CN110441822A - A kind of seismic data noise attenuation method, apparatus, electronic equipment and storage medium - Google Patents
A kind of seismic data noise attenuation method, apparatus, electronic equipment and storage medium Download PDFInfo
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Abstract
It includes: acquisition seismic observation data that the application, which provides a kind of seismic data noise attenuation method, apparatus, electronic equipment and storage medium, method,；Sparse inversion model is constructed according to the seismic observation data；Regular parameter sequence is constructed, regular parameter is positive number in the regular parameter sequence, and is gradually become smaller from first regular parameter；According to the regular parameter sequence, the sparse inversion model is solved using iteration method, obtains the corresponding solution of regular parameter, the regular parameter is corresponding to be solved as the seismic data after denoising；Regular parameter and the corresponding solution of the regular parameter are substituted into evaluation function, determine the extreme value of evaluation function；Determine that being used to calculate the corresponding solution of the regular parameter for obtaining the extreme value is the seismic data after final denoising.As a result, when denoising to different seismic datas, it is not necessarily to artificial selection threshold parameter, meanwhile, also improve the working efficiency of seismic data noise attenuation.
Description
Technical field
This application involves seismic data process fields, in particular to a kind of seismic data noise attenuation method, apparatus, electricity
Sub- equipment and storage medium.
Background technique
Seismic data can inevitably generate various noises during acquisition, processing etc., and the presence of noise can reduce
The signal-to-noise ratio of seismic data, so that the result of the crucial processing such as migration imaging, full waveform inversion is affected, therefore seismic data
Noise-removed technology is the important means of seismic data process.Researcher is continuous to the research enthusiasm of seismic data noise attenuation all the time,
And propose various denoising methods.
Traditional seismic data noise attenuation method since seismic data can be separated in transform domain with random noise, wherein
Shake data have a sparse expression and biggish coefficient in transform domain, and noise in the transform domain as illustrated show as dense distribution, coefficient
Amplitude is smaller, can be with Removing Random No by threshold operation, the knot that is then denoised by inverse transformation to time spatial domain
Fruit.But traditional seismic data noise attenuation method needs artificial adjusting threshold parameter, could be directed to different seismic datas and obtain
Reliable denoising effect.
Summary of the invention
In view of this, the embodiment of the present application is designed to provide a kind of seismic data noise attenuation method, apparatus, electronic equipment
And storage medium, it needs artificially to adjust threshold parameter for different seismic datas to improve traditional seismic data noise attenuation method
The problem of.
In a first aspect, the embodiment of the present application provides a kind of seismic data noise attenuation method, comprising: obtain seismological observation number
According to；Sparse inversion model is constructed according to the seismic observation data；Regular parameter sequence is constructed, in the regular parameter sequence just
Then parameter is positive number, and is gradually become smaller from first regular parameter；According to the regular parameter sequence, asked using iteration method
The sparse inversion model is solved, the corresponding solution of regular parameter is obtained, the regular parameter is corresponding to be solved as the earthquake number after denoising
According to；Regular parameter and the corresponding solution of the regular parameter are substituted into evaluation function, determine the extreme value of evaluation function；Determination is used for
Calculating and obtaining the corresponding solution of the regular parameter of the extreme value is the seismic data after final denoising.
The embodiment of the present application is by setting evaluation function come to multiple regular parameters and corresponding sparse inversion model
Solution is evaluated, and determines the seismic data after final denoising by determining the extreme value of evaluation function.As a result, to different
When seismic data is denoised, it is not necessarily to artificial selection threshold parameter, meanwhile, also improve the working efficiency of seismic data noise attenuation.
Further, the evaluation function characterizes the corresponding solution of the regular parameter and the difference of the seismic observation data
Difference between norm, with the regular parameter.
The embodiment of the present application is by setting evaluation function come to multiple sparse inversion solution to model and seismic observation data
Difference and the difference of corresponding regular parameter are evaluated, and allow and final go is determined more accurately by evaluation function
Seismic data after making an uproar.
Further, the evaluation function are as follows:
Wherein, PiFor the value of evaluation function corresponding with i-th of regular parameter；λiFor i-th of regular parameter；diFor with i-th
The corresponding solution of a regular parameter；dobsFor the seismic observation data；The extreme value of the determining evaluation function, comprising: determine evaluation
The maximum of function.
The embodiment of the present application is by setting evaluation function come to multiple regular parameters and corresponding sparse inversion model
Solution is evaluated, and determines the seismic data after final denoising by determining the maximum of evaluation function as a result,.So that right
When different seismic datas is denoised, it is not necessarily to artificial selection threshold parameter, meanwhile, also improve the work of seismic data noise attenuation
Efficiency.
