtered wavelet, the waveform of the smooth spectrum linear phase short wavelet in the main energy interval is intercepted and smoothed;

[0066] It should be noted that the smoothing process can be performed by averaging, moving average or applying a window function.

[0067] Step S106, calculating the phase spectrum corresponding to the smoothed waveform, and constructing a target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet.

[0068] The embodiment of the application discloses a method for estimating a seismic source wavelet, comprising: constructing a Ricker wavelet with the same main frequency as the main frequency of the obtained observation data; obtaining seismic wave forward simulation shot gather prediction data through the Ricker wavelet forward; filtering the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data; constructing a smooth spectrum linear phase short wavelet based on the spectrum characteristics of the filtered wavelet; in the filtered wavelet, the waveform of the smooth spectrum linear phase short wavelet in the main energy interval is intercepted and smoothed; calculating the phase spectrum corresponding to the smoothed waveform, and constructing a target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet. By intercepting the waveform in the main energy interval of the filtered wavelet, smoothing the waveform and calculating the phase spectrum, the target wavelet is finally constructed by using the smooth spectrum linear phase short wavelet and the phase spectrum. The estimated target wavelet not only retains the spectrum characteristics of the wavelet estimated by the conventional method, but also compresses the sidelobe energy, improves the resolution and accuracy of the subsequent processing result, and solves the problem that the conventional land data is difficult to estimate a high-precision and high-resolution wavelet stably.

[0069] Embodiment two, please refer to Figure 2 , Figure 2 is a flow chart of an embodiment two of a method for estimating a seismic source wavelet of the application, the method comprising:

[0070] Step S201, constructing a Ricker wavelet with the same main frequency as the main frequency of the obtained observation data;

[0071] Step S202, obtaining the seismic wave forward simulation shot gather prediction data by the Radon wavelet forward.

[0072] In the embodiment of the application, the Radon wavelet is used for forward calculation to obtain the seismic wave forward simulation shot gather prediction data.

[0073] Step S203, calculating the linear filter coefficients based on the seismic wave forward simulation shot gather prediction data and the observation data.

[0074] In the embodiment of the application, the linear filter is calculated by using the signal processing technology based on the observation data and the seismic wave forward simulation shot gather prediction data.

[0075] In the specific implementation, the linear filter can be used for weighted average on the input signal to extract the specific frequency component or remove the noise.

[0076] Step S204, filtering the Radon wavelet by using the filter coefficients.

[0077] Step S205, constructing the smooth spectrum linear phase short wavelet based on the spectrum characteristics of the filtered wavelet.

[0078] Step S206, intercepting the waveform of the smooth spectrum linear phase short wavelet in the main energy interval in the filtered wavelet and performing the smoothing processing.

[0079] Step S207, calculating the phase spectrum corresponding to the waveform after the smoothing processing, and constructing the target wavelet based on the phase spectrum and the smooth spectrum linear phase short wavelet.

[0080] The embodiment of the application discloses an estimation method of the source wavelet, which calculates the linear filter coefficients based on the seismic wave forward simulation shot gather prediction data and the observation data, and filters by using the linear filter coefficients to achieve the purposes of denoising, enhancing the specific frequency component or changing the spectrum characteristics.

[0081] Embodiment three, please refer to Figure 3 , Figure 3 is a flow chart of the embodiment three of the estimation method of the source wavelet of the application, and the method comprises the following steps:

[0082] Step S301, constructing the Radon wavelet with the main frequency same as the main frequency of the obtained observation data.

[0083] In the embodiment of the application, the expression of the Radon wavelet is as follows:

[0084]

[0085] wherein w0 is a Ricker wavelet, t is time, f is frequency p0 is a Ricker wavelet main frequency.

[0086] Step S302, obtaining the seismic wave forward simulation shot gather prediction data through the Ricker wavelet forward.

