CN104062683A - Combined attenuation random noise processing method based on curvelet transform and total variation - Google Patents
Combined attenuation random noise processing method based on curvelet transform and total variation Download PDFInfo
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Abstract
The invention relates to the technical field of seismic exploration, in particular to a combined attenuation random noise processing method based on curvelet transform and total variation. The method comprises the following steps: acquiring single-shot earthquake data; performing curvelet transform on the acquired single-shot data or stacking of single-shot data; performing multi-scale curvelet threshold denoising; denoising by adopting a total-variation denoising technology; outputting earthquake data through graphic display software. According to the method, an optimal threshold is selected according to the distribution rule of random noise in a curvelet domain to maximize the signal to noise ratio of data, and the aim of optimally denoising is fulfilled through curvelet transform; according to the total variation minimization technology; a curvelet coefficient is adjusted through a total variation minimization technology, thereby overcoming the defect of pseudo-curve caused by separate use of curvelet transform, and making displayed stratum data more real and reliable in order to further perform stratum analysis and obtain more accurate analysis results about the oil content and ore content.
Description
Technical field
The present invention relates to a kind of seismic exploration technique field, particularly the drawing method of random noise in geological data processing.
Background technology
Bent wave conversion (Curvelet conversion) is a kind of newer multiple dimensioned geometric transformation algorithm.1999, Candes and Donoho proposed continuous Qu Bo (Curvelet) conversion on the basis of Ridgelet conversion, i.e. first generation Curvelet conversion; 2002, the people such as Candes proposed Second Generation Curvelet Transform; 2005, the people such as Candes proposed two kinds of fast discrete implementation methods based on Second Generation Curvelet Transform theory: the 1) two-dimensional FFT (Unequally-Spaced Fast Fourier Transform, USFFT) of nonuniform space sampling; 2) Wrapping algorithm (Wrapping-Based Transform).Because Qu Bo has superior locality to frequency and direction, be widely used in geological data process field, wherein, Herrmann F in 2004 etc. are applied to geological data process field by Curvelet conversion at first.
In seismic prospecting, conventional random noise attenuation method is larger to the damage ratio of useful signal, as the denoising of RPF(radial prediction), AMCOD (inclination angle coherence stack), fitting of a polynomial denoising, medium filtering, the RNA denoising method that SVD method and industry member are generally used etc., although RNA is regarded as reasonable random noise attenuation method in all multi-methods, but the method also has certain infringement to useful signal, in order to remove preferably random noise, the people such as Neelamani have introduced in 2008 that a kind of multiple dimensioned transform method---bent wave conversion carrys out random noise attenuation, obtained good effect.Although Curvelet conversion exists many advantages aspect denoising, also there is intrinsic defect in Curvelet conversion---during denoising, easily in seismic section, produce strong energy group, at lineups edge, produce rough phenomenon simultaneously.
Summary of the invention
The object of the present invention is to provide a kind of associating random noise attenuation disposal route based on bent wave conversion and total variation, the method can effectively be suppressed the random noise that affects prestack list big gun data, poststack data quality, reducing to greatest extent the infringement to useful signal, is a kind of effective and efficient random noise attenuation technology.
For realizing above-mentioned technical purpose, the present invention can adopt following technical scheme: a kind of associating random noise attenuation disposal route based on bent wave conversion and total variation, in turn includes the following steps:
1) obtaining of geological data:
First obtain single big gun data: arrange shot point position and a plurality of acceptance points position, then by exciting generation seismic event at the embedding explosive charge of shot point, the seismic event that the subsurface reflective boundary that receives the wave detector that each acceptance point is laid reflects up, the seismic data of the same gun excitation that all wave detectors receive, forms single big gun data;
2) by the stack march wave conversion of single big gun data of obtaining or single big gun data
To superposition of data or single big gun data
fmarch wave conversion obtains bent wave conversion coefficient
c(j, l, k), wherein
c(j, l, k)can be expressed as:
In formula,
cT(f)for
fbent wave conversion,
represent bent wave function,
j, l, krepresent respectively yardstick, direction and location parameter.