Further, the construction regular parameter sequence, comprising: rarefaction is carried out to the seismic observation data, according to
The seismic observation data of rarefaction determines the value of first regular parameter in the regular parameter sequence.
The embodiment of the present application can determine the value of first regular parameter according to the seismic observation data of rarefaction, so that root
According to the regular parameter sequence of building, more rapidly iteration the seismic data after final denoising can be obtained.
Further, the value of the first regular parameter is the maximum value of the seismic observation data of the rarefaction
Presupposition multiple.
It is absolute for the maximum of the seismic observation data of rarefaction that the embodiment of the present application passes through the value of clearly first regular parameter
The presupposition multiple of value can also more rapidly iteration obtain finally it is possible thereby to construct more reasonable regular parameter sequence
Denoising after seismic data.
Further, described according to the regular parameter sequence, the sparse inversion model is solved using iteration method,
Obtain the corresponding solution of regular parameter, comprising: threshold denoising is carried out according to seismic observation data of the regular parameter to the rarefaction
Processing, the seismic data after obtaining the denoising of rarefaction corresponding with the regular parameter；To the ground after the denoising of the rarefaction
It shakes data and carries out sparse inverse transformation, the seismic data after obtaining the denoising；Wherein, the numerical value of the regular parameter is gone with threshold value
The numerical value for the threshold parameter made an uproar in handling is equal.
Property of the embodiment of the present application based on seismic observation data in transform domain, earthquake according to regular parameter to rarefaction
It observes data and carries out threshold denoising processing, then by the seismic data after the available denoising of inverse transformation, so that iteration threshold can
Solve sparse inversion model, the seismic data after can more quickly and accurately being denoised.
Further, described that sparse inversion model is constructed according to the seismic observation data, comprising: to be based on warp wavelet pair
The seismic observation data constructs sparse inversion model；The sparse inversion model are as follows:
Wherein, xλFor the corresponding solution of regular parameter；λ is the regular parameter；CHFor bent wave inverse transformation；X is the ground of rarefaction
Shake data；dobsFor the seismic observation data.
The embodiment of the present application constructs sparse inversion model to seismic observation data based on warp wavelet, so as to it is subsequent can be with
Sparse inversion solution to model is sought by successive ignition regular parameter, that is, the seismic data after denoising.
Second aspect, the embodiment of the present application provide a kind of seismic data noise attenuation device, comprising: module are obtained, for obtaining
Obtain seismic observation data；Model module is constructed, for constructing sparse inversion model according to the seismic observation data；Constructing variable
Module, for constructing regular parameter sequence, regular parameter is positive number in the regular parameter sequence, and from first regular parameter
It gradually becomes smaller；Solving model module, for solving the sparse inversion using iteration method according to the regular parameter sequence
Model obtains the corresponding solution of regular parameter, and the regular parameter is corresponding to be solved as the seismic data after denoising；Evaluation module is used
In each regular parameter and the corresponding solution of the regular parameter are substituted into evaluation function, the extreme value of evaluation function is determined；Finally
Processing module, for determining that being used to calculate the corresponding solution of the regular parameter for obtaining the extreme value is the ground after final denoising
Shake data.
The embodiment of the present application is by being arranged evaluation function in evaluation model come to multiple regular parameters and corresponding dilute
The solution for dredging inverse model is evaluated, and determines the seismic data after final denoising by determining the extreme value of evaluation function.By
This is not necessarily to artificial selection threshold parameter when denoising to different seismic datas, meanwhile, it also improves seismic data and goes
The working efficiency made an uproar.
The third aspect, the embodiment of the present application provide a kind of electronic equipment, comprising: processor, memory and bus,
In, the processor and the memory complete mutual communication by the bus；The memory is stored with can be by institute
The program instruction of processor execution is stated, the processor calls described program instruction to be able to carry out such as above-mentioned method.
Fourth aspect, the embodiment of the present application provide a kind of non-transient computer readable storage medium, the non-transient meter
Calculation machine readable storage medium storing program for executing stores computer instruction, and the computer instruction makes the computer execute such as above-mentioned method.