[0087] In the embodiment of the present application, the forward expression is as follows:

[0088] p0=G(m,w0);

[0089] wherein m is a stratum medium model parameter, which can be stratum medium velocity or other elastic parameters, p0 is the seismic wave forward simulation shot gather prediction data, and G(·) is a forward operator.

[0090] Step S303, calculating the linear filter coefficient based on the seismic wave forward simulation shot gather prediction data and the observation data.

[0091] In the embodiment of the present application, the expression of the linear filter coefficient is as follows:

[0092] c=(P0 T P0) -1 P0 T d;

[0093] wherein c is the linear filter coefficient, P0 is the Toubuli matrix of the seismic wave forward simulation shot gather prediction data, and the expression indicates that the seismic wave forward simulation shot gather prediction data is deconvoluted using the observation data.

[0094] Step S304, filtering the Ricker wavelet using the filter coefficient.

[0095] In the embodiment of the present application, the filtering expression is specifically as follows:

[0096] w1=w0*c;

[0097] wherein w1 is the filtered wavelet, and * is a convolution operator.

[0098] Step S305, extracting the amplitude spectrum from the filtered wavelet, and fitting the amplitude spectrum through a bell-shaped curve to obtain a corresponding target bell-shaped curve.

[0099] Step S306, inverting the linear phase spectrum of the seismic wave forward simulation shot gather prediction data through the target bell-shaped curve to construct the smooth spectrum linear phase short wavelet.

[0100] In the embodiment of the present application, the linear phase short wavelet is determined through the following expression:

[0101] w2(t)=∫A(f)e i2 πfΔt0 df;

[0102]

[0103] where f p1 is the main frequency of the filtered wavelet, σ l and σ r are obtained by regression matching estimation, and Δt0is a constant used for matching the main energy of the filtered wavelet and the short wavelet, which can be determined by cross-correlation estimation.

[0104] In step S307, the waveform of the linear-phase short wavelet in the main energy range is intercepted from the filtered wavelet, and the intercepted waveform is smoothed.

[0105] In the embodiment of the present application, the waveform of the linear-phase short wavelet in the determined main energy range is intercepted from the filtered wavelet, and the intercepted waveform is smoothed to reduce possible noise or sharp change and ensure smooth and continuous waveform. Then, the phase spectrum of the waveform is calculated by performing spectrum analysis on the smoothed waveform. Finally, the high-precision wavelet is constructed by combining the amplitude spectrum of the filtered wavelet and the calculated phase spectrum.

[0106] In step S308, the phase spectrum corresponding to the smoothed waveform is calculated, and the target wavelet is constructed based on the phase spectrum and the smoothed spectrum linear-phase short wavelet.

[0107] In an optional embodiment, the phase spectrum corresponding to the smoothed waveform is calculated, and the target wavelet is constructed based on the phase spectrum and the smoothed spectrum linear-phase short wavelet, including:

[0108] The target wavelet is constructed based on the smoothed spectrum linear-phase short wavelet and the phase spectrum, and a pre-set target wavelet determination expression.

[0109] The target wavelet determination expression is specifically:

[0110]

[0111] W3(t) is the time-domain function of the target wavelet, t is time, A(f) is the amplitude spectrum, is the phase spectrum, i is an imaginary unit, and f is a frequency variable.

[0112] The embodiment of the present application discloses a method for estimating a seismic wavelet, which comprises the following steps: intercepting a smooth spectrum linear phase short wavelet waveform in a main energy interval in filtered prediction data, smoothing the waveform and calculating a phase spectrum, and finally constructing a high-precision wavelet by using the amplitude spectrum and the phase spectrum.

