CN113359187B - Wavelet sidelobe elimination method for seismic data - Google Patents
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
The invention relates to a wavelet sidelobe elimination method of seismic data; in order to solve the difficulty of thin mutual reservoir identification, wavelet side lobe elimination is carried out on seismic data according to frequency domain complex filtering; estimating seismic wavelets according to the frequency spectrum of the seismic data; (2) eliminating side lobes of the seismic wavelets in a time domain; (3) an energy equalization operator for wavelet sidelobe elimination; (4) constructing a transmission filter according to wavelet spectrums before and after wavelet sidelobe elimination; (5) conducting filter is used for eliminating wavelet sidelobe of the seismic data; the seismic wavelet in the step (1) is the seismic wavelet which is extracted from the actual seismic record and accurately reflects the waveform distortion effect of seismic waves when the seismic waves propagate in a stratum medium. The method has the advantages of eliminating wavelet side lobes of the seismic data, increasing effective low-frequency components of the seismic data, and improving the longitudinal resolution of seismic records, thereby enhancing the thin mutual reservoir identification capability.
Description
Technical Field
The invention relates to a method for improving the quality of seismic exploration wave data, in particular to a wavelet side lobe elimination method of seismic data.
Background
At present, geological targets of oil and gas containing basins in the world are more and more complex, problems of thin reservoirs, small transverse span and the like are faced, and a geophysical method for improving seismic data resolution is urgently needed to be developed, so that thin mutual reservoirs rich in oil and gas are effectively described. The resolution of the seismic data is directly controlled by the length of the seismic wavelet, and the shorter the wavelet length of the time domain is, the higher the resolution of the seismic data is.
However, when seismic waves actually propagate in a subsurface formation, high-frequency energy of the seismic waves is gradually absorbed by a viscoelastic formation medium, seismic wavelets appearing on a seismic reflection profile are gradually stretched as the propagation distance of the seismic waves increases, and the wavelet stretching directly causes gradual reduction of the resolution of seismic data. The inventor firstly proposes a stabilized Inverse Q filtering method in 2002 so as to improve the resolution of Seismic data (available and effective application of reach of Inverse Q filtering, Geophysics, vol: 67/2002/inventor; Inverse Q-filter for differential resolution enhancement, Geophysics, vol: 71/2006/inventor; differential Inverse Q filtering, Blackwell Publishing, Oxford, UK/2008/inventor). The inverse Q filtering method can only correct the phase of the seismic wave before, and cannot compensate the amplitude of the seismic data. And the Wang's stabilized inverse Q filtering method realizes amplitude compensation and phase correction of seismic data synchronization.
Researchers have proposed modifying the amplitude spectrum of seismic data by the maximum entropy spectrum estimation method to obtain an ideal amplitude spectrum (CN108445539A/2018.03.07), but the ideal amplitude spectrum in the method has no definite physical basis as a support and has no obvious effect in practical application.
Disclosure of Invention
It is an object of the present invention to overcome the above-mentioned deficiencies of the prior art and to provide a wavelet sidelobe canceling method for seismic data that is capable of identifying thin interbed reflections from seismic profiles.
In order to achieve the above object, the wavelet sidelobe elimination method of seismic data of the invention, characterized by that to utilize complex filtering of frequency domain to carry on wavelet sidelobe elimination to seismic data, the step has (1) according to the frequency spectrum estimation seismic wavelet of seismic data; (2) eliminating side lobes of the seismic wavelets in a time domain; (3) an energy equalization operator for wavelet sidelobe elimination; (4) constructing a transmission filter according to wavelet spectrums before and after wavelet sidelobe elimination; (5) conducting filter is used for eliminating wavelet sidelobe of the seismic data; the seismic wavelet in the step (1) is the seismic wavelet which is extracted from the actual seismic record and accurately reflects the waveform distortion effect of seismic waves when the seismic waves propagate in a stratum medium. The physical basis used as the technical support of the method is the wavelet sidelobe of the seismic data, and the invention provides a wavelet sidelobe elimination method technology directly aiming at the essence of the problem according to the physical basis.