k=(k 1 , k 2 ),
k 1 =1,2 ... M,
k 2 =1,2 ... N, wherein
mfor seismic trace number,
nbe that one time-sampling counted, yardstick
j=ceil (log 2 min (M, N)-3), direction
;
3)multiple dimensioned bent ripple threshold denoising
Data, after bent wave conversion, are divided into j yardstick layer, and each yardstick layer all comprises multiple directions data, and innermost layer is called low frequency coefficient layer; Outermost layer is called high frequency coefficient layer; Middle yardstick layer is called medium-high frequency coefficient layer; At different yardstick layers, it is different that the coefficient of useful signal and random noise distributes, at low frequency coefficient layer, the coefficient of useful signal and random noise is obviously boundary not, therefore, at this yardstick layer, retain whole coefficients, in high frequency coefficient layer and medium-high frequency coefficient layer, every one deck is chosen the optimal threshold that signal to noise ratio (S/N ratio) is the highest separately, to reach optimum denoising effect;
Threshold value acquiring method is as follows:
Wherein,
, here
length for geological data;
for the noise variance of a certain yardstick a direction, simultaneously
αvalue be no more than 2.
Then, all bent wave system number and the threshold value to a direction under a certain yardstick
t α carry out size relatively, retain and be not less than threshold value
t α bent wave system number, then the bent wave system number retaining is reconstructed to (also claiming bent ripple inverse transformation), be shown below:
represent bent ripple inverse transformation,
represent through threshold value
bent wave system number after processing,
represent bent wave function;
Conversion
value, obtains different
, to all
carry out Analysis signal-to-noise ratio (SNR), obtain different signal to noise ratio (S/N ratio)s
,
for
corresponding signal to noise ratio (S/N ratio) numerical value, makes
be worth the highest
be worth corresponding threshold value
value is the optimal threshold under this angle of this yardstick;
4) total variation denoising
Signal
by being greater than and being less than optimal threshold
two parts form:
To signal
in bent wave conversion territory, do multiple dimensioned multi-direction threshold process, multiple dimensioned multidirectional optimal threshold is
, reconstruct obtains:
The denoising result obtaining thus
easily there is pseudo-gibbs oscillatory occurences, by total variation minimization technique, can carry out inhibition to a certain degree to this phenomenon; Here make the data after bent ripple threshold denoising is processed
be expressed as
, the total variation of data is as follows:
Wherein
represent data
gradient,
for gradient represents,
represent data
total variation,
for data
support Interval,
coordinate vector for data;
gradient fields be:
Wherein
,
for data
f?
(i+1, j)the numerical value of point, the rest may be inferred for other;
Denoising method based on total variation can realize by the function minimizing below:
Wherein, first makes the image after denoising still can approach preferably original image for approaching item, has certain fidelity; Second is total variation regularization term, and λ is Lagrangian constant, approach and regularization term between play equilibrium activity;
Above-mentioned objective function
be
convex function, it exists the sufficient and necessary condition of extreme value to be
, can obtain thus its corresponding Euler-Largrange equation and be:
This equation is non-linear, in formula
for divergence, suppose that equation meets Neumann boundary condition, by gradient descent method, data are iterated until obtain a stable solution, thereby obtain the data after denoising, its iterative formula is as follows:
Wherein,
represent
the result of inferior iteration,
f n+1 be
n+1the result of inferior iteration, makes initial value
,
represent iteration step length,
represent that function of total variation exists
the subgradient at place;
Conventionally use
replace
, get
and be one very little on the occasion of, its with
differ at least two orders of magnitude, to solve function of total variation at the non-differentiability of some point;
With the geological data after optimal threshold denoising and bent wave system number, as input, utilize total variation minimization technique to adjust bent wave system number, establish maximum iterations, initial value
,
, calculate subgradient
, get step-length
, calculate
; Get
, judgement
whether equaling maximum iterations, is finishing iteration, otherwise continues iteration, until finishing iteration;
5) output geological data: export geological data by Graphics Software.
Above-mentioned steps 2) in, the acquisition methods of superposition of data is: a plurality of single big gun data of obtaining are carried out respectively to ground roll and anomalous amplitude decay, earth's surface-consistent energy compensating, surface consistent deconvolution, earth's surface-consistent residual static correction, then be sorted into common midpoint data field, carry out speed explanation, by the speed of explaining, carry out normal moveout correction, data after normal moveout correction are superposeed in common midpoint territory, obtain superposition of data.
Be in step 5), Graphics Software can comprise the figure show tools in Omega process software, the figure show tools in Cgg process software, figure show tools in Promax process software, figure show tools in Focus process software, figure show tools in Geoeast process software, figure show tools in Landmark interpretation software, the figure show tools in Geoframe interpretation software, a kind of in the wigb program in Matlab.
