CN107976713B - Method and device for removing deposition background under high-dimensional seismic data input - Google Patents
Method and device for removing deposition background under high-dimensional seismic data input Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for removing a deposition background under the input of high-dimensional seismic data, wherein the method comprises the steps of performing Wheeler conversion on high-dimensional original seismic data of a reservoir to be detected to obtain high-dimensional Wheeler domain seismic data; performing clustering analysis processing on the high-dimensional Wheeler domain seismic data to obtain a clustering result; respectively calculating the average value of the seismic channel data of a single cluster on the same sampling point in the clustering result, and taking the average value as the seismic data of the corresponding cluster on the same sampling point to form clustered seismic channel data; performing linear superposition processing on the clustered seismic channel data to obtain linear superposition vectors, and determining deposition background data according to the linear superposition vectors; and performing volume operation on the high-dimensional Wheeler domain seismic data and the deposition background data to obtain reservoir lithology seismic data to be detected. By utilizing the method and the device, the deposition background can be accurately and efficiently removed, and the precision of fine seismic deposition research is improved.
Description
Technical field
The present invention relates to field of petroleum geophysical exploration, especially a kind of lower removal deposition back of higher-dimension seismic data input
The method and device of scape.
Background technique
Stratigraphic and subtle reservoirs Exploration Potential is big, and field is wide, is the main body of China's oil-gas exploration, earthquake sedimentation analysis technology
There is deep application in each oilfield prospecting developing.But the sedimentations such as coal measures, volcanic rock, strongly reflecting layer is formed, it is past
Upper and lower reservoir is caused to shield toward meeting；In addition, its corresponding earthquake reflected wave will be anti-with earthquake when lithology changes
It penetrates the change of parameter and changes, the similitude also resulted between seismic channel is deteriorated, and correlation is lower, and influences the knowledge of lithologic reservoir
Not.Therefore, because the influence of deposition considerations, the difficulty such as existing earthquake sedimentation analysis technological prediction ancient stream channel, abnormal deposition body compared with
Greatly.
Problem is removed for the strong reflections such as coal seam, volcanic rock stratum, is made that compares in-depth study both at home and abroad, main benefit
The strong reflection information in seismic signal is matched to eliminate strong reflection to the effective reflective information of target zone with matching pursuit algorithm
Shielding action, but matching pursuit algorithm removes strong axis that there is also shortcomings, such as poor to interrupted strong axis removal effect,
Wavelet Base, which is easy redundancy, causes calculation amount larger etc..In addition, in order to improve the accuracy of Reservoir Analysis, it usually needs to a large amount of
Data are analyzed, and cause the data dimension inputted when operation higher, to further increase calculation amount, influence data processing
Efficiency.
Therefore, sedimentation setting is effectively removed, the accuracy of fine earthquake study on deposition is improved, becomes technology urgently to be resolved
Problem.
Summary of the invention
The method for being designed to provide a kind of lower removal sedimentation setting of higher-dimension seismic data input of the embodiment of the present application and
Device accurately can efficiently remove sedimentation setting, improve the accuracy of fine earthquake study on deposition.
A kind of method and device of the lower removal sedimentation setting of higher-dimension seismic data input provided by the present application is by including
What following manner was realized:
A kind of method of the lower removal sedimentation setting of higher-dimension seismic data input, comprising:
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, obtains the domain higher-dimension Wheeler earthquake number
According to；
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
In the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately,
Seismic data using the average value as corresponding cluster on the identical sampled point forms cluster seismic channel data；
Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the linear superposition
Vector determines sedimentation setting data；
Body operation is carried out to higher-dimension Wheeler domain seismic data and sedimentation setting data, obtains reservoir lithology to be measured
Seismic data.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, it is described by the higher-dimension
The domain Wheeler seismic data carries out clustering processing, obtains in the seismic data of the domain the higher-dimension Wheeler belonging to each seismic channel
Classification, comprising:
Class center is initialized according to preset classification number, according to initial classes center by the domain higher-dimension Wheeler earthquake number
Seismic channel in is based on the first preset rules and carries out preliminary classification；
Execute following iterative step:
It updates step and updates class center of all categories based on the second preset rules according to the classification results of last time；
Step is redistributed, according to the class center of current iteration by the seismic channel base in the seismic data of the domain higher-dimension Wheeler
It carries out redistributing classification in first preset rules；
Judgment step, judges whether cost function restrains；
Until the cost function is restrained, terminates above-mentioned iterative step, export in the seismic data of the domain the higher-dimension Wheeler
Each seismic channel generic.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, it is described to the cluster earthquake
Track data carries out linear superposition processing and obtains linear superposition vector, comprising:
Covariance data are sought to the cluster seismic channel data, calculate the characteristic value and feature of the covariance data
Vector, the eigen vector correspond；
By the characteristic value by arranging from big to small, feature vector is accordingly arranged, and obtains characteristic vector data；
Using described eigenvector data as coefficient, the cluster seismic channel data is overlapped, is obtained linear folded
Add vector.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, it is described according to described linear folded
Vector is added to determine sedimentation setting data, comprising:
First component of the linear superposition vector is determined as sedimentation setting data.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, it is described to the higher-dimension
The domain Wheeler seismic data and the sedimentation setting data carry out body operation, obtain reservoir lithology seismic data to be measured, comprising:
By in the seismic data of the domain higher-dimension Wheeler seismic channel data and the sedimentation setting data make it is poor, acquisition it is to be measured
Reservoir lithology seismic data.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, it is described to the cluster earthquake
Track data carries out linear combination and obtains linear superposition vector, before determining sedimentation setting data according to the linear superposition vector,
Further include:
The cluster seismic channel data is normalized, normalization seismic data is obtained, is based on the normalization
Seismic data obtains linear superposition vector.
