CN112068202A - High-precision time-varying wavelet extraction method and system - Google Patents
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Abstract
The invention provides a high-precision time-varying wavelet extraction method and a system, comprising the following steps: acquiring target seismic data of a target area; the target seismic data carries geological information of an underground space of a target area; performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data; decomposing time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum; performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum; and obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum. The method and the device solve the technical problem that in the prior art, the calculation of the reflection coefficient is inaccurate due to low extraction precision of the time-varying wavelet.
Description
Technical Field
The invention relates to the technical field of seismic data processing, in particular to a high-precision time-varying wavelet extraction method and system.
Background
The extraction of the seismic wavelets is one of the important steps in the seismic data processing, and has important significance on the data processing result. In the process of transmitting seismic waves in an underground medium, due to the existence of internal friction and heterogeneity, frequency components of actual seismic waves can change along with the transmission, so that the seismic wavelets show attenuation characteristics in the process of wave field transmission, and seismic signals are non-stationary signals. The estimation of the seismic wavelet has important influence on seismic data deconvolution, directly determines the longitudinal resolution of the seismic data, and has important significance for identifying buried geology with complex structure, small scale and deep burial depth.
The traditional resolution improvement method is mostly based on a seismic trace convolution model, and the seismic wavelet is assumed to be a stable signal and does not change along with the propagation time. In fact, seismic waves propagate in a geometrically dispersive manner in a heterogeneous, viscoelastic subsurface space as non-stationary signals. The existing methods mostly assume that the reflection coefficient of the underground medium is white-noise distribution and does not accord with the actual data characteristics. Therefore, the technical problem of inaccurate calculation of the reflection coefficient caused by low extraction precision of the time-varying wavelet exists in the prior art.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for extracting a time-varying wavelet with high precision, so as to alleviate the technical problem in the prior art that the reflection coefficient is not calculated accurately due to low accuracy of extracting the time-varying wavelet.
In a first aspect, an embodiment of the present invention provides a high-precision time-varying wavelet extraction method, including: acquiring target seismic data of a target area; the target seismic data carries geological information of an underground space of the target area; performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data; decomposing the time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum; performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum; and obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
Further, the time-frequency transformation is preset generalized S transformation; performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data, wherein the time-frequency seismic data comprises the following steps: by the formula:
to the abovePerforming preset generalized S transformation on the target seismic data to obtain time-frequency seismic data; wherein τ is time, f is frequency, x (t) is the target seismic data, λ, b and p are three preset constants, VsAnd x (tau, f) is the time-frequency seismic data.
Further, decomposing the time-frequency seismic data by using logarithmic transformation to obtain a time-varying wavelet time-frequency spectrum and a reflection coefficient time-frequency spectrum, including: decomposing the time-frequency seismic data into the product of time-varying sub-wave time frequency spectrum and reflection coefficient time frequency spectrum; and decomposing the log-form time-frequency seismic data into the sum of log-form time-varying wavelet time-frequency spectrum and log-form reflection coefficient time-frequency spectrum by using log transformation.
Further, performing fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum, including: establishing a target function when Fourier series fitting is carried out on the time-varying sub-wave time frequency spectrum through a least square method; wherein the mathematical expression of the objective function is:
f(τ,fm) In order to perform Fourier series expansion,
is the square of the norm of L2,
for fitting error, m is the frequency point, fmThe frequency of the mth frequency point; and carrying out iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
Further, obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum, including: obtaining a target reflection coefficient time frequency spectrum based on the time-frequency seismic data, the fitted time-varying wavelet time frequency spectrum and a target relational expression between the time-frequency seismic data and the time-varying wavelet time frequency spectrum; the target relation is: ln | Vsx(τ,f)|≈ln|σ(τ,f)|+ln|Vsr(τ,f)|，Vsx (tau, f) is the time-frequency seismic data, sigma (tau, f) is the time-varying sub-wave time-frequency spectrum, Vsr (τ, f) is the reflection coefficient time spectrum; and performing time-frequency transformation inverse transformation on the target reflection coefficient time-frequency spectrum to obtain a reflection coefficient.