Beneficial effect of the present invention is: the present invention is by multiple dimensioned bent ripple threshold denoising, decay for random noise, to select optimal threshold according to the regularity of distribution at bent wave zone of random noise, problem for different yardstick and different directions, it is different that the bent wave system of random noise is counted the regularity of distribution, according to the feature of each yardstick and angle-data, select optimal threshold, make the signal to noise ratio (S/N ratio) of data reach the highest, so just reached the object of using bent wave conversion to obtain optimum denoising effect; After bent ripple threshold denoising, introduce total variation minimization technique, bent wave system number is adjusted, overcome the shortcomings such as " Qu Bozhuan " pseudocurve that the bent wave conversion of independent use brings.On the whole, associating noise-removed technology is better than the effect of using separately Curvelet denoising in prior art.Make the formation data that shows truer, reliable, to further carry out stratigraphic analysis, obtain oil-containing more accurately, containing the analysis result in ore deposit.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
Fig. 2-Fig. 7 is the result that the present invention's single big gun geological data that simulation produces to forward modeling carries out random noise attenuation.
Fig. 2 does not contain the simulation list big gun geological data of random noise, Fig. 3 adds the simulation list big gun geological data of random noise, Fig. 4 is the result of the independent bent wave conversion Attenuating Random Noise of tradition, the result comparison diagram of Fig. 5 for using bent wave conversion to carry out multiple dimensioned decomposition to the single big gun geological data of the noisy simulation in Fig. 3, Fig. 6 is for using the result of Attenuating Random Noise of the present invention, and Fig. 7 does not contain the error information that the simulation list big gun geological data of random noise subtracts each other in the result after Attenuating Random Noise of the present invention and Fig. 2 in Fig. 6.
Fig. 8-Figure 12 is the result that the skew stack geological data in Yi Ge work area, exploratory area, Jiangsu oilfield northern Suzhou is processed.
Fig. 8 is original skew superposition of data, Fig. 9 is that the software Omega of geological data process field main flow is used the result after RNA random noise attenuation module Attenuating Random Noise, Figure 10 is the error information of the result data of Omega after processing after subtracting each other in skew superposition of data original in Fig. 8 and Fig. 9, Figure 11 is for using the result after random noise attenuation of the present invention, Figure 12 be skew superposition of data original in Fig. 8 with Figure 11 in result data after the present invention's processing error information after subtracting each other.
In each figure, horizontal ordinate is earthquake Taoist monastic name, and ordinate is writing time (unit is ms).
Embodiment
Embodiment 1
An associating random noise attenuation disposal route based on bent wave conversion and total variation, step is as follows:
1) obtaining of geological data:
Obtaining of simulated data: simulate single big gun geological data by wave equation forward modeling, technician provides skew superposition of data.
Obtaining of measured data: arrange shot point position and a plurality of acceptance points position, then by exciting generation seismic event at the embedding explosive charge of shot point, the seismic event that the subsurface reflective boundary that receives the wave detector that each acceptance point is laid reflects up, the seismic data of the same gun excitation that all wave detectors receive, forms single big gun data.
2) by the single big gun geological data obtaining
fmarch wave conversion
To geological data
fmarch wave conversion obtains bent wave conversion coefficient
c(j, l, k), wherein
c(j, l, k)can be expressed as:
In formula,
for
fbent wave conversion,
represent bent wave function,
represent respectively yardstick, direction and location parameter.
, wherein
mfor seismic trace number,
nbe that one time-sampling counted, yardstick
, direction
;
Simulate single big gun geological data size Wei512 road, 2048 sampled points, actual shifts superposition of data size Wei200 road, 451 sampled points, utilizing the computing formula of yardstick and direction can obtain simulating single big gun can be decomposed into
individual yardstick and
individual direction, actual shifts superposition of data can be decomposed into
individual yardstick and
individual direction.
3) multiple dimensioned bent ripple optimal threshold denoising
Fixing some yardstick layers
jall bent wave system number constant, the bent wave system number of other yardstick layers all set to 0, right
jthe whole bent wave system of yardstick layer is counted march reconstructed wave, obtains
jthe data of the independent reconstruct of yardstick layer.All yardstick layers are carried out to same operation, just obtain the data of the independent reconstruct of all yardstick layers.
Simulate single big gun geological data and after bent wave conversion, be divided into 6 yardsticks and 8 directions, skew superposition of data is divided into 5 yardsticks and 6 directions; At different yardstick layers, it is different that the bent wave system number of useful signal and random noise distributes, at low frequency coefficient layer, the bent wave system number of useful signal and random noise is obviously boundary not, therefore, at this yardstick layer, retain whole coefficients, the highest optimal threshold of signal to noise ratio (S/N ratio) after every one deck is chosen denoising separately in high frequency coefficient layer and medium-high frequency coefficient layer, to reach best denoising effect.