The method of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, the height by reservoir to be measured
Dimension original earthquake data carry out Wheeler conversion, obtain the domain higher-dimension Wheeler seismic data after, further includes:
Filtering processing is diffused to the domain higher-dimension Wheeler seismic data, the domain higher-dimension Wheeler after being denoised
Seismic data obtains reservoir lithology seismic data to be measured based on the higher-dimension Wheeler domain seismic data after denoising.
On the other hand, the embodiment of the present application also provides a kind of device of lower removal sedimentation setting of higher-dimension seismic data input,
Include:
Conversion module obtains higher-dimension for the high-dimensional original earthquake data of reservoir to be measured to be carried out Wheeler conversion
The domain Wheeler seismic data；
Cluster Analysis module obtains cluster knot for higher-dimension Wheeler domain seismic data to be carried out clustering
Fruit；
Dimensionality reduction module, in the cluster result, calculating separately seismic channel of the single cluster on identical sampled point
The average value of data, the seismic data using the average value as corresponding cluster on the identical sampled point, forms cluster ground
Shake track data；
Data determining module is deposited, obtains linear superposition for carrying out linear superposition processing to the cluster seismic channel data
Vector determines sedimentation setting data according to the linear superposition vector；
Lithology data determining module, for carrying out body to higher-dimension Wheeler domain seismic data and sedimentation setting data
Operation obtains reservoir lithology seismic data to be measured.
The device of the lower removal sedimentation setting of the higher-dimension seismic data input of the embodiment of the present application, the deposition data determine mould
Block, comprising:
Covariance unit is calculated, for seeking covariance data to the cluster seismic channel data；
Feature vector determination unit, for calculating the characteristic value and feature vector of the covariance data, the feature
Value is corresponded with feature vector, and by the characteristic value by arranging from big to small, feature vector is arranged accordingly, is obtained
Obtain characteristic vector data；
Linear superposition vector determination unit is used for using described eigenvector data as coefficient, by the cluster earthquake
Track data is overlapped, and obtains linear superposition vector.
The device of the lower removal sedimentation setting of higher-dimension seismic data input of the embodiment of the present application, including processor and for depositing
Store up processor-executable instruction memory, when described instruction is executed by the processor realization the following steps are included:
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, obtains the domain higher-dimension Wheeler earthquake number
According to；
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
In the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately,
Seismic data using the average value as corresponding cluster on the identical sampled point forms cluster seismic channel data；
Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the linear superposition
Vector determines sedimentation setting data；
Body operation is carried out to higher-dimension Wheeler domain seismic data and sedimentation setting data, obtains reservoir lithology to be measured
Seismic data.
A kind of side of the lower removal sedimentation setting of higher-dimension seismic data input that this specification one or more embodiment provides
Method and device, can be then right by the way that the high-dimensional original earthquake data is first converted to the domain higher-dimension Wheeler seismic data
Higher-dimension Wheeler domain seismic data carries out clustering processing, and cluster seismic channel data is further obtained according to cluster result, from
And reduce data dimension；Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to institute
It states linear superposition vector and determines sedimentation setting data, then, to higher-dimension Wheeler domain seismic data and the sedimentation setting
Data carry out body operation, to accurately remove sedimentary seismic data, obtain reservoir lithology seismic data to be measured.Utilize this Shen
Please each embodiment, accurately can efficiently remove sedimentation setting, improve the accuracy of fine earthquake study on deposition.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The some embodiments recorded in application, for those of ordinary skill in the art, in the premise of not making the creative labor property
Under, it is also possible to obtain other drawings based on these drawings.In the accompanying drawings:
Fig. 1 is a kind of stream of the embodiment of the method for the lower removal sedimentation setting of higher-dimension seismic data input that this specification provides
Journey schematic diagram；
After Line943 line is converted to the domain Wheeler in the specific example that Fig. 2 provides for this specification, diffusion filter is done
The diagrammatic cross-section of wave denoising；
Fig. 3 be clustered in a specific example providing of this specification in seismic channel data the 1st, 2 ... 35,36 eachlyly
Shake road schematic diagram；
The background road schematic diagram extracted in the specific example that Fig. 4 provides for this specification；
The 1st, 2 ... 184528 seismic channels show in lithology seismic data in the specific example that Fig. 5 provides for this specification
It is intended to；
The 75th earthquake is cut in the seismic data of the domain higher-dimension Wheeler in the specific example that Fig. 6 provides for this specification
Piece and corresponding diagrammatic cross-section；
Fig. 7 is the 75th seismic slice and right in lithology seismic data in another specific example for providing of this specification
The diagrammatic cross-section answered；
Fig. 8 is a kind of mould of the Installation practice for the lower removal sedimentation setting of higher-dimension seismic data input that this specification provides
Block structure schematic diagram.