Other feature and advantage of the application will be illustrated in subsequent specification, also, partly be become from specification
It is clear that by implementing the embodiment of the present application understanding.The purpose of the application and other advantages can be by written theorys
Specifically noted structure is achieved and obtained in bright book, claims and attached drawing.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application will make below to required in the embodiment of the present application
Attached drawing is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore should not be seen
Work is the restriction to range, for those of ordinary skill in the art, without creative efforts, can be with
Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of flow diagram of seismic data noise attenuation method provided by the embodiments of the present application；
Fig. 2 be one not Noise simulation seismic data schematic diagram；
Fig. 3 is the schematic diagram of the simulation seismic data of an increase noise；
Fig. 4 is a kind of numerical value change trend schematic diagram of evaluation function provided by the embodiments of the present application；
Fig. 5 is the signal-to-noise ratio variation tendency schematic diagram of the seismic data after a kind of denoising provided by the embodiments of the present application；
Fig. 6 is the schematic diagram of the simulation seismic data after denoising；
Fig. 7 is a kind of schematic diagram of true seismic data；
Fig. 8 is a kind of schematic diagram of the true seismic data after denoising；
Fig. 9 is a kind of schematic diagram of the noise data of removal；
Figure 10 is a kind of structural schematic diagram of seismic data noise attenuation device provided by the embodiments of the present application；
Figure 11 is a kind of structural block diagram that can be applied to the electronic equipment in the embodiment of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application is described.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile the application's
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
Often with noise problem in seismic data process.Since the data of field of seismic exploration are mainly adopted by field
The mode of collection is obtained, and observation condition is uncontrollable, therefore, can generate many noises.For seismic survey data, make an uproar
The presence of sound can reduce the signal-to-noise ratio of seismic data, so that the result of the crucial processing such as migration imaging, full waveform inversion is by shadow
It rings, therefore seismic data noise attenuation technology is the important means of seismic data process.
Meanwhile the existing method denoised for seismic data, median filtering class method is usually used, based on one-dimensional
The denoising method of Fourier transformation, based on two-dimentional Fourier's denoising method, Singular Value Decomposition method, Karhunen-Loeve transformation method
(Karhunen-Loeve, K-L), Empirical mode decomposition, the method based on independent component analysis, Cadzow filtering method, base
In the method for wavelet transformation.
But traditional denoising method based on sparse transformation and threshold process, need artificial selection transform domain to carry out threshold
It is worth the threshold parameter size of denoising.When needing to carry out a large amount of earthquake observed numbers according to being denoised, need to expend a large amount of
Manpower and time, efficiency are lower.
Fig. 1 is a kind of flow diagram of seismic data noise attenuation method provided by the embodiments of the present application, the embodiment of the present application
Provide a kind of seismic data noise attenuation method, comprising:
Step 110: obtaining seismic observation data.
During the application optional embodiment, it can be got by seismic instrument with noisy original earthquake number
According to.
Wherein, seismological observation refers to the seismic waveform caused by the earthquake of earthquake instrument record or secondary reflection, and
Thereby determine that the basic parameter of earthquake or explosive incident, such as the origin time of earthquake, earthquake centre longitude and latitude, the depth of focus and earthquake magnitude.Meanwhile
Can carry out Reconnaissance Survey by seismic instrument, to obtain seismic observation data, seismic instrument can for wired U system, in real time
Wireless instruments RT2, node instrument UNITE, HWAK etc., the type of seismic instrument can seismic observation datas according to actual needs
Type and precision selected.
Step 120: according to seismological observation data configuration sparse inversion model.
In the optional implementation process of the application, can it become according to the property of seismic observation data in the transform domain as illustrated
Changing seismic data in domain has different properties from noise data, and seismic data has a sparse expression in the transform domain as illustrated, and noise
Data in the transform domain as illustrated show as dense distribution, coefficient amplitude is smaller.It therefore, can to the Denoising Problems of seismic observation data
To be converted to the problem of solving sparse inversion model.
It is worth noting that step 120, comprising: construct sparse inversion model to seismic observation data based on warp wavelet；
Sparse inversion model can be with are as follows:
Wherein, xλFor the corresponding solution of regular parameter；λ is regular parameter；CHFor bent wave inverse transformation；X is the earthquake number of rarefaction
According to；dobsFor seismic observation data.
In the optional implementation process of the application, construction sparse inversion model can be realized by warp wavelet, wherein
Warp wavelet (Curvelet transformation) is a kind of multiple dimensioned set transform algorithm, can be three-dimensional or two dimension.
As an example it is assumed that seismic observation data is dobs, effective seismic data is d, and noise data ε then has: dobs=
d+ε.According to seismic observation data in the sparsity of bent wave zone, analyzes and know: d=CHX, wherein CHIndicate bent wave inverse transformation, x is
It is capable of the warp wavelet coefficient of rarefaction representation seismic data seismic phase information.L1 norm constraint condition is recycled, it can will be to earthquake
The Denoising Problems for observing data can be converted to the problem of solving sparse inversion model, also available above-mentioned sparse inversion mould
Type.
Step 130: constructing regular parameter sequence, regular parameter is positive number in regular parameter sequence, and is joined from first canonical
Several gradually become smaller.
Step 140: according to regular parameter sequence, sparse inversion model being solved using iteration method, obtains regular parameter
Corresponding solution, the corresponding solution of regular parameter is the seismic data after denoising.