[0113] Embodiment four, please refer to Figure 4 and Figure 5 , Figure 4 a schematic diagram of a wavelet estimated by a conventional method of the present application, Figure 5 a schematic diagram of a wavelet estimated by a method for estimating a seismic wavelet of the present application, the embodiment of the present application is based on land shot gather data, and a wavelet is estimated by using a conventional method and a method for estimating a seismic wavelet of the present application respectively without separating direct wave energy. Wherein, the actual data is 1000 shots, the sampling interval is 1.5 ms, a wavelet is estimated for each shot, and the schematic diagram of the wavelet is shown in Figure 4 and Figure 5 three samples are randomly extracted from Figure 4 and Figure 5 for wavelet waveform and wavelet spectrum comparison, and the specific wavelet waveform and wavelet spectrum comparison are shown in Figure 6 and Figure 7 It can be seen that the wavelet waveform and the wavelet spectrum obtained by using the method for estimating a seismic wavelet of the present application are more accurate than the wavelet waveform and the wavelet spectrum obtained by using the conventional method.

[0114] Embodiment five, please refer to Figure 8 , Figure 8 a structural block diagram of an embodiment of a device for estimating a seismic wavelet of the present application, the device comprises:

[0115] a Ricker wavelet construction module 501, configured to construct a Ricker wavelet with a main frequency same as a main frequency of acquired observation data;

[0116] a forward modeling module 502, configured to obtain seismic wave forward modeling shot gather prediction data by forward modeling of the Ricker wavelet;

[0117] a filtering module 503, configured to filter the Ricker wavelet based on the observation data and the seismic wave forward modeling shot gather prediction data;

[0118] a linear phase short wavelet construction module 504, configured to construct a smooth spectrum linear phase short wavelet based on spectral characteristics of the filtered wavelet;

[0119] an intercepting module 505, configured to intercept a waveform of the smooth spectrum linear phase short wavelet in a main energy interval in the filtered wavelet and perform smoothing processing;

[0120] The target wavelet construction module 506 is used to calculate the phase spectrum corresponding to the waveform after smoothing, and construct the target wavelet based on the phase spectrum and the smoothed spectrum linear phase short wavelet.

[0121] In an optional embodiment, the filtering module 503 includes:

[0122] A filter coefficient calculation submodule, configured to calculate linear filter coefficients based on the seismic wave forward simulation shot gather prediction data and the observation data;

[0123] The filtering submodule is configured to filter the Ricker wavelet using the filter coefficients.

[0124] In an optional embodiment, the linear phase short wavelet construction module 504 includes:

[0125] a fitting submodule, configured to extract an amplitude spectrum from the filtered wavelet, and fit the amplitude spectrum using a bell curve to obtain a corresponding target bell curve;

[0126] The first construction submodule is used to invert the linear phase spectrum of the seismic wave forward simulation shot gather prediction data through the target bell-shaped curve to construct the smoothed spectrum linear phase short wavelet.

[0127] In an optional embodiment, the target wavelet construction module 506 includes:

[0128] A second construction submodule is configured to construct the target wavelet based on the amplitude spectrum and the phase spectrum in combination with a preset target wavelet determination expression;

[0129] The target wavelet determination expression is specifically:

[0130]

[0131] W3(t) is the time domain function of the target wavelet, t is time, A(f) is the amplitude spectrum, is the phase spectrum, i is the imaginary unit, and f is the frequency variable.

[0132] An embodiment of the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a source wavelet estimation method as described in any of the above embodiments.

[0133] An embodiment of the present invention further provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by the processor, the steps of the method for estimating source wavelets as described in any of the above embodiments are implemented.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0135] In several embodiments provided in the present application, it should be understood that the disclosed method, device, electronic equipment and storage medium can be implemented by other manners. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0136] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0137] In addition, the functional units in each embodiment of the present application 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 in the form of software functional unit.