Specific embodiment
In order to make those skilled in the art more fully understand the technical solution in this specification, below in conjunction with this explanation
Attached drawing in book one or more embodiment carries out the technical solution in this specification one or more embodiment clear, complete
Site preparation description, it is clear that described embodiment is only specification a part of the embodiment, instead of all the embodiments.Based on saying
Bright book one or more embodiment, it is obtained by those of ordinary skill in the art without making creative efforts all
The range of this specification example scheme protection all should belong in other embodiments.
This specification embodiment provides a kind of method of lower removal sedimentation setting of high-dimensional seismic data input, heavy to remove
Product background interference, improves the accuracy of the fine earthquake sedimentation analysis of effective reservoir.Assuming that true stratum can be regarded as quasi- stratiform
Model, seismic reflection may include chronological change reflection (i.e. sedimentary seismic data) and lithologic character stratum reflection (i.e. lithology earthquake
Data).Then strata slicing is obtained, then contains the sedimentation setting data of reaction tectonic information and anti-in corresponding strata slicing
Answer the lithology seismic data of subtle sandbody information.It is needed during carrying out earthquake sedimentation analysis reaction construction in strata slicing
The sedimentation setting data of information remove, and the lithology seismic data of reaction subtle sandbody information is extracted, to improve effectively storage
The accuracy of the fine earthquake sedimentation analysis of layer.
In this specification embodiment, high-dimensional original earthquake data can be converted into higher-dimension Wheeler domain earthquake first
Data, in the domain Wheeler, progress sedimentation analysis is more accurate on the basis of seismic data.Then, by the domain higher-dimension Wheeler earthquake number
According to clustering is carried out, new seismic channel sample set is obtained, to keep the original domain higher-dimension Wheeler seismic data feature in height
On the basis of, input data dimension is reduced, the efficiency of follow-up data processing is improved.Based on the new seismic channel sample set of acquisition, root
According to sedimentation setting data characteristics, using the determination sedimentary seismic data of linear method precise and high efficiency, so that accurately removal is heavy
Product background, obtains lithology seismic data.
Fig. 1 is a kind of embodiment of the method for the lower removal sedimentation setting of higher-dimension seismic data input that this specification provides
Flow diagram.Although present description provides as the following examples or method operating procedure shown in the drawings or apparatus structure,
But after may include more in the method or device or part merging based on routine or without creative labor more
Few operating procedure or modular unit.In the step of there is no necessary causalities in logicality or structure, these steps
Execution sequence or the modular structure of device are not limited to this specification embodiment or execution shown in the drawings sequence or modular structure.Institute
Device in practice, server or the end product of the method or modular structure stated in application, can according to embodiment or
Method or modular structure carry out sequence execution shown in the drawings or parallel execution (such as parallel processor or multiple threads
Environment, even include distributed treatment, server cluster implementation environment).
Specific one embodiment is as shown in Figure 1, a kind of lower removal of higher-dimension seismic data input of this specification offer is heavy
In one embodiment of the method for product background, the method may include:
S0, the high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, with obtaining the domain higher-dimension Wheeler
Shake data；
S2, higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
S4, in the cluster result, calculate separately being averaged for seismic channel data of the single cluster on identical sampled point
Value, the seismic data using the average value as corresponding cluster on the identical sampled point, forms cluster seismic channel data；
S6, linear superposition processing acquisition linear superposition vector is carried out to the cluster seismic channel data, according to described linear
Superimposed vector determines sedimentation setting data；
S8, body operation is carried out to higher-dimension Wheeler domain seismic data and sedimentation setting data, obtains reservoir rock to be measured
Property seismic data.
, can be using the implementation process for the three-dimensional Wheeler transformation that Paul de Groot is proposed in the present embodiment, it will be high
Dimension original earthquake data is converted to the domain higher-dimension Wheeler seismic data.First tracing of horizons can be utilized in the case where layer position constrains
The method of explanation determines sequence boundaries, and choosing target zone, continuous strong reflection axis is used as control sequence boundaries up and down；It then can be with
Equal proportion is carried out to sequence boundaries inside stratum to divide equally；Horizon flattening is carried out later, to complete Wheeler transformation, finally will
High-dimensional original earthquake data SoriginBe converted to the domain higher-dimension Wheeler seismic data Swheeler.Common seismic data carry out sequence
Analysis, sedimentary facies recognition, sedimentary evolvement analysis and reservoir prediction have stronger more solutions because being influenced by Recent Structural trend
Property, and the domain Wheeler seismic data is convenient for after well shake calibration because with isochronism, stratigraphic cycles understand, areal extent is illustrated
Sequence and system tract divide, and evaluate Favorable Reservoir, reduce multi-solution.This specification above-described embodiment pass through by it is high-dimensional primitively
Shake data are converted to the domain Wheeler seismic data, and in the domain Wheeler, progress sedimentation analysis is more accurate on the basis of seismic data.