In the optional implementation process of the application, regular parameter is to solve for the key factor of sparse inversion model, because of canonical
Parameter is acting the weight relationship adjusted between two in sparse inversion model, the preset quantity according to rule variation is arranged as a result,
Regular parameter sequence so that it is subsequent can be directed to a regular parameter, above-mentioned sparse inversion is solved by Threshold denoising
Model, the seismic data after obtaining the corresponding solution of the regular parameter, namely denoising.
Step 150: the corresponding solution of regular parameter and regular parameter being substituted into evaluation function, determines the pole of evaluation function
Value.
In a kind of optional embodiment of the application, can based on the rule of regular parameter in regular parameter sequence, for
One regular parameter can bring regular parameter solution corresponding with the regular parameter into evaluation function, determine that the canonical is joined
The value of the corresponding evaluation function of number.It repeats and solves the corresponding solution of next regular parameter in regular parameter sequence, then determine canonical
The value of the corresponding evaluation function of parameter.Extreme value until determining evaluation function stops repeating step.
In the application an alternative embodiment, it can be directed to each regular parameter in regular parameter sequence, according to
The regular parameter solves sparse inversion model, obtains the corresponding solution of the regular parameter.And by each regular parameter and the canonical
The corresponding solution of parameter brings evaluation function into, determines the value of the corresponding evaluation function of the regular parameter.According to regular parameter sequence
The value of the corresponding evaluation function of all regular parameters of middle preset number, can determine the extreme value of evaluation function.
It is worth noting that the norm of evaluation function characterization regular parameter corresponding solution and the difference of seismic observation data, with
Difference between regular parameter.The embodiment of the present application is by setting evaluation function come to multiple sparse inversion solution to model and earthquake
The difference of the difference and corresponding regular parameter of observing data is evaluated, allow by evaluation function more it is accurate really
Seismic data after fixed final denoising.
It should also be noted that, evaluation function can be with are as follows:
Wherein, PiFor the value of evaluation function corresponding with i-th of regular parameter；λiFor i-th of regular parameter；diFor with i-th
The corresponding solution of a regular parameter, i.e., the seismic data after denoising corresponding with i-th of regular parameter；dobsFor seismic observation data.
Meanwhile determining the extreme value of evaluation function, comprising: determine the maximum of evaluation function.I.e. when will decline according to numerical value
The regular parameter sequence of rule setting, after iteration determines the corresponding solution of regular parameter, and regular parameter is brought into corresponding solution
After evaluation function.During iterative solution, the value of evaluation function is in downward trend after rising, accordingly, it is determined that evaluation function
Extreme value, that is, determine evaluation function maximum.
For example, regular parameter sequence may include n regular parameter, and step 150 specifically includes: by i-th of canonical
Parameter and the corresponding solution of i-th of regular parameter bring evaluation function into, obtain the value P of evaluation functioni, wherein n be more than or equal to
3 positive integer；I is the positive integer more than or equal to 3 and less than or equal to n；Work as PiLess than Pi-1, and Pi-1Greater than Pi-2When, determine Pi-1
For the maximum of evaluation function.
It should also be noted that, evaluation function can also ratio between regular parameter and norm.The shape of evaluation function
Formula does not limit, and can be adjusted according to actual demand.At the same time it can also be commented according to the variation tendency of evaluation function to determine
The method of determination of valence extreme value of a function.
Step 160: determining that the corresponding solution of the regular parameter for calculating acquisition extreme value is the earthquake number after final denoising
According to.
In the optional implementation process of the application, after the extreme value of evaluation function has been determined, it can determine and calculate the extreme value
Corresponding regular parameter can determine that regular parameter corresponds to the solution of sparse inversion function according to the regular parameter, i.e., final goes
Seismic data after making an uproar.The embodiment of the present application is by setting evaluation function come to multiple regular parameters and corresponding dilute as a result,
The solution for dredging inverse model is evaluated, and determines the seismic data after final denoising by determining the extreme value of evaluation function.Make
It obtains when being denoised to different seismic datas, is not necessarily to artificial selection threshold parameter, meanwhile, also improve seismic data noise attenuation
Working efficiency.
On the basis of the above embodiments, step 130, comprising: rarefaction is carried out to seismic observation data, according to rarefaction
Seismic observation data determine the value of first regular parameter in regular parameter sequence.
In the optional implementation process of the application, since regular parameter sequence gradually becomes smaller from first regular parameter,
It can be by presetting the value of the first regular parameter of regular parameter sequence, to determine the range of regular parameter.Pass through by
Seismic observation data carries out rarefaction, and the value of first regular parameter is determined further according to the seismic observation data of rarefaction.As a result,
So that more rapidly iteration the ground after final denoising can be obtained according to the regular parameter sequence that first regular parameter constructs
Shake data.
It is worth noting that the value of first regular parameter is the default of the maximum value of the seismic observation data of rarefaction
Multiple.