The advantages of the invention are as follows: after the amplitude of seismic data is compensated by the inverse Q filtering method in the prior art, the main frequency is increased. However, after the amplitude spectrum normalization processing, while the dominant frequency is shifted to the high frequency direction, the amplitude of the low frequency amplitude with respect to the dominant frequency seems to be suppressed, and a phenomenon of low frequency loss appears, although the absolute value of the amplitude may not be changed. However, such low-frequency missing tends to generate wavelet side lobes, which tends to affect the subsequent imaging processing of seismic data and the effect of reservoir inversion. Even if the seismic data is not subjected to inverse Q filtering processing, wavelet sidelobes exist in the seismic data because a low-frequency wave limiting means is required to be adopted for suppressing interference such as surface waves during field data acquisition or data preprocessing. For example, due to the limitation of the geophone equipment, the seismic signals collected in the field lack low-frequency components after being uploaded by the geophone. This low frequency filtering phenomenon can be expressed in terms of the detector transfer function, and if the denominator term of the detector transfer function can be cancelled as much as possible, the low frequency filtering effect of the detector can be compensated. However, the propagation process of seismic waves in the viscoelastic medium of the stratum and the absorption attenuation of the seismic waves during the propagation process are the main factors causing waveform distortion. Therefore, the method has the advantages of reflecting the waveform distortion effect of seismic waves in the stratum medium and identifying the thin-layer reflection from the seismic section.
As optimization, the step (1) estimates the seismic wavelets according to the frequency spectrum of the seismic data, and is realized by three steps: calculating a power spectrum of the seismic data; smoothing and filtering to obtain an amplitude spectrum; and constructing the seismic wavelets of the mixed phase by taking the smooth amplitude spectrum as a constraint condition.
As optimization, the side lobe of the seismic wavelet eliminated in the time domain in the step (2) is directly eliminated by wavelet shaping; the wavelet shaping function is defined into three sections: when- ∞<(t-τ)<-T 1 Is a first stage and is shaped into
When is-T 1 ≤(t-τ)≤T 2 When it is the second stage, h (t) is 1; when T is 2 <(t-τ)<Infinity time is a third stage, the shaping is
Where t is time and τ is the center position of the wavelet; the second segment is a non-shaped segment of wavelets having a length T 1 +T 2 The first and third sections are shaping sections with a width of σ 1 And σ 2 And (5) controlling parameters.
As an optimization, the time domain seismic wavelet sidelobe elimination in the step (2) is based on template wavelet sidelobe elimination in the time domain, frequency components are resampled in the frequency domain, each frequency component of the signal comprises both amplitude and corresponding phase, and the seismic wavelet sidelobe elimination process not only changes the amplitude of the seismic data but also changes the corresponding phase of the seismic data, but also is beneficial to identifying thin interbedded reservoirs.
As optimization, the energy balance operator for wavelet sidelobe elimination is set in the step (3); the ratio of the wavelet energy before wavelet sidelobe elimination to the wavelet energy after wavelet sidelobe elimination is an energy balance operator; an energy equalization operator is applied to the wavelet after wavelet sidelobe canceling.
As optimization, the energy balance operator for wavelet sidelobe elimination set in step (3) is to apply the energy balance operator, so that the energy of wavelets after sidelobe elimination is kept unchanged, and the total energy of the processed seismic data volume is kept unchanged.
As optimization, the step (4) of constructing the transmission filter is based on wavelet spectrums before and after wavelet sidelobe elimination, and calculating a plurality of transmission filters; constructing a transmission filter by adopting an inverse problem solving method; the initial wavelet spectrum is W (f), and the wavelet spectrum after wavelet sidelobe cancellation is
The wavelet sidelobe canceling process is expressed as
Wherein the conduction filter is H (f); seismic wavelets obtained from seismic data and wavelets after side lobe elimination are mixed phase wavelets, and a stable transmission filter is constructed by adopting a stable inverse problem solving method.
As optimization, the construction method of the seismic wavelet sidelobe elimination conductive filter in the step (4) is as follows; after wavelet sidelobes are eliminated in a time domain through a wavelet shaping technology, the wavelet sidelobes are converted into a frequency domain to construct a wavelet sidelobe eliminating conduction filter; the sidelobe canceling guided filter is a frequency-variant factor, equivalent to a resampling in the frequency domain, and is not a conventional constant factor independent of frequency.