It, can be with after obtaining the domain higher-dimension Wheeler seismic data in one or more embodiment of this specification
Using diffusing filter technology to the domain higher-dimension Wheeler seismic data SwheelerCarry out denoising, the higher-dimension after being denoised
The domain Wheeler seismic data Sfilter, such as can use Coherent-enhancing anisotropic diffusion filtering technique to the domain higher-dimension Wheeler
Seismic data SwheelerDenoising is carried out, the continuity of the domain higher-dimension Wheeler seismic data reflection line-ups is further enhanced
And isochronism.Then, sedimentation setting removal is carried out based on the domain the Wheeler seismic data after denoising, obtains lithology seismic data,
To be further ensured that the accuracy of input data.
In one embodiment of this specification, clustering can be carried out to higher-dimension Wheeler domain seismic data, be gathered
Class result.Each seismic channel in the seismic data of the domain higher-dimension Wheeler is clustered to different classifications, so that together by clustering
Seismic channel in one classification has a biggish characteristic similarity, it is different classes of between seismic channel have biggish feature different
Property.Then, in the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately,
Seismic data using the average value as corresponding cluster on the identical sampled point forms cluster seismic channel data.It is based on
The cluster seismic channel data carries out subsequent removal sedimentation setting processing, so as to reduce number in subsequent deposition background process
According to dimension；And the cluster seismic channel data height maintains the feature of the domain former higher-dimension Wheeler seismic data, thus into one
Step ensure that the accuracy of follow-up data processing.
, can be on the basis of guaranteeing counting accuracy and efficiency in one or more embodiment of this specification, root
Classification number is preset according to actual needs, it is assumed that preset classification number is n.Class center is initialized according to preset classification number, it can
To randomly select n seismic channel as initial classes center from the seismic data of the domain the higher-dimension Wheeler.And according to described initial
Seismic channel in the seismic data of the domain the higher-dimension Wheeler is based on the first preset rules and carries out preliminary classification by class center.Then,
Execute following iterative step:
It updates step and updates class center of all categories based on the second preset rules according to the classification results of last time；
Step is redistributed, according to the class center of current iteration by the seismic channel base in the seismic data of the domain higher-dimension Wheeler
It carries out redistributing classification in first preset rules；
Judgment step, judges whether cost function restrains；
Until the cost function is restrained, terminates above-mentioned iterative step, export in the seismic data of the domain the higher-dimension Wheeler
Each seismic channel generic.
In one specific embodiment of this specification, carrying out clustering to higher-dimension Wheeler domain seismic data can be with
Include:
S402, class center is initialized according to preset classification number, is based on preset rules for higher-dimension Wheeler domain earthquake
Seismic channel in data carries out preliminary classification.
Assuming that dividing n class, each seismic channel has m sampled point；The random matrix for generating n row m column is as initial matrix.From
And select n class center u1,u2…un, each class center includes m sampled point.
Then, the seismic channel in the seismic data of the domain the higher-dimension Wheeler is subjected to preliminary classification based on preset rules.This
In one embodiment of specification, the preset rules may include: to calculate each seismic channel in the seismic data of the domain higher-dimension Wheeler
The Euclidean distance at opposite class center distributes seismic channel generic according to the size of Euclidean distance, and meets the domain higher-dimension Wheeler
The Euclidean distance at each seismic channel to the class center of generic is minimum in seismic data.Certainly, in other implementations of this specification
In example, classification can also be allocated according to other parameters, here without limitation.
S404, following iterative step is executed:
It updates step and updates class center of all categories based on the second preset rules according to the classification results of last time.This explanation
In one embodiment of book, the classification results according to last time update class center of all categories based on the second preset rules, can
To include: the classification results according to last time, the seismic channel data of certain classification is obtained, calculate in the category each seismic channel and adopt at some
New seismic data of the seismic data average value as the sampled point at sampling point, successively obtains earthquake at other m-1 sampled point
New seismic data of the statistical average as corresponding sampled point；Using this m of acquisition new seismic channel datas as the class of the category
Center, to update such class center.And so on, update the class center of all categories.
Classification step is redistributed, according to the class center of current iteration by the earthquake in the seismic data of the domain higher-dimension Wheeler
Road is based on the preset rules and carries out redistributing classification；
Judgment step can judge whether cost function restrains according to following formula:
Wherein, cpIndicate classification belonging to seismic channel p, unIndicate n-th of class center,Indicate i-th of seismic channel
Sfilter iThe corresponding class center of the class at place, p indicate the seismic channel number in the seismic data of the domain higher-dimension Wheeler.
Until the cost function is restrained, then terminates above-mentioned iterative step, export the domain higher-dimension Wheeler seismic data
In each seismic channel generic.
In some embodiments of this specification, it can also be clustered, according to Direct Cluster Analysis, correlation analysis based on statistics
The methods of clustering method carries out clustering to higher-dimension Wheeler domain seismic data, obtains in the seismic data of the domain higher-dimension Wheeler
Classification belonging to seismic channel.