In the optional implementation process of the application, seismic observation data be used for indicates measure obtain with noisy earthquake
Data.And the first regular parameter chosen is default times of the coefficient maximum value of absolute value of the observation data after warp wavelet
Number.
It should be noted that the value of first regular parameter does not limit, it can be the maximum of the seismic observation data of rarefaction
The half of absolute value, or 2/3rds of the maximum value of the seismic observation data of rarefaction, specific canonical
The value of parameter can be defined according to actual needs.
It should also be noted that, the downward trend of regular parameter sequence can be according to index decreased formula or linear decline
Formula is declined, which can be with are as follows:Linear decline formula can be with are as follows:Wherein, λiFor i-th of regular parameter, λ1For first regular parameter, λNFor regular parameter sequence
The last one regular parameter in column, N are the preset number of regular parameter.
On the basis of the above embodiments, step 140 includes: the seismological observation number according to regular parameter to the rarefaction
Seismic data according to progress threshold denoising processing, after obtaining the denoising of rarefaction corresponding with the regular parameter；To described sparse
Seismic data after the denoising of change carries out sparse inverse transformation, the seismic data after obtaining the denoising.
In a kind of optional implementation process of the application, sparse inversion solution to model is solved using Threshold Denoising Method, is had
Body can be with are as follows: for different regular parameters, denoises to the seismic data of rarefaction, the earthquake after obtaining sparse denoising
Data.Sparse inverse transformation is carried out to the seismic data after the sparse denoising again, the seismic data after obtaining corresponding denoising.
In optionally another embodiment of the application, threshold denoising model can use to regular parameter and seismological observation
Data are calculated, the seismic data after being denoised.The threshold denoising model can be with are as follows:
Wherein, C (dobs) it is the seismic observation data that rarefaction is carried out using warp wavelet；For with λiIt is corresponding
To x carry out threshold denoising processing threshold function table；CHFor the bent wave inverse transformation to coefficient.
It is worth noting that the numerical value of regular parameter can be equal with the numerical value of threshold parameter in threshold denoising processing,
And above-mentioned threshold function table can be hard threshold function, hard threshold functionIt can be with are as follows:
Meanwhile above-mentioned threshold function table can also be soft-threshold function, soft-threshold functionIt can be with are as follows:
It should be noted that the type of threshold function table can there are many, specific type can be gone according to actual threshold value
The demand of making an uproar is selected.
In addition, the example that the simulation seismic data for increasing noise is denoised with one below, to above-mentioned earthquake number
It is illustrated according to denoising method.Fig. 2 be one not Noise simulation seismic data schematic diagram, size of data 726*96,
Time shaft has 726 sampled points, and time sampling interval 4ms, Fig. 3 are the signal of the simulation seismic data of an increase noise
Figure, mainly to the simulation seismic data increase noise in Fig. 2 as a result, signal-to-noise ratio is 3.0625dB.
Meanwhile Fig. 4 is a kind of numerical value change trend schematic diagram of evaluation function provided by the embodiments of the present application, Fig. 5 is this
The signal-to-noise ratio variation tendency schematic diagram of seismic data after applying for a kind of denoising that embodiment provides, it is assumed that have a regular parameter sequence
Column have N number of regular parameter wherein first regular parameter is 1.6527, and the smallest regular parameter is 0.00001, then utilize canonical
Parameter executes above-mentioned seismic data noise attenuation method to the above-mentioned simulation seismic data for increasing noise, obtains multiple evaluation functions
Value Pi, Pi=[25.0289,26.8234,28.3331,29.9828,31.7766,33.5988,35.4825,37.6749,
39.8707,42.2941,45.0143,48.0318,51.2901,54.8934,58.8560,63.0216,67.1712,
70.0422,69.9571,66.1061,58.9309,50.0351,40.7125,32.3754,25.4270,19.9215,
15.7435,12.6168,10.2294,8.3864,6.9297,5.7695,4.8146,4.0437,3.3858,2.8437,
2.3894,2.0115,1.6873,1.4111,1.1876,1.0067,0.8458,0.7040,0.5929,0.5041,0.4204,
0.3537,0.2978,0.2529,0.2120,0.1778,0.1465,0.1253,0.1032,0.0872,0.0736,0.0605,
0.0494,0.0426,0.0362,0.0314,0.0258,0.0234,0.0185,0.0164,0.0130,0.0112,0.0092,
0.0076].It can be seen that according to above-mentioned numerical value as i=18, PiNumerical value be maximum.It as can be seen from Figure 5 goes as a result,
The signal-to-noise ratio of seismic data after the making an uproar equally signal-to-noise ratio highest in i=18 when i.e. i=18, can obtain best denoising effect
Fruit.Also, Fig. 6 is the schematic diagram of the simulation seismic data after denoising, show in Fig. 6 it is a kind of to the simulation for increasing noise
Shake the simulation seismic data after data are denoised.