In a second aspect, an embodiment of the present invention further provides a high-precision time-varying wavelet extraction system, including: the system comprises an acquisition module, a transformation module, a decomposition module, a fitting module and a calculation module, wherein the acquisition module is used for acquiring target seismic data of a target area; the target seismic data carries geological information of an underground space of the target area; the transformation module is used for carrying out time-frequency transformation on the target seismic data to obtain time-frequency seismic data; the decomposition module is used for decomposing the time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum; the fitting module is used for performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum; and the computing module is used for obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
Further, the time-frequency transformation is preset generalized S transformation; the transformation module is further configured to: by the formula:
performing preset generalized S transformation on the target seismic data to obtain time-frequency seismic data; wherein τ is time, f is frequency, x (t) is the target seismic data, λ, b and p are three preset constants, VsAnd x (tau, f) is the time-frequency seismic data.
Further, the fitting module is further configured to: establishing a target function when Fourier series fitting is carried out on the time-varying sub-wave time frequency spectrum through a least square method; wherein the mathematical expression of the objective function is:
f(τ,fm) In order to perform Fourier series expansion,
is the square of the norm of L2,
for fitting error, m is the frequency point, fmThe frequency of the mth frequency point; and carrying out iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable medium having non-volatile program code executable by a processor, where the program code causes the processor to execute the method of the first aspect.
According to the high-precision time-varying wavelet extraction method and system, Fourier series expansion fitting is adopted in the wavelet extraction process, the accuracy of wavelet fitting is improved, high-precision wavelet extraction is achieved, the calculated reflection coefficient is more accurate, and the technical problem that the calculation of the reflection coefficient is inaccurate due to the fact that the extraction precision of the time-varying wavelet is low in the prior art is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for extracting high-precision time-varying wavelets according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a high-precision time-varying wavelet extraction system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
FIG. 1 is a diagram illustrating a method for extracting a high-precision time-varying wavelet according to an embodiment of the present invention. As shown in fig. 1, the method specifically includes the following steps:
step S102, target seismic data of a target area are obtained; the target seismic data carries the geological information of the underground space of a target area, and the target area is an area to be processed.
Preferably, in an embodiment of the present invention, the target seismic data is shot gather data.
And step S104, performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data.
Optionally, the time-frequency transform is a preset generalized S-transform.
Specifically, by the formula:
performing preset generalized S transformation on target seismic data to obtain time-frequency seismic data; wherein tau is time, f is frequency, x (t) is target seismic data, lambda, b and p are three preset constants, VsAnd x (tau, f) is time-frequency seismic data.
In the embodiment of the invention, the preset generalized S transformation is an improved transformation of the traditional generalized S transformation, the problem that the energy of the seismic signal in an S domain leaks along a time axis is considered, and the preset constant b is an adjustable parameter related to the frequency f.
And S106, decomposing the time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum.
Optionally, decomposing the time-frequency seismic data into a product of a time-varying wavelet time-frequency spectrum and a reflection coefficient time-frequency spectrum; for example, time-frequency seismic data may be represented as: i Vsx(τ,f)|≈|σ(τ,f)||Vsr(τ,f)|，|Vsx (tau, f) | is time-frequency seismic data obtained by non-stationary seismic channel generalized S transformation, | sigma (tau, f) | is time-varying wavelet time-frequency spectrum, | Vsr (τ, f) | is the reflection coefficient time spectrum obtained by the generalized S transform of the reflection coefficient sequence.
And decomposing the log-form time-frequency seismic data into the sum of log-form time-varying wavelet time-frequency spectrum and log-form reflection coefficient time-frequency spectrum by using log transformation. For example, taking the logarithm of time-frequency seismic data may result in: ln | Vsx(τ,f)|≈ln|σ(τ,f)|+ln|Vsr(τ,f)|。
And S108, performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain the fitted time-varying wavelet time-frequency spectrum.
And step S110, obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
According to the high-precision time-varying wavelet extraction method provided by the invention, Fourier series expansion fitting is adopted in the wavelet extraction process, the accuracy of wavelet fitting is improved, high-precision wavelet extraction is realized, the calculated reflection coefficient is more accurate, and the technical problem of inaccurate calculation of the reflection coefficient caused by low extraction precision of the time-varying wavelet in the prior art is solved.