In one embodiment of this specification, after the processing of above-mentioned clustering, by the domain higher-dimension Wheeler seismic data
It is clustered into n class, obtains the seismic channel data of each cluster.For any sort in the n classification after cluster, calculates the cluster and adopt
The average value of seismic channel data on sampling point, the seismic data using the average value as the cluster on corresponding sampled point,
And so on, seismic data of the category on all sampled points is obtained, the new seismic channel of the cluster is formed.Pass through above-mentioned side
Method obtains the new seismic channel of this n classification respectively, using above-mentioned n new seismic channels as cluster seismic channel.The cluster then obtained
Seismic channel data includes that n ties up seismic channel, and every dimension seismic channel includes m sampled point.The earthquake that the cluster seismic channel data includes
Road dimension is lower, therefore, is removed sedimentation setting analysis based on the cluster seismic channel data, data processing can be improved
Efficiency.
It is determining heavy carrying out linear superposition processing based on the cluster seismic channel data in one embodiment of this specification
Product background data before, can first to cluster seismic channel data be normalized, for example, can be normalized to [- 1 1] it
Between.In one or more embodiment of this specification, it can be normalized according to the following formula:
Snorm=-1+ (Scluster-Scluster min)/(Scluster max-Scluster)×(1-(-1)) (2)
Wherein, SnormIndicate normalization seismic data；SclusterIndicate cluster seismic channel data；Scluster minIndicate cluster
Seismic channel data minimum value in seismic channel data；Scluster maxIndicate seismic channel data maximum value in cluster seismic channel data.
In one embodiment of this specification, linear superposition processing is carried out to the cluster seismic channel data and obtains linear fold
Add vector, may include:
Covariance data can be sought to above-mentioned normalization seismic data.It, can be by way of matrix in the present embodiment
The covariance data are stated, covariance matrix is obtained.Calculate the feature vector and characteristic value of the covariance matrix, the spy
Value indicative and described eigenvector correspond.In one or more embodiment of this specification, it is assumed that cluster seismic channel data
There is n seismic channel, each seismic channel has m sampled point, normalizes seismic data SnormIt can be described as:
The normalization seismic data S being calculatednormCovariance matrix can indicate are as follows:
Wherein,Indicate the covariance matrix of normalization seismic data；cov(Snorm i1,Snorm i2) indicate normalizing
Change the covariance between 1 and the 2nd in seismic data, other and so on.
Wherein cov (Snorm i1,Snorm i2) specific formula for calculation it is as follows, other and so on:
Wherein, Snorm i1Indicate the 1st seismic channel in normalization seismic data；Snorm i2Indicate the in normalization seismic data
2 seismic channels；Indicate the mean value of the 1st seismic channel data in normalization seismic data；Indicate normalization earthquake number
The mean value of the 2nd seismic channel data in.
It is then possible to the feature vector Ev and eigenvalue λ of above-mentioned covariance matrix be calculated using Jacobi method, to characteristic value
It is arranged by sequence from big to small, corresponding feature vector also rearranges, and obtains characteristic vector data.It finally obtains:
λ=(λ1,λ2…λk) (6)
Wherein, k is feature vector number, and k < n；λ1> λ2> ... > λk> 0.
Corresponding feature vector are as follows:
Ev=(Ev1,Ev2…Evk) (7)
Wherein, Ev1 T=(Ev11, Ev21......Evn1),
Ev2 T=(Ev12, Ev22......Evn2),
……
Evk T=(Ev1k, Ev2k......Evnk)。
It is coefficient using described eigenvector data, the normalization seismic data is subjected to linear superposition, obtains linear
Superimposed vector.In one or more embodiment of this specification, linear superposition vector Z can be indicated are as follows:
Z=Snorm×Ev1+Snorm×Ev2+…+Snorm×Evk (8)
Wherein, Snorm×Ev1For the first component in linear superposition vector, i.e. Z1=Snorm×Ev1；Snorm×Ev2For line
Second component in property superimposed vector, i.e. Z2=Snorm×Ev2, and so on.
In some embodiments of this specification, linear superposition vector can also be obtained according to other linear superposition methods, this
In without limitation.
In one embodiment of this specification, preceding i component in the linear superposition vector can be chosen and be determined as deposition back
Scape data, wherein i is less than K；The preceding i component in linear superposition vector will be retained, the data of other components tax zero are used as deposition
Background data.In general, sedimentary shows as strong amplitude characteristic in earthquake, and similitude is poor between lithology layer seismic channel, amplitude
Value is smaller, and sedimentary seismic data is more obvious with respect to lithology seismic data feature diversity.This specification above-described embodiment mentions
The sedimentation setting data that the scheme of confession determines, can accurately reflect reservoir seismic reflection data feature to be measured, thus
It is accurate to determine sedimentary seismic data.
In one or more embodiment of this specification, it is preferred that can be by the first component in linear superposition vector
It is determined as sedimentation setting data, further increases the accuracy that sedimentary seismic data determines.Sedimentation setting data Sbackground
Calculation formula can be expressed as:
Sbackground=Z1 (9)
It, can to the domain the higher-dimension Wheeler after obtaining sedimentation setting data in one embodiment of this specification
It shakes data and the sedimentation setting data carries out body operation and obtain reservoir lithology seismic data to be measured.One implementation of this specification
In example, it is poor the seismic channel data in the seismic data of the domain higher-dimension Wheeler can be made with the sedimentation setting data, obtains to be measured
Reservoir lithology seismic data.In some other embodiment of this specification, naturally it is also possible to by other operation modes, according to
Data characteristics between higher-dimension Wheeler domain seismic data and the sedimentation setting data, by the domain high latitude Wheeler
The sedimentary seismic data shaken in data is rejected, and reservoir lithology seismic data to be measured is obtained.This specification one or more
In embodiment, reservoir lithology seismic data S to be measuredlithFormula of seeking can be with are as follows:
Slith=Snorm-Sbackground (10)
In order to enable the scheme in the embodiment that this specification provides is clearer, this specification is additionally provided using above-mentioned
The specific example in the reality of scheme region to be measured, as shown in Figures 2 to 7.