Meanwhile the example that true seismic data is denoised with one below, to above-mentioned seismic data noise attenuation method into
Row explanation.For the true seismic data of a 276*400, Temporal sampling 4ms, time sampling number is 276, space
Shang You 400, original Noise Data is as shown in fig. 7, Fig. 7 is a kind of schematic diagram of true seismic data；Utilize the application reality
It applies the denoising result that example is denoised and sees that Fig. 8, Fig. 8 are a kind of schematic diagram of the true seismic data after denoising, the noise of removal
See that Fig. 9, Fig. 9 are a kind of schematic diagram of the noise data of removal, it can be seen that the embodiment of the present application can be realized automatic denoising.
Figure 10 is a kind of structural schematic diagram of seismic data noise attenuation device provided by the embodiments of the present application；Based on same invention
Conceive, also provide a kind of seismic data noise attenuation device 1000 in the embodiment of the present application, comprising: module 1010 is obtained, for obtaining
Seismic observation data；Model module 1020 is constructed, for constructing sparse inversion model according to the seismic observation data；Construction ginseng
Digital-to-analogue block 1030, for constructing regular parameter sequence, regular parameter is positive number in the regular parameter sequence, and from first canonical
Parameter rises and gradually becomes smaller；Solving model module 1040, for solving institute using iteration method according to the regular parameter sequence
Sparse inversion model is stated, the corresponding solution of regular parameter is obtained, the regular parameter is corresponding to be solved as the seismic data after denoising；It comments
Valence module 1050 determines evaluation letter for each regular parameter and the corresponding solution of the regular parameter to be substituted into evaluation function
Several extreme values；Final process module 1060, for determining for calculating the corresponding solution of the regular parameter for obtaining the extreme value
For the seismic data after final denoising.
On the basis of the above embodiments, the evaluation function characterizes the corresponding solution of the regular parameter and sees with the earthquake
Difference between the norm of the difference of measured data, with the regular parameter.
On the basis of the above embodiments, the evaluation function are as follows:
Wherein, PiFor the value of evaluation function corresponding with i-th of regular parameter；λiFor i-th of regular parameter；diFor with i-th
The corresponding solution of a regular parameter；dobsFor the seismic observation data；The evaluation module 1050 is specifically used for: determining evaluation letter
Several maximum.
On the basis of the above embodiments, the constructing variable module 1030 is specifically used for: to the seismic observation data
Rarefaction is carried out, the value of first regular parameter in the regular parameter sequence is determined according to the seismic observation data of rarefaction.
On the basis of the above embodiments, the value of the first regular parameter is the seismic observation data of the rarefaction
The half of maximum value.
On the basis of the above embodiments, the solution module is specifically used for: according to regular parameter to the rarefaction
Seismic observation data carries out threshold denoising processing, the seismic data after obtaining the denoising of rarefaction corresponding with the regular parameter；
Sparse inverse transformation is carried out to the seismic data after the denoising of the rarefaction, the seismic data after obtaining the denoising.
On the basis of the above embodiments, the number of the numerical value of the regular parameter and the threshold parameter in threshold denoising processing
It is worth equal.
Based on any of the above embodiments, the building model module 1020 is specifically used for: being based on warp wavelet pair
The seismic observation data constructs sparse inversion model；The sparse inversion model are as follows:
Wherein, xλFor the corresponding solution of regular parameter；λ is the regular parameter；CHFor bent wave inverse transformation；X is the ground of rarefaction
Shake data；dobsFor the seismic observation data.
Seismic data noise attenuation device 1000 provided by the embodiments of the present application is for executing the above method, specific embodiment party
Formula is consistent with the embodiment of method, and details are not described herein again.
Figure 11 is please referred to, Figure 11 shows a kind of structural frames of electronic equipment 10 that can be applied in the embodiment of the present application
Figure.Electronic equipment 10 may include memory 101, storage control 102, processor 103, Peripheral Interface 104, input and output list
First 105, display unit 107.
The memory 101, processor 103, Peripheral Interface 104, input-output unit 105, is shown storage control 102
Show that each element of unit 107 is directly or indirectly electrically connected between each other, to realize the transmission or interaction of data.For example, these
Element can be realized by one or more communication bus or signal wire be electrically connected between each other.At least one software or firmware
(firmware) software for being stored in the memory 101 or being solidificated in operating system (operating system, OS)
Functional module.The processor 103 is for executing the executable module stored in memory 101, software function module or calculating
Machine program.
Wherein, memory 101 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only
Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM),
Electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..