As optimization, the wavelet sidelobe elimination processing is carried out on the seismic data by applying the conductive filter in the step (5), namely the conductive filter is applied to the frequency domain seismic data to generate new frequency domain seismic data; and (4) realizing inverse Fourier transform and outputting the seismic data of the time domain. The wavelet sidelobes of the new data are eliminated and the resolution is improved. Therefore, there is an advantage in that thin layer reflection can be recognized from the seismic section.
After the technical scheme is adopted, the wavelet sidelobe elimination method of the seismic data has the beneficial effects that: the wavelet side lobe of the seismic data is eliminated, and the effective low-frequency components of the seismic data are increased; the beneficial effects still lie in: the seismic wavelet sidelobes are eliminated, and the longitudinal resolution of the seismic record is improved, thereby enhancing the ability to identify thin layer reflections from the seismic profile.
Drawings
FIG. 1 is a block flow diagram of a wavelet sidelobe canceling method of seismic data of the present invention; FIG. 2 is a diagram of an embodiment of the wavelet sidelobe canceling method of seismic data of the present invention showing an amplitude spectrum (top) obtained from the original seismic data and its estimated seismic wavelets (bottom); FIG. 3 is a diagram of a wavelet sidelobe canceling method embodiment of the invention showing a wavelet shaping function; FIG. 4 is a diagram of wavelets (top) and their corresponding amplitude spectra (bottom) after wavelet sidelobe canceling in an embodiment of a wavelet sidelobe canceling method of seismic data in accordance with the present invention; FIG. 5 illustrates an example comparing the amplitude spectrum of the original seismic data wavelet (solid line) with the amplitude spectrum of the wavelet after wavelet sidelobe cancellation (dashed line); the FIG. 6 embodiment compares seismic wavelets prior to wavelet sidelobe canceling (solid line) to seismic wavelets after wavelet sidelobe canceling (dashed line). As shown in the figure, the wavelet sidelobe elimination method of the seismic data has the beneficial effects of eliminating the seismic wavelet sidelobe and improving the longitudinal resolution of seismic records, thereby enhancing the capability of identifying the thin layer reflection from the seismic profile.
Detailed Description
The wavelet sidelobe elimination method of the seismic data of the invention is to utilize complex filtering of frequency domain to carry out wavelet sidelobe elimination on the seismic data, and the implementation steps comprise: (1) estimating seismic wavelets according to the frequency spectrum of the seismic data; (2) eliminating side lobes of the seismic wavelets in the time domain; (3) setting an energy balance operator for wavelet sidelobe elimination; (4) constructing a transmission filter according to wavelet spectrums before and after sidelobe elimination; (5) conducting filter is used for eliminating wavelet sidelobe of the seismic data; the seismic wavelet in the step (1) is the seismic wavelet which is extracted from the actual seismic record and accurately reflects the waveform distortion effect of seismic waves when the seismic waves propagate in a stratum medium. The physical basis used as the technical support of the method is the wavelet sidelobe of the seismic data, and the invention provides a wavelet sidelobe elimination method technology directly aiming at the essence of the problem according to the physical basis.
The advantages of the invention are as follows: after the amplitude of seismic data is compensated by the inverse Q filtering method in the prior art, the main frequency is increased. However, after the amplitude spectrum normalization processing, while the dominant frequency is shifted to the high frequency direction, the amplitude of the low frequency amplitude with respect to the dominant frequency seems to be suppressed, and a phenomenon of low frequency loss appears, although the absolute value of the amplitude may not be changed. However, such low-frequency missing tends to generate wavelet side lobes, which tends to affect the subsequent imaging processing of seismic data and the effect of reservoir inversion. Even if the seismic data is not subjected to inverse Q filtering processing, wavelet sidelobes exist in the seismic data because a low-frequency wave limiting means is required to be adopted for suppressing interference such as surface waves during field data acquisition or data preprocessing. For example, due to the limitation of the geophone equipment, the seismic signals collected in the field lack low-frequency components after being uploaded by the geophone. This low frequency filtering phenomenon can be expressed in terms of the detector transfer function, and if the denominator term of the detector transfer function can be cancelled as much as possible, the low frequency filtering effect of the detector can be compensated. However, the propagation process of seismic waves in the viscoelastic medium of the stratum and the absorption attenuation of the seismic waves during the propagation process are the main factors causing waveform distortion. Therefore, the method has the advantages of reflecting the waveform distortion effect of seismic waves in the stratum medium and identifying the thin-layer reflection from the seismic section.