The acquiring method of threshold value is as follows:
wherein,
, here
length for geological data;
for the noise variance of a certain yardstick a direction, simultaneously
αvalue be no more than 2.
About simulating single big gun geological data, through test, for yardstick layer 1, retain whole bent wave system numbers, the bent wave system number of this yardstick is not carried out to threshold process; For yardstick layer 2,
αduring 2=1.0, the threshold denoising best results obtaining; For yardstick layer 3,
αduring 3=1.0, the threshold denoising best results obtaining; For yardstick layer 4,
αduring 4=1.2, the threshold denoising best results obtaining; For yardstick layer 5,
αduring 5=1.2, the threshold denoising best results obtaining; For yardstick layer 6,
αduring 6=0.8, the threshold denoising best results obtaining; For yardstick layer 7,
αduring 7=0.5, the threshold denoising best results obtaining; For yardstick layer 8, there is no useful signal, be substantially all random noise, therefore, the bent wave system number of this yardstick layer is all set to 0.
About actual shifts stack geological data, through test, for yardstick layer 1, retain whole bent wave system numbers,
; For yardstick layer 2,
αduring 2=1.2, the threshold denoising best results obtaining; For yardstick layer 3,
αduring 3=1.2, the threshold denoising best results obtaining; For yardstick layer 4,
αduring 4=0.9, the threshold denoising best results obtaining; For yardstick layer 5,
α5=0.6745 time, the threshold denoising best results obtaining.
Then, the bent wave system that each yardstick layer is carried out after optimal threshold processing is counted march reconstructed wave, for the single big gun data of simulation, is shown below:
The rest may be inferred for bent reconstructed wave after actual shifts superposition of data optimal threshold is processed, thereby obtain the geological data after the denoising of multiple dimensioned bent ripple optimal threshold
.
4) total variation denoising
In bent wave conversion optimal threshold denoising process, after given optimal threshold, also can think geological data
by being greater than and being less than optimal threshold
two parts form:
To signal
in bent wave conversion territory, do multiple dimensioned multi-direction optimal threshold and process, multiple dimensioned multidirectional optimal threshold is
, reconstruct obtains:
The denoising result obtaining thus
easily there is pseudo-gibbs oscillatory occurences, by total variation minimization technique, can carry out inhibition to a certain degree to this linearity; Here make the data after the denoising of bent ripple optimal threshold
be expressed as
, the total variation of data is as follows:
Utilization solves the minimized iterative formula of total variation and carries out total variation processing, and formula is as follows:
Wherein,
represent
the result of inferior iteration,
be
the result of inferior iteration, makes initial value
,
represent iteration step length,
represent that function of total variation exists
the subgradient at place;
Conventionally use
replace
,
and be one very little on the occasion of, its with
differ at least two orders of magnitude, to solve function of total variation at the non-differentiability of some point, get here
;
With the geological data after the denoising of adjustment optimal threshold
with bent wave system number
as input, utilize total variation minimization technique to adjust bent wave system number, establish maximum iterations and be
, initial value
,
, calculate subgradient
, get step-length
, by
this formula calculates
; Then carry out loop iteration, get
, judgement
whether close to
, be finishing iteration, otherwise continue iteration, until finishing iteration, to simulating single big gun geological data and actual shifts stack geological data all carries out iteration 30 times, thereby obtain final process result
.
4) output geological data: export geological data by Graphics Software.
Its result as shown in Fig. 2-Fig. 7,
This simulates single big gun geological data is hyperbolic model, size of data Wei512 road, 2048 sampled points, 1ms sampling, Fig. 2 is original not containing random noise data, Fig. 3's is the data that add random noise later, and the signal to noise ratio (S/N ratio) obtaining is 0.28, and the signal to noise ratio (S/N ratio) here represents with the ratio of the quadratic sum of signal amplitude and the quadratic sum of noise amplitude.Fig. 4 is that signal to noise ratio (S/N ratio) is 1.2 by the result of traditional bent wave conversion method test.Fig. 5 is the geological data under six yardsticks after noisy geological data march wave conversion.Fig. 6 is the result after the associating denoising method denoising based on bent wave conversion and total variation, during bent wave conversion, concrete threshold value is chosen as previously mentioned, the iterations of total variation is 30 times, after denoising of the present invention, the signal to noise ratio (S/N ratio) of section reaches 4.2, Fig. 7 is the error section of original not noisy data in result after denoising of the present invention and Fig. 2, the present invention is based on as we can see from the figure the associating noise-removed technology of bent wave conversion and total variation, be far superior to traditional method based on bent wave conversion Attenuating Random Noise, the present invention has not only well suppressed random noise, and protected preferably useful signal.