[0138] When the integrated unit is realized in the form of software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that makes a contribution to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a readable storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing readable storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0139] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for estimating source wavelet, characterized in that: include: Construct a Ricker wavelet with the same main frequency as the main frequency of the acquired observation data; Obtaining seismic wave forward simulation shot gather prediction data through the Ricker wavelet forward modeling; filtering the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data; Based on the spectrum characteristics of the filtered wavelet, a smooth spectrum linear phase short wavelet is constructed; In the filtered wavelet, intercepting the waveform of the smoothed spectrum linear phase short wavelet in the main energy interval and performing smoothing processing; The phase spectrum corresponding to the waveform after smoothing is calculated, and a target wavelet is constructed based on the phase spectrum and the smoothed spectrum linear phase short wavelet.

2. The method for estimating source wavelet according to claim 1, characterized in that: Filtering the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data includes: Calculating linear filter coefficients based on the seismic wave forward simulation shot gather prediction data and the observation data; The Ricker wavelet is filtered using the filter coefficients.

3. The method for estimating source wavelet according to claim 1, characterized in that: Based on the spectral characteristics of the filtered wavelet, a smooth spectrum linear phase short wavelet is constructed, including: extracting an amplitude spectrum from the filtered wavelet, and fitting the amplitude spectrum through a bell curve to obtain a corresponding target bell curve; The linear phase spectrum of the seismic wave forward simulation shot gather prediction data is inverted through the target bell-shaped curve to construct the smoothed spectrum linear phase short wavelet.

4. The method for estimating source wavelet according to claim 3, characterized in that: Calculating the phase spectrum corresponding to the waveform after smoothing, and constructing the target wavelet based on the phase spectrum and the smoothed spectrum linear phase short wavelet, including: Based on the smoothed spectrum linear phase short wavelet and the phase spectrum, combined with a preset target wavelet determination expression, the target wavelet is constructed; The target wavelet determination expression is specifically: W3(t) is the time domain function of the target wavelet, t is time, A(f) is the amplitude spectrum, is the phase spectrum, i is the imaginary unit, and f is the frequency variable.

5. A device for estimating source wavelets, characterized in that: include: A Ricker wavelet construction module is used to construct a Ricker wavelet with the same main frequency as the main frequency of the acquired observation data; A forward modeling module, configured to obtain seismic wave forward simulation shot gather prediction data through the Ricker wavelet forward modeling; A filtering module, configured to filter the Ricker wavelet based on the observation data and the seismic wave forward simulation shot gather prediction data; A linear phase short wavelet construction module is used to construct a smooth spectrum linear phase short wavelet based on the spectrum characteristics of the filtered wavelet; An interception module is used to intercept the waveform of the smoothed spectrum linear phase short wavelet in the main energy interval from the filtered wavelet and perform smoothing processing; The target wavelet construction module is used to calculate the phase spectrum corresponding to the waveform after smoothing, and construct the target wavelet based on the phase spectrum and the smoothed spectrum linear phase short wavelet.

6. The device for estimating source wavelet according to claim 5, characterized in that: The filtering module includes: A filter coefficient calculation submodule, configured to calculate linear filter coefficients based on the seismic wave forward simulation shot gather prediction data and the observation data; The filtering submodule is configured to filter the Ricker wavelet using the filter coefficients.

7. The device for estimating source wavelet according to claim 5, characterized in that: The linear phase short wavelet building module includes: a fitting submodule, configured to extract an amplitude spectrum from the filtered wavelet, and fit the amplitude spectrum using a bell curve to obtain a corresponding target bell curve; The first construction submodule is used to invert the linear phase spectrum of the seismic wave forward simulation shot gather prediction data through the target bell-shaped curve to construct the smoothed spectrum linear phase short wavelet.

8. The device for estimating source wavelets according to claim 7, characterized in that: The target wavelet construction module includes: A second construction submodule is configured to construct the target wavelet based on the amplitude spectrum and the phase spectrum in combination with a preset target wavelet determination expression; The target wavelet determination expression is specifically: W3(t) is the time domain function of the target wavelet, t is time, A(f) is the amplitude spectrum, is the phase spectrum, i is the imaginary unit, and f is the frequency variable.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 4 is executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

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