The area Ordos Basin SLG is typical low hole, hypotonic fine and close gas field, and major pay is the upper palaeozoic Permian System
Box 8, mountain 1,2 sandstone layer of mountain, be the thin reservoir of strong heterogeneity, but the area mountain 2 and Taiyuan Forma-tion generally develop coal seam, in earthquake
On show as strong amplitude characteristic, have strong shielding action to box 8, mountain 1,2 sandstone layer of mountain, while uniform deposition is also covered
The Partial Feature of reservoir.The scheme that this specification embodiment is provided is applied to the area the Ordos Basin SLG work area S19*,
It is total to have 184528 for the work area 3D, main step:
1, original earthquake data is converted into the domain Wheeler and does denoising.
It is illustrated in figure 2 after Line943 line is converted to the domain Wheeler in work area, does the section of diffusing filter denoising.
In figure as it can be seen that due to the strong reflection of Taiyuan Forma-tion coal seam influence, 2 reservoir of mountain is located in trough, and reservoir characteristic is blanked, at the same by
In the influence of uniform deposition, 1 reservoir of mountain is excessively continuous, does not meet with actual deposition feature.This original domain Wheeler section is not
Earthquake sedimentation analysis can directly be done.
2, the Wheeler body after denoising is done into clustering processing.
Number of classifying is 36, after carrying out clustering processing by the scheme that this specification embodiment provides, finally obtains 36
Road clusters seismic channel data, as shown in figure 3, respectively clustering the 1st in seismic channel data, 2 ... 35,36 seismic channels.
3, sedimentation setting data are extracted by linear method.
Cluster seismic channel data is normalized, covariance matrix is sought, rearranges the spy acquired by size
Value indicative and feature vector, using the characteristic vector data rearranged as coefficient, by the seismic data vector after normalization into
Row linear superposition obtains linear superposition vector, the first component in linear superposition vector is extracted, and obtains sedimentation setting number
According to.It is illustrated in figure 4 the background road acquired.It is learnt by background road, coal seam is extracted at sampled point 50.
4, in the sedimentation setting data basis of extraction, reservoir lithology seismic-data traces to be measured are obtained.
Body operation is carried out to the higher-dimension Wheeler domain seismic volume after denoising and obtained sedimentation setting data, is obtained to be measured
Reservoir lithology seismic data.It is illustrated in figure 5 the 1st, 2 ... 184528 seismic channels of the reservoir lithology seismic data to be measured of acquisition.
Comparison diagram 3 is it is known that the coal seam etc. near sampled point 50 is removed.
5, carry out fine Seismic Sedimentology research on reservoir lithology seismic data to be measured.
Fig. 6 (a) indicates that the schematic diagram of the 75th seismic slice of the domain higher-dimension Wheeler seismic channel, Fig. 6 (b) indicate higher-dimension
The corresponding diagrammatic cross-section of the domain Wheeler seismic channel, Fig. 7 (a) indicate the 75th seismic slice of reservoir lithology seismic data to be measured
Schematic diagram, Fig. 7 (b) indicate the corresponding diagrammatic cross-section of reservoir lithology seismic data to be measured.Corresponding profile position in Fig. 6 (b)
For 2 reservoir position of mountain, but due to the shielding action of coal seam strong reflection, reservoir is caused to be located at trough, corresponding slice map 6 (a)
Global feature it is more fuzzy, Seismic Sedimentology research can not be carried out.And by above scheme provided by the invention, treated
There is seismic reflection in 2 reservoir position of mountain shown in Fig. 7 (b), and it is upper whole more natural to correspond to slice.Y31, y34 and b7 difference
Indicate drilling well, by drilling well b7 well, carrying out calibration can know, the reservoir lithology earthquake number to be measured obtained using present invention processing
According to section, mountain 1 at well point, 2 sand body of mountain and the gamma goodness of fit is very high and laterally consecutive and variation is naturally, meet actual
Geologic sedimentation situation.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Specifically it is referred to
The description of aforementioned relevant treatment related embodiment, does not do repeat one by one herein.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the movement recorded in detail in the claims or step can be come according to the sequence being different from embodiment
It executes and desired result still may be implemented.In addition, process depicted in the drawing not necessarily require show it is specific suitable
Sequence or consecutive order are just able to achieve desired result.In some embodiments, multitasking and parallel processing be also can
With or may be advantageous.