Optionally, the fitting process in step S108 specifically includes:
step S1081, establishing a target function when Fourier series fitting is carried out on a time-varying sub-wave time frequency spectrum through a least square method; wherein, the mathematical expression of the objective function is:
f(τ,fm) Is a Fourier seriesUnfolding:
And step S1082, performing iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
Optionally, step S110 further includes the steps of:
step S1101, fitting a time-varying wavelet time-frequency spectrum after fitting based on time-frequency seismic data, and a target relational expression between the time-varying wavelet time-frequency spectrum and the time-varying wavelet seismic data to obtain a target reflection coefficient time-frequency spectrum; the target relationship is: ln | Vsx(τ,f)|≈ln|σ(τ,f)|+ln|Vsr(τ,f)|，Vsx (tau, f) is time-frequency seismic data, sigma (tau, f) is time-varying sub-wave time-frequency spectrum, Vsr (τ, f) is the reflection coefficient time spectrum.
Step S1102, time-frequency transformation inverse transformation is carried out on the target reflection coefficient time-frequency spectrum to obtain a reflection coefficient. The time-frequency transformation is preset generalized S transformation, and the mathematical expression of the time-frequency transformation inverse transformation is as follows:
where s (t) is the reflection coefficient.
The invention provides a high-precision time-varying wavelet extraction method, which comprises the steps of firstly, obtaining target seismic data of a target area; then, performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data; decomposing time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum; then, Fourier series fitting is carried out on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum; and finally, obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum. The time-frequency transformation method has the advantages that the problem of energy leakage of seismic signals in a time-frequency domain is considered, and the assumption that the reflection coefficient is white is overcome; meanwhile, Fourier series expansion fitting is adopted in the wavelet extraction process, the accuracy of wavelet fitting is improved, high-precision wavelet extraction is achieved, and further, the reflection coefficient is calculated according to the extracted high-precision wavelet, so that the obtained reflection coefficient is more accurate.
Example two:
FIG. 2 is a diagram illustrating a high-precision time-varying wavelet extraction system according to an embodiment of the present invention. As shown in fig. 2, the system includes: an acquisition module 10, a transformation module 20, a decomposition module 30, a fitting module 40 and a calculation module 50.
Specifically, the acquiring module 10 is configured to acquire target seismic data of a target area; the target seismic data carries subsurface space geological information for the target region.
Preferably, in an embodiment of the present invention, the target seismic data is shot gather data.
And the transformation module 20 is configured to perform time-frequency transformation on the target seismic data to obtain time-frequency seismic data.
Optionally, the time-frequency transform is a preset generalized S-transform.
Specifically, the transformation module 20 is further configured to: by the formula:
performing preset generalized S transformation on target seismic data to obtain time-frequency seismic data; wherein tau is time, f is frequency, x (t) is target seismic data, lambda, b and p are three preset constants, VsAnd x (tau, f) is time-frequency seismic data.
And the decomposition module 30 is configured to decompose the time-frequency seismic data by using logarithmic transformation to obtain a time-varying sub-wave time-frequency spectrum and a reflection coefficient time-frequency spectrum.
And the fitting module 40 is configured to perform fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum.
And the calculating module 50 is used for obtaining the reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
According to the high-precision time-varying wavelet extraction system, Fourier series expansion fitting is adopted in the wavelet extraction process, the accuracy of wavelet fitting is improved, high-precision wavelet extraction is achieved, the calculated reflection coefficient is more accurate, and the technical problem that the calculation of the reflection coefficient is inaccurate due to the fact that the extraction accuracy of the time-varying wavelet is low in the prior art is solved.
Optionally, the fitting module 40 is further configured to: establishing a target function when Fourier series fitting is carried out on a time-varying sub-wave time spectrum by a least square method; and (4) carrying out iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
Wherein, the mathematical expression of the objective function is:
f(τ,fm) In order to perform Fourier series expansion,
is the square of the norm of L2,
for fitting error, m is the frequency point, fmIs the frequency of the mth frequency point.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the method in the first embodiment are implemented.