Wherein, memory 101 is for storing program, and the processor 103 executes described program after receiving and executing instruction, aforementioned
Method performed by the server that the stream process that the embodiment of the present application any embodiment discloses defines can be applied to processor 103
In, or realized by processor 103.
Processor 103 can be a kind of IC chip, the processing capacity with signal.Above-mentioned processor 103 can
To be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network processing unit
(Network Processor, abbreviation NP) etc.；Can also be digital signal processor (DSP), specific integrated circuit (ASIC),
Ready-made programmable gate array (FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hard
Part component.It may be implemented or execute disclosed each method, step and the logic diagram in the embodiment of the present application.General processor
It can be microprocessor or the processor 103 be also possible to any conventional processor etc..
Various input/output devices are couple processor 103 and memory 101 by the Peripheral Interface 104.Some
In embodiment, Peripheral Interface 104, processor 103 and storage control 102 can be realized in one single chip.Other one
In a little examples, they can be realized by independent chip respectively.
Input-output unit 105 realizes user and the server (or local terminal) for being supplied to user input data
Interaction.The input-output unit 105 may be, but not limited to, mouse and keyboard etc..
Display unit 107 provides an interactive interface (such as user's operation circle between the electronic equipment 10 and user
Face) or for display image data give user reference.In the present embodiment, the display unit 107 can be liquid crystal display
Or touch control display.It can be the capacitance type touch control screen or resistance of support single-point and multi-point touch operation if touch control display
Formula touch screen etc..Single-point and multi-point touch operation is supported to refer to that touch control display can sense on the touch control display one
Or at multiple positions simultaneously generate touch control operation, and the touch control operation that this is sensed transfer to processor 103 carry out calculate and
Processing.
It is appreciated that Figure 11 shown in structure be only illustrate, the electronic equipment 10 may also include than shown in Figure 11 more
More perhaps less component or with the configuration different from shown in Figure 11.Each component shown in Figure 11 can using hardware,
Software or combinations thereof is realized.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description
Specific work process, no longer can excessively be repeated herein with reference to the corresponding process in preceding method.
It is situated between in conclusion the embodiment of the present application provides a kind of seismic data noise attenuation method, apparatus, electronic equipment and storage
Matter, which comprises obtain seismic observation data；Sparse inversion model is constructed according to the seismic observation data；Construction is just
Then argument sequence, regular parameter is positive number in the regular parameter sequence, and is gradually become smaller from first regular parameter；According to institute
State regular parameter sequence, the sparse inversion model solved using iteration method, obtain the corresponding solution of regular parameter, it is described just
Then the corresponding solution of parameter is the seismic data after denoising；Regular parameter and the corresponding solution of the regular parameter are substituted into evaluation letter
Number, determines the extreme value of evaluation function；Determine that it is final for being used to calculate the corresponding solution of the regular parameter for obtaining the extreme value
Seismic data after denoising.The embodiment of the present application is by setting evaluation function come to multiple regular parameters and corresponding sparse anti-
It drills solution to model to be evaluated, determines the seismic data after final denoising by determining the extreme value of evaluation function.In as a result,
When denoising to different seismic datas, it is not necessarily to artificial selection threshold parameter, meanwhile, also improve the work of seismic data noise attenuation
Make efficiency.
In embodiment provided herein, it should be understood that disclosed device and method, it can be by others side
Formula is realized.The apparatus embodiments described above are merely exemplary, for example, the division of the unit, only one kind are patrolled
Function division is collected, there may be another division manner in actual implementation, in another example, multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some communication interfaces, device or unit
It connects, can be electrical property, mechanical or other forms.
In addition, unit may or may not be physically separated as illustrated by the separation member, as unit
The component of display may or may not be physical unit, it can and it is in one place, or may be distributed over more
In a network unit.Some or all of unit therein can be selected to realize this embodiment scheme according to the actual needs
Purpose.
Furthermore each functional module in each embodiment of the application can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
If it should be noted that function is realized in the form of software function module and sells or make as independent product
Used time can store in a computer readable storage medium.Based on this understanding, the technical solution essence of the application
On in other words the part of the part that contributes to existing technology or the technical solution can embody in the form of software products
Out, which is stored in a storage medium, including some instructions are used so that a computer equipment
(can be personal computer, server or the network equipment etc.) execute each embodiment the method for the application whole or
Part steps.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM) with
Machine accesses various Jie that can store program code such as memory (Random Access Memory, RAM), magnetic or disk
Matter.
Herein, relational terms such as first and second and the like be used merely to by an entity or operation with it is another
One entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this reality
Relationship or sequence.
The above description is only an example of the present application, the protection scope being not intended to limit this application, for ability
For the technical staff in domain, various changes and changes are possible in this application.Within the spirit and principles of this application, made
Any modification, equivalent substitution, improvement and etc. should be included within the scope of protection of this application.