Embodiment 2
As Fig. 8-Figure 12, the result of processing for the skew stack geological data in Yi Ge work area, exploratory area, Jiangsu oilfield northern Suzhou.
Difference from Example 1 is, step 2) in, utilize superposition of data march wave conversion, the acquisition methods of its superposition of data is: a plurality of single big gun data of obtaining are carried out respectively to ground roll and anomalous amplitude decay, earth's surface-consistent energy compensating, surface consistent deconvolution, earth's surface-consistent residual static correction, then be sorted into common midpoint data field, carry out speed explanation, by the speed of explaining, carry out normal moveout correction, data after normal moveout correction are superposeed in common midpoint territory, obtain superposition of data.All the other steps are identical with disposal route, and only design parameter value is different.
After processing, result is as follows:
Fig. 8 is original offset superposition of data, size of data Wei200 road, and 451 sampled points, sampling interval is 1ms, the RNA random noise attenuation module of using the bent wave conversion of the present invention's proposition and the software Omega of total variation associating denoising method and geological data process field main flow to carry compares, wherein, Fig. 9 is the result after the RNA random noise attenuation module denoising that carries of Omega, Figure 10 is the error information of the data after RNA denoising and the original offset superposition of data in Fig. 8 in Fig. 9, this area data mature fault as we can see from the figure, existence due to random noise, affected the quality of data, after RNA denoising, random noise in original section has obtained good compacting, the signal to noise ratio (S/N ratio) of section is significantly improved, the breakpoint of section has obtained good reservation simultaneously.But in structural complex, useful signal is subject to certain infringement, in error section, can obviously see the existence of useful signal, simultaneously in weak signal region, deep, to the fidelity relative mistake of useful signal a bit.Figure 11 is the result of using after denoising of the present invention, Figure 12 is the error information of the data after denoising of the present invention and the original offset superposition of data in Fig. 8 in Figure 11, from denoising, in section and error section, can see, the method of using the present invention to propose, random noise has not only obtained good compacting, useful signal in structural complex has also obtained good fidelity, deep weak signal has also obtained good reservation simultaneously, therefore the method that the present invention proposes can be good at processing the lineups of steep dip, is a kind of denoising method of high-fidelity.
The present invention is not limited to above-described embodiment; on the basis of technical scheme disclosed by the invention; those skilled in the art is according to disclosed technology contents; do not need performing creative labour just can make some replacements and distortion to some technical characterictics wherein, these replacements and distortion are all in protection scope of the present invention.For example, Graphics Software can be the figure show tools in Omega process software, the figure show tools in Cgg process software, figure show tools in Promax process software, figure show tools in Focus process software, figure show tools in Geoeast process software, figure show tools in Landmark interpretation software, the figure show tools in Geoframe interpretation software, a kind of in the wigb program in Matlab.