The embodiment of the invention also provides a computer readable medium with a non-volatile program code executable by a processor, wherein the program code causes the processor to execute the method in the first embodiment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A high-precision time-varying wavelet extraction method is characterized by comprising the following steps:
acquiring target seismic data of a target area; the target seismic data carries geological information of an underground space of the target area;
performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data;
decomposing the time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum;
performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum;
and obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
2. The method of claim 1, wherein the time-frequency transform is a preset generalized S-transform; performing time-frequency transformation on the target seismic data to obtain time-frequency seismic data, wherein the time-frequency seismic data comprises the following steps:
by the formula:
performing preset generalized S transformation on the target seismic data to obtain time-frequency seismic data;
wherein τ is time, f is frequency, x (t) is the target seismic data, λ, b and p are three preset constants, VsAnd x (tau, f) is the time-frequency seismic data.
3. The method of claim 1, wherein decomposing the time-frequency seismic data using a logarithmic transformation to obtain a time-varying wavelet time-frequency spectrum and a reflection coefficient time-frequency spectrum comprises:
decomposing the time-frequency seismic data into the product of time-varying sub-wave time frequency spectrum and reflection coefficient time frequency spectrum;
and decomposing the log-form time-frequency seismic data into the sum of log-form time-varying wavelet time-frequency spectrum and log-form reflection coefficient time-frequency spectrum by using log transformation.
4. The method of claim 2, wherein fitting a fourier series to the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum comprises:
establishing a target function when Fourier series fitting is carried out on the time-varying sub-wave time frequency spectrum through a least square method; wherein the mathematical expression of the objective function is:
f(τ，fm) In order to perform Fourier series expansion,
is the square of the norm of L2,
for fitting error, m is the frequency point, fmIs the frequency of the m-th frequency point；
And carrying out iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
5. The method of claim 1, wherein deriving reflection coefficients based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum comprises:
obtaining a target reflection coefficient time frequency spectrum based on the time-frequency seismic data, the fitted time-varying wavelet time frequency spectrum and a target relational expression between the time-frequency seismic data and the time-varying wavelet time frequency spectrum; the target relation is: ln | Vsx(τ，f)|≈ln|σ(τ，f)|+ln|Vsr(τ，f)|，Vsx (tau, f) is the time-frequency seismic data, sigma (tau, f) is the time-varying sub-wave time-frequency spectrum, Vsr (τ, f) is the reflection coefficient time spectrum;
and performing time-frequency transformation inverse transformation on the target reflection coefficient time-frequency spectrum to obtain a reflection coefficient.
6. A high accuracy time-varying wavelet extraction system, comprising: an acquisition module, a transformation module, a decomposition module, a fitting module and a calculation module, wherein,
the acquisition module is used for acquiring target seismic data of a target area; the target seismic data carries geological information of an underground space of the target area;
the transformation module is used for carrying out time-frequency transformation on the target seismic data to obtain time-frequency seismic data;
the decomposition module is used for decomposing the time-frequency seismic data by utilizing logarithmic transformation to obtain a time-varying sub-wave time frequency spectrum and a reflection coefficient time frequency spectrum;
the fitting module is used for performing Fourier series fitting on the time-varying wavelet time-frequency spectrum to obtain a fitted time-varying wavelet time-frequency spectrum;
and the computing module is used for obtaining a reflection coefficient based on the time-frequency seismic data and the fitted time-varying wavelet time-frequency spectrum.
7. The system according to claim 6, wherein the time-frequency transform is a preset generalized S-transform; the transformation module is further configured to:
by the formula:
performing preset generalized S transformation on the target seismic data to obtain time-frequency seismic data;
wherein τ is time, f is frequency, x (t) is the target seismic data, λ, b and p are three preset constants, VsAnd x (tau, f) is the time-frequency seismic data.
8. The system of claim 7, wherein the fitting module is further configured to:
establishing a target function when Fourier series fitting is carried out on the time-varying sub-wave time frequency spectrum through a least square method; wherein the mathematical expression of the objective function is:
f(τ，fm) In order to perform Fourier series expansion,
is the square of the norm of L2,
for fitting error, m is the frequency point, fmThe frequency of the mth frequency point;
and carrying out iterative solution on the objective function through a confidence domain algorithm to obtain a fitted time-varying wavelet time-frequency spectrum.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1-5.
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