The step (1) is to extract accurate seismic wavelets from actual seismic data, wherein the wavelets have the advantage of reflecting the waveform distortion effect of seismic waves when the seismic waves propagate in a stratum medium. The step (1) of estimating the seismic wavelet from the seismic data is realized in three steps: calculating a power spectrum of the seismic data; smoothing and filtering to obtain an amplitude spectrum; and constructing the seismic wavelets of the mixed phase by taking the smooth amplitude spectrum as a constraint condition.
The step (2) of eliminating the side lobe of the seismic wavelet in the time domain directly eliminates the side lobe of the sample wavelet through the wavelet shaping technology; the wavelet shaping function is defined into three sections: when- ∞<(t-τ)<-T 1 Is first whenSegment, is shaped into
When is-T 1 ≤(t-τ)≤T 2 When it is the second stage, h (t) is 1; when T is 2 <(t-τ)<Infinity time is a third stage, the shaping is
Where t is time and τ is the center position of the wavelet; the second segment is a non-shaped segment of wavelets having a length T 1 +T 2 The first and third sections are shaping sections with a width of σ 1 And σ 2 And (5) controlling parameters.
The physical basis of the wavelet sidelobe elimination in the time domain in the step (2) is based on the basic principle that the wavelet sidelobe elimination in the time domain is equal to the resampling of frequency components in the frequency domain, each frequency component of the signal comprises both amplitude and corresponding phase, and the seismic wavelet sidelobe elimination processing not only changes the amplitude of the seismic data but also changes the corresponding phase of the seismic data, but also is beneficial to identifying thin mutual reservoirs.
The energy balance operator for wavelet sidelobe elimination set in the step (3) is as follows: the ratio of the wavelet energy before wavelet sidelobe elimination to the wavelet energy after wavelet sidelobe elimination is an energy balance operator; an energy equalization operator is applied to the wavelet after wavelet sidelobe canceling.
The energy balance operator for wavelet sidelobe elimination set in the step (3) is an energy balance operator, and not only is the energy of the wavelet after sidelobe elimination kept unchanged, but also the total energy of the processed seismic data volume is kept unchanged.
And (4) constructing the transmission filter, namely calculating a complex transmission filter according to the wavelet spectrums before and after the sidelobe elimination. Constructing a transmission filter by adopting an inverse problem solving method; the initial wavelet spectrum is W (f), and the wavelet spectrum after wavelet sidelobe cancellation is
The wavelet sidelobe canceling process is expressed as
Wherein the conduction filter is H (f); because the seismic wavelets obtained from the seismic data and the wavelets after the sidelobe elimination are both mixed-phase wavelets, a stable transmission filter is constructed by adopting a stable inverse problem solving method.
The construction method of the wavelet sidelobe elimination conductive filter of the seismic data in the step (4) is as follows: after wavelet sidelobes are eliminated in a time domain through a wavelet shaping technology, the wavelet sidelobes are converted into a frequency domain to construct a wavelet sidelobe eliminating conduction filter; the sidelobe canceling guided filter is thus a frequency variant factor, equivalent to a resampling in the frequency domain, rather than the conventional frequency independent constant factor.
The wavelet sidelobe processing is carried out on the seismic data by applying the conductive filter in the step (5), namely the conductive filter is applied to the frequency domain seismic data to generate new frequency domain seismic data; the method realizes the inverse Fourier transform to output the seismic data of the time domain, eliminates the wavelet side lobe of new data, improves the resolution ratio and has the advantage of identifying the thin layer reflection from the seismic profile.
In order to make the wavelet sidelobe canceling method and advantages of the seismic data of the present invention more clear, embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. FIG. 1 is a basic implementation flow of the wavelet sidelobe canceling method for seismic data according to the present invention.
And S101, estimating seismic wavelets according to the frequency spectrum of the seismic data, wherein the estimation is realized in three steps. Calculating a power spectrum of the seismic data; smoothing and filtering to obtain an amplitude spectrum; and constructing the seismic wavelets of the mixed phase by taking the smooth amplitude spectrum as a constraint condition. FIG. 2 is a diagram of an embodiment of step S101 showing an amplitude spectrum (top) obtained from raw seismic data and its estimated seismic wavelets (bottom); wavelets of seismic data are constructed from amplitude spectra obtained from the original seismic data.