A kind of side of the lower removal sedimentation setting of higher-dimension seismic data input that this specification one or more embodiment provides
Method, can be by being first converted to the domain higher-dimension Wheeler seismic data for the high-dimensional original earthquake data, then to higher-dimension
The domain Wheeler seismic data carries out clustering processing, cluster seismic channel data is further obtained according to cluster result, to drop
Low data dimension；Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the line
Property superimposed vector determines sedimentation setting data, then, to higher-dimension Wheeler domain seismic data and the sedimentation setting data
Body operation is carried out, to accurately remove sedimentary seismic data, obtains reservoir lithology seismic data to be measured.It is each using the application
A embodiment accurately can efficiently remove sedimentation setting, improve the accuracy of fine earthquake study on deposition.
Based on the method for the lower removal sedimentation setting of higher-dimension seismic data input described above, this specification is one or more
Embodiment also provides a kind of device of lower removal sedimentation setting of higher-dimension seismic data input.The device may include using
System, software (application), module, component, server of this specification embodiment the method etc. simultaneously combine necessary implement firmly
The device of part.Based on same innovation thinking, the device in one or more embodiments that this specification embodiment provides is for example following
Embodiment described in.Since the implementation that device solves the problems, such as is similar to method, this specification embodiment is specifically filled
The implementation set may refer to the implementation of preceding method, and overlaps will not be repeated.It is used below, term " unit " or
The combination of the software and/or hardware of predetermined function may be implemented in " module ".Although device is preferably described in following embodiment
It is realized with software, but the realization of the combination of hardware or software and hardware is also that may and be contemplated.Specifically, Fig. 8
It is a kind of modular structure signal of the Installation practice for the lower removal sedimentation setting of higher-dimension seismic data input that this specification provides
Figure, as shown in figure 8, the apparatus may include:
Conversion module 102 can be used for the high-dimensional original earthquake data of reservoir to be measured carrying out Wheeler conversion, obtain
Obtain the domain higher-dimension Wheeler seismic data；
Cluster Analysis module 104 can be used for higher-dimension Wheeler domain seismic data carrying out clustering processing,
Obtain cluster result；
Dimensionality reduction module 106 can be used in the cluster result, calculate separately single cluster on identical sampled point
The average value of seismic channel data, the seismic data using the average value as corresponding cluster on the identical sampled point, forms
Cluster seismic channel data；
Data determining module 108 is deposited, can be used for carrying out the cluster seismic channel data linear superposition processing and obtain
Linear superposition vector determines sedimentation setting data according to the linear superposition vector；
Lithology data determining module 110 can be used for higher-dimension Wheeler domain seismic data and sedimentation setting data
Body operation is carried out, reservoir lithology seismic data to be measured is obtained.
Certainly, it is described referring to preceding method embodiment, in the other embodiments of described device, the deposition data determine mould
Block 108 may include:
Covariance unit is calculated, for seeking covariance data to the cluster seismic channel data；
Feature vector determination unit, for calculating the characteristic value and feature vector of the covariance data, the feature
Value is corresponded with feature vector, and by the characteristic value by arranging from big to small, feature vector is arranged accordingly, is obtained
Obtain characteristic vector data；
Linear superposition vector determination unit, for folding described eigenvector data and the cluster seismic channel data
Add, obtains linear superposition vector.
It should be noted that device described above can also include other embodiment party according to the description of embodiment of the method
Formula.Concrete implementation mode is referred to the description of related method embodiment, does not repeat one by one herein.
A kind of dress of the lower removal sedimentation setting of higher-dimension seismic data input that this specification one or more embodiment provides
It sets, it can be by the way that the high-dimensional original earthquake data be first converted to the domain higher-dimension Wheeler seismic data, then to higher-dimension
The domain Wheeler seismic data carries out clustering processing, cluster seismic channel data is further obtained according to cluster result, to drop
Low data dimension；Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the line
Property superimposed vector determines sedimentation setting data, then, to higher-dimension Wheeler domain seismic data and the sedimentation setting data
Body operation is carried out, to accurately remove sedimentary seismic data, obtains reservoir lithology seismic data to be measured.It is each using the application
A embodiment accurately can efficiently remove sedimentation setting, improve the accuracy of fine earthquake study on deposition.
Method or apparatus described in above-described embodiment that this specification provides can realize that business is patrolled by computer program
It collects and records on a storage medium, the storage medium can be read and be executed with computer, realize this specification embodiment institute
The effect of description scheme.Therefore, this specification also provides a kind of device of lower removal sedimentation setting of higher-dimension seismic data input, packet
The memory of processor and storage processor executable instruction is included, realizes to include following when described instruction is executed by the processor
Step:
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, obtains the domain higher-dimension Wheeler earthquake number
According to；
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
In the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately,
Seismic data using the average value as corresponding cluster on the identical sampled point forms cluster seismic channel data；
Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the linear superposition
Vector determines sedimentation setting data；
Body operation is carried out to higher-dimension Wheeler domain seismic data and sedimentation setting data, obtains reservoir lithology to be measured
Seismic data.
It should be noted that device described above can also include other embodiment party according to the description of embodiment of the method
Formula.Concrete implementation mode is referred to the description of related method embodiment, does not repeat one by one herein.
The storage medium may include the physical unit for storing information, usually by after information digitalization again with benefit
The media of the modes such as electricity consumption, magnetic or optics are stored.It may include: that letter is stored in the way of electric energy that the storage medium, which has,
The device of breath such as, various memory, such as RAM, ROM；The device of information is stored in the way of magnetic energy such as, hard disk, floppy disk, magnetic
Band, core memory, magnetic bubble memory, USB flash disk；Using optical mode storage information device such as, CD or DVD.Certainly, there are also it
Readable storage medium storing program for executing of his mode, such as quantum memory, graphene memory etc..