Claims (3)
1. the associating random noise attenuation disposal route based on bent wave conversion and total variation, is characterized in that in turn including the following steps:
1) obtaining of geological data:
First obtain single big gun data: arrange shot point position and a plurality of acceptance points position, then by exciting generation seismic event at the embedding explosive charge of shot point, the seismic event that the subsurface reflective boundary that receives the wave detector that each acceptance point is laid reflects up, the seismic data of the same gun excitation that all wave detectors receive, forms single big gun data;
2) by the stack march wave conversion of single big gun data of obtaining or single big gun data
To superposition of data or single big gun data
march wave conversion obtains bent wave conversion coefficient
, wherein
can be expressed as:
In formula,
for
bent wave conversion,
represent bent wave function,
represent respectively yardstick, direction and location parameter;
, wherein
for seismic trace number,
be that one time-sampling counted, yardstick
, direction
;
3)multiple dimensioned bent ripple threshold denoising
Data, after bent wave conversion, are divided into j yardstick layer, and each yardstick layer all comprises multiple directions data, and innermost layer is called low frequency coefficient layer; Outermost layer is called high frequency coefficient layer; Middle yardstick layer is called medium-high frequency coefficient layer; Low frequency coefficient layer retains whole coefficients, and in high frequency coefficient layer and medium-high frequency coefficient layer, every one deck is chosen the optimal threshold that signal to noise ratio (S/N ratio) is the highest separately;
Threshold value acquiring method is as follows:
Wherein,
, here
length for geological data;
noise variance for a certain yardstick a direction;
Then, all bent wave system number and the threshold value to a direction under a certain yardstick
t α carry out size relatively, retain and be not less than threshold value
t α bent wave system number, then the bent wave system number retaining is reconstructed to (also claiming bent ripple inverse transformation), be shown below:
represent bent ripple inverse transformation,
represent through threshold value
bent wave system number after processing,
represent bent wave function;
Conversion
value, obtains different
, to all
carry out Analysis signal-to-noise ratio (SNR), obtain different signal to noise ratio (S/N ratio)s
,
for
corresponding signal to noise ratio (S/N ratio) numerical value, makes
be worth the highest
be worth corresponding threshold value
value is the downward optimal threshold of this yardstick the party;
4) total variation denoising
Signal
by being greater than and being less than optimal threshold
two parts form:
To signal
in bent wave conversion territory, do multiple dimensioned multi-direction threshold process, multiple dimensioned multidirectional optimal threshold is
, reconstruct obtains:
The denoising result obtaining thus
easily there is pseudo-gibbs oscillatory occurences, by total variation minimization technique, can carry out inhibition to a certain degree to this phenomenon; Here make the data after bent ripple threshold denoising is processed
be expressed as
, the total variation of data is as follows:
Wherein
represent data
gradient,
for gradient represents,
represent data
total variation,
for data
support Interval,
coordinate vector for data;
gradient fields be:
Wherein
for data
?
the numerical value of point, the rest may be inferred for other;
Denoising method based on total variation can realize by the function minimizing below:
Wherein, first makes the image after denoising still can approach preferably original image for approaching item, has certain fidelity; Second is total variation regularization term, and λ is Lagrangian constant;
Above-mentioned objective function
be
convex function, it exists the sufficient and necessary condition of extreme value to be
, can obtain thus its corresponding Euler-Largrange equation and be:
This equation is non-linear, in formula
for divergence, suppose that equation meets Neumann boundary condition, by gradient descent method, data are iterated until obtain a stable solution, thereby obtain the data after denoising, its iterative formula is as follows:
Wherein,
represent
the result of inferior iteration,
be
the result of inferior iteration, makes initial value
,
represent iteration step length,
represent that function of total variation exists
the subgradient at place;
With
replace
, get
and be one very little on the occasion of, its with
differ at least two orders of magnitude;
With the geological data after optimal threshold denoising and bent wave system number, as input, utilize total variation minimization technique to adjust bent wave system number, establish maximum iterations, initial value
,
, calculate subgradient
, get step-length
, calculate
; Get
, judgement
whether equaling maximum iterations, is finishing iteration, otherwise continues iteration, until finishing iteration;
5) output geological data: export geological data by Graphics Software.
2. a kind of associating random noise attenuation disposal route based on bent wave conversion and total variation according to claim 1, it is characterized in that step 2) in the acquisition methods of superposition of data be: a plurality of single big gun data of obtaining are carried out respectively to ground roll and anomalous amplitude decay, earth's surface-consistent energy compensating, surface consistent deconvolution, earth's surface-consistent residual static correction, then be sorted into common midpoint data field, carry out speed explanation, by the speed of explaining, carry out normal moveout correction, data after normal moveout correction are superposeed in common midpoint territory, obtain superposition of data.
3. a kind of associating random noise attenuation disposal route based on bent wave conversion and total variation according to claim 1 and 2, it is characterized in that in step 5), Graphics Software comprises the figure show tools in Omega process software, figure show tools in Cgg process software, figure show tools in Promax process software, figure show tools in Focus process software, figure show tools in Geoeast process software, figure show tools in Landmark interpretation software, figure show tools in Geoframe interpretation software, a kind of in wigb program in Matlab.
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| CN112147692A (en) * | 2019-06-28 | 2020-12-29 | 中国石油化工股份有限公司 | Secondary seismic source attenuation method for marine seismic data |
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| CN111273351A (en) * | 2019-11-21 | 2020-06-12 | 西安工业大学 | Structural guide direction generalized total variation regularization method for seismic data denoising |
| CN111273351B (en) * | 2019-11-21 | 2022-04-08 | 西安工业大学 | Structural guide direction generalized total variation regularization method for seismic data denoising |
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