And step S102, directly eliminating the side lobe of the seismic wavelet in the time domain through a wavelet shaping technology. The wavelet shaping function is defined into three sections: when- ∞<(t-τ)<-T 1 Is a first stage and is shaped into
When is-T 1 ≤(t-τ)≤T 2 When it is the second stage, h (t) is 1; when T is 2 <(t-τ)<Infinity time is a third stage, the shaping is
Where t is time and τ is the center position of the wavelet; the second segment is a non-shaped segment of wavelets having a length T 1 +T 2 The first and third sections are shaping sections with a width of σ 1 And σ 2 And (5) controlling parameters. FIG. 3 shows wavelet shaping functions in an embodiment of a wavelet sidelobe canceling method for seismic data in accordance with the present invention.
The time domain wavelet sidelobe elimination utilizes that the seismic wavelet sidelobe elimination pair in the time domain is equal to the frequency component resampling in the frequency domain, each frequency component of the signal comprises both amplitude and corresponding phase, and the seismic wavelet sidelobe elimination processing not only changes the amplitude of the seismic data, but also changes the corresponding phase of the seismic data, thereby being beneficial to identifying thin mutual reservoirs. FIG. 4 is a diagram of wavelets (top) and their corresponding amplitude spectra (bottom) after wavelet sidelobe canceling in an embodiment of a wavelet sidelobe canceling method of seismic data in accordance with the present invention; the wavelet sidelobe canceling embodiment shows that the low frequency component on the left side of the band is effectively expanded after the sidelobe canceling is performed on the seismic wavelet.
Step S103, an energy balance operator for wavelet sidelobe elimination, wherein the ratio of wavelet energy before wavelet sidelobe elimination to wavelet energy after wavelet sidelobe elimination is the energy balance operator; an energy equalization operator is applied to the wavelet after wavelet sidelobe canceling. The physical basis of the wavelet sidelobe elimination energy balance operator is that the energy balance operator is applied, so that the energy of wavelets after sidelobe elimination is kept unchanged, and the total energy of the processed seismic data volume is kept unchanged.
And step S104, constructing a transmission filter according to the wavelet spectrums before and after the sidelobe elimination. Constructing a transmission filter by adopting an inverse problem solving method; the initial wavelet spectrum is W (f), wavelets after wavelet sidelobe cancellationThe frequency spectrum is
The wavelet sidelobe canceling process is expressed as
Wherein the conduction filter is H (f); because the seismic wavelets obtained from the seismic data and the wavelets after the sidelobe elimination are both mixed-phase wavelets, a stable transmission filter is constructed by adopting a stable inverse problem solving method.
Step S105, conducting filter is applied to the seismic data to perform wavelet sidelobe elimination, namely the conducting filter is applied to the frequency domain seismic data to generate new frequency domain seismic data; the seismic data of the time domain is output by inverse Fourier transform, the wavelet side lobe of new data is eliminated, and the resolution is improved.
The FIG. 5 embodiment compares the amplitude spectrum of the original seismic data wavelet (solid line) with the amplitude spectrum of the wavelet after wavelet sidelobe cancellation (dashed line). As shown in the figure, the wavelet sidelobe elimination method for the seismic data has the advantages that the wavelet sidelobe of the seismic data is eliminated, and effective low-frequency components of the seismic data are increased.
The FIG. 6 embodiment compares seismic wavelets prior to wavelet sidelobe canceling (solid line) to seismic wavelets after wavelet sidelobe canceling (dashed line). As shown in the figure, the wavelet sidelobe elimination method of the seismic data has the beneficial effects of eliminating the seismic wavelet sidelobe and improving the longitudinal resolution of seismic records, thereby enhancing the capability of identifying the thin layer reflection from the seismic profile.
In summary, the wavelet sidelobe elimination method of the seismic data has the beneficial effects that: eliminating wavelet side lobes of the seismic data and increasing effective low-frequency components of the seismic data; the beneficial effects still lie in: the seismic wavelet sidelobes are eliminated, and the longitudinal resolution of the seismic record is improved, thereby enhancing the ability to identify thin layer reflections from the seismic profile.