Claims (10)
1. a kind of seismic data noise attenuation method characterized by comprising
Obtain seismic observation data；
Sparse inversion model is constructed according to the seismic observation data；
Regular parameter sequence is constructed, regular parameter is positive number in the regular parameter sequence, and from first regular parameter gradually
Become smaller；
According to the regular parameter sequence, the sparse inversion model is solved using iteration method, it is corresponding to obtain regular parameter
Solution, the corresponding solution of the regular parameter is the seismic data after denoising；
Regular parameter and the corresponding solution of the regular parameter are substituted into evaluation function, determine the extreme value of evaluation function；
Determine that being used to calculate the corresponding solution of the regular parameter for obtaining the extreme value is the seismic data after final denoising.
2. seismic data noise attenuation method according to claim 1, which is characterized in that the evaluation function characterizes the canonical
The norm of parameter corresponding solution and the difference of the seismic observation data, with the difference between the regular parameter.
3. seismic data noise attenuation method according to claim 2, which is characterized in that the evaluation function are as follows:
Wherein, PiFor the value of evaluation function corresponding with i-th of regular parameter；λiFor i-th of regular parameter；diFor with i-th just
The then corresponding solution of parameter；dobsFor the seismic observation data；
The extreme value of the determining evaluation function, comprising: determine the maximum of evaluation function.
4. seismic data noise attenuation method according to claim 1, which is characterized in that the construction regular parameter sequence, packet
It includes:
Rarefaction is carried out to the seismic observation data, the regular parameter sequence is determined according to the seismic observation data of rarefaction
In first regular parameter value.
5. seismic data noise attenuation method according to claim 4, which is characterized in that the value of the first regular parameter is institute
State the presupposition multiple of the maximum value of the seismic observation data of rarefaction.
6. seismic data noise attenuation method according to claim 4, which is characterized in that described according to the regular parameter sequence
Column solve the sparse inversion model using iteration method, obtain the corresponding solution of regular parameter, comprising:
Threshold denoising processing is carried out according to seismic observation data of the regular parameter to the rarefaction, is obtained and the regular parameter
Seismic data after the denoising of corresponding rarefaction；
Sparse inverse transformation is carried out to the seismic data after the denoising of the rarefaction, the seismic data after obtaining the denoising；
Wherein, the numerical value of the regular parameter is equal with the numerical value of threshold parameter in threshold denoising processing.
7. seismic data noise attenuation method according to claim 1-6, which is characterized in that described according to the earthquake
Observe data configuration sparse inversion model, comprising:
Sparse inversion model is constructed to the seismic observation data based on warp wavelet；
The sparse inversion model are as follows:
Wherein, xλFor the corresponding solution of regular parameter；λ is the regular parameter；CHFor bent wave inverse transformation；X is the earthquake number of rarefaction
According to；dobsFor the seismic observation data.
8. a kind of seismic data noise attenuation device characterized by comprising
Module is obtained, for obtaining seismic observation data；
Model module is constructed, for constructing sparse inversion model according to the seismic observation data；
Constructing variable module, for constructing regular parameter sequence, regular parameter is positive number in the regular parameter sequence, and from head
A regular parameter rises and gradually becomes smaller；
Solving model module, for solving the sparse inversion model using iteration method according to the regular parameter sequence,
The corresponding solution of regular parameter is obtained, the regular parameter is corresponding to be solved as the seismic data after denoising；
Evaluation module determines evaluation for each regular parameter and the corresponding solution of the regular parameter to be substituted into evaluation function
Function Extreme Value；
Final process module, for determining that be used to calculate the corresponding solution of the regular parameter for obtaining the extreme value goes for final
Seismic data after making an uproar.
9. a kind of electronic equipment characterized by comprising processor, memory and bus, wherein
The processor and the memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough execute the method according to claim 1 to 7.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method according to claim 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112799131A (en) * | 2019-11-13 | 2021-05-14 | 中国石油天然气股份有限公司 | Seismic data denoising method and device |
| CN114114421A (en) * | 2021-11-05 | 2022-03-01 | 中国石油大学(华东) | Deep learning-based guided self-learning seismic data denoising method and device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112799131A (en) * | 2019-11-13 | 2021-05-14 | 中国石油天然气股份有限公司 | Seismic data denoising method and device |
| CN112799131B (en) * | 2019-11-13 | 2024-05-28 | 中国石油天然气股份有限公司 | Seismic data denoising method and device |
| CN114114421A (en) * | 2021-11-05 | 2022-03-01 | 中国石油大学(华东) | Deep learning-based guided self-learning seismic data denoising method and device |
| CN114114421B (en) * | 2021-11-05 | 2023-09-29 | 中国石油大学(华东) | Deep learning-based guided self-learning seismic data denoising method and device |
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