Claims (8)
1. A wavelet sidelobe elimination method of seismic data is characterized in that wavelet sidelobe elimination is carried out on the seismic data by using frequency domain complex filtering, and the steps comprise (1) seismic wavelets are estimated according to frequency spectrums of the seismic data; (2) eliminating side lobes of the seismic wavelets in a time domain; (3) setting an energy balance operator for wavelet sidelobe elimination; (4) constructing a transmission filter according to wavelet spectrums before and after wavelet sidelobe elimination; (5) conducting filter is used for eliminating wavelet sidelobe of the seismic data; the seismic wavelet of the step (1) is to extract the seismic wavelet which accurately reflects the waveform distortion effect of seismic waves when the seismic waves propagate in a stratum medium from an actual seismic record;
the side lobe of the seismic wavelet eliminated in the time domain in the step (2) is directly eliminated through wavelet shaping; the wavelet shaping function is defined into three sections: when- ∞<(t-τ)<-T 1 Is a first stage and is shaped into
When is-T 1 ≤(t-τ)≤T 2 When it is the second stage, h (t) is 1; when T is 2 <(t-τ)<Infinity time is a third stage, the shaping is
Where t is time and τ is the center position of the wavelet; the second segment is a non-shaped segment of wavelets having a length T 1 +T 2 The first and third sections are shaping sections with a width of σ 1 And σ 2 And (5) controlling parameters.
2. The wavelet sidelobe canceling method of seismic data according to claim 1, wherein said step (1) of estimating seismic wavelets based on a spectrum of the seismic data is implemented in three steps: calculating a power spectrum of the seismic data; smoothing and filtering to obtain an amplitude spectrum; and constructing the seismic wavelets of the mixed phase by taking the smooth amplitude spectrum as a constraint condition.
3. The wavelet sidelobe canceling method for seismic data according to claim 1, wherein in said step (2) the time domain seismic wavelet sidelobe canceling is based on template wavelet sidelobe canceling in the time domain, wherein frequency components are resampled in the frequency domain, and wherein each frequency component of the signal includes both an amplitude and a corresponding phase, and wherein the seismic wavelet sidelobe canceling process not only changes the amplitude of the seismic data but also changes the corresponding phase of the seismic data, and facilitates identification of thin interbed reservoirs.
4. The wavelet sidelobe canceling method of seismic data according to claim 1, wherein said energy equalization operator for setting wavelet sidelobe canceling in said step (3) is: the ratio of the wavelet energy before wavelet sidelobe elimination to the wavelet energy after wavelet sidelobe elimination is an energy balance operator; an energy equalization operator is applied to the wavelet after wavelet sidelobe canceling.
5. The wavelet sidelobe canceling method of seismic data according to claim 1, wherein said energy equalization operator for setting wavelet sidelobe canceling in said step (3) is: and applying an energy balance operator to keep the wavelet energy unchanged after the side lobe is eliminated and keep the total energy of the processed seismic data volume unchanged.
6. The wavelet sidelobe canceling method of seismic data according to claim 1, wherein said step (4) of constructing a guided filter calculates a complex guided filter based on wavelet spectra before and after the sidelobe canceling; constructing a transmission filter by adopting an inverse problem solving method; the initial wavelet spectrum is W (f), and the wavelet spectrum after wavelet sidelobe cancellation is
The wavelet sidelobe canceling process is expressed as
Wherein the conduction filter is H (f); because the seismic wavelets obtained from the seismic data and the wavelets after the sidelobe elimination are both mixed-phase wavelets, a stable transmission filter is constructed by adopting a stable inverse problem solving method.
7. The wavelet sidelobe canceling method of seismic data according to claim 6, wherein the construction method of the seismic wavelet sidelobe canceling conductive filter of the step (4) is; after wavelet sidelobes are eliminated in a time domain through a wavelet shaping technology, converting the wavelet sidelobes into a frequency domain to construct a sidelobe eliminating conduction filter; the sidelobe canceling guided filter is a frequency variant factor, equivalent to a re-sampling in the frequency domain, rather than the conventional frequency independent constant factor.
8. The wavelet sidelobe canceling method for seismic data according to claim 1, wherein said step (5) of applying a conductive filter to wavelet sidelobe canceling the seismic data generates new frequency domain seismic data by applying the conductive filter to the frequency domain seismic data; and (4) realizing inverse Fourier transform and outputting the seismic data of the time domain.
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| CN109946740B (en) * | 2019-03-01 | 2020-06-30 | 成都理工大学 | Seismic resolution enhancement method based on wide flat spectrum seismic wavelet shaping |
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