The device of the lower removal sedimentation setting of the input of higher-dimension seismic data described in above-described embodiment, can be by first will be described
High-dimensional original earthquake data is converted to the domain higher-dimension Wheeler seismic data, then carries out to higher-dimension Wheeler domain seismic data
Clustering processing further obtains cluster seismic channel data according to cluster result, to reduce data dimension；To described poly-
Class seismic channel data carries out linear superposition processing and obtains linear superposition vector, determines sedimentation setting according to the linear superposition vector
Then data carry out body operation to higher-dimension Wheeler domain seismic data and the sedimentation setting data, thus accurately
Sedimentary seismic data is removed, reservoir lithology seismic data to be measured is obtained.It, can accurately efficiently using each embodiment of the application
Removal sedimentation setting, improve the accuracy of fine earthquake study on deposition.
It should be noted that this specification device or system described above according to the description of related method embodiment also
It may include other embodiments, concrete implementation mode is referred to the description of embodiment of the method, does not go to live in the household of one's in-laws on getting married one by one herein
It states.All the embodiments in this specification are described in a progressive manner, and same and similar part is mutual between each embodiment
Mutually referring to each embodiment focuses on the differences from other embodiments.Especially for hardware+program
For class, storage medium+program embodiment, since it is substantially similar to the method embodiment, so be described relatively simple, it is related
Place illustrates referring to the part of embodiment of the method.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the movement recorded in detail in the claims or step can be come according to the sequence being different from embodiment
It executes and desired result still may be implemented.In addition, process depicted in the drawing not necessarily require show it is specific suitable
Sequence or consecutive order are just able to achieve desired result.In some embodiments, multitasking and parallel processing be also can
With or may be advantageous.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, vehicle-mounted human-computer interaction device, cellular phone, camera phone, smart phone, individual
Digital assistants, media player, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or
The combination of any equipment in these equipment of person.
For convenience of description, it is divided into various modules when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each module can be realized in the same or multiple software and or hardware when specification one or more, it can also be with
The module for realizing same function is realized by the combination of multiple submodule or subelement etc..Installation practice described above is only
It is only illustrative, for example, in addition the division of the unit, only a kind of logical function partition can have in actual implementation
Division mode, such as multiple units or components can be combined or can be integrated into another system or some features can be with
Ignore, or does not execute.Another point, shown or discussed mutual coupling, direct-coupling or communication connection can be logical
Some interfaces are crossed, the indirect coupling or communication connection of device or unit can be electrical property, mechanical or other forms.
It is also known in the art that other than realizing controller in a manner of pure computer readable program code, it is complete
Entirely can by by method and step carry out programming in logic come so that controller with logic gate, switch, specific integrated circuit, programmable
Logic controller realizes identical function with the form for being embedded in microcontroller etc..Therefore this controller is considered one kind
Hardware component, and the structure that the device for realizing various functions that its inside includes can also be considered as in hardware component.Or
Person even, can will be considered as realizing the device of various functions either the software module of implementation method can be hardware again
Structure in component.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want
There is also other identical elements in the process, method or equipment of element.
It will be understood by those skilled in the art that this specification one or more embodiment can provide as method, system or calculating
Machine program product.Therefore, this specification one or more embodiment can be used complete hardware embodiment, complete software embodiment or
The form of embodiment combining software and hardware aspects.Moreover, this specification one or more embodiment can be used at one or
It is multiple wherein include computer usable program code computer-usable storage medium (including but not limited to magnetic disk storage,
CD-ROM, optical memory etc.) on the form of computer program product implemented.
This specification one or more embodiment can computer executable instructions it is general on
It hereinafter describes, such as program module.Generally, program module includes executing particular task or realization particular abstract data type
Routine, programs, objects, component, data structure etc..This this specification one can also be practiced in a distributed computing environment
Or multiple embodiments, in these distributed computing environments, by being held by the connected remote processing devices of communication network
Row task.In a distributed computing environment, program module can be located at the local and remote computer including storage equipment
In storage medium.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method
Part explanation.In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ",
The description of " specific example " or " some examples " etc. means specific features described in conjunction with this embodiment or example, structure, material
Or feature is contained at least one embodiment or example of this specification.In the present specification, to the signal of above-mentioned term
Property statement must not necessarily be directed to identical embodiment or example.Moreover, specific features, structure, material or the spy of description
Point may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other,
Those skilled in the art can be by different embodiments or examples described in this specification and different embodiments or examples
Feature is combined.
The above description is only an example of the present application, is not intended to limit this application.For those skilled in the art
For, various changes and changes are possible in this application.All any modifications made within the spirit and principles of the present application are equal
Replacement, improvement etc., should be included within the scope of the claims of this application.
Claims (9)
1. a kind of method of the lower removal sedimentation setting of higher-dimension seismic data input characterized by comprising
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, obtains the domain higher-dimension Wheeler seismic data；
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
In the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately, with institute
Seismic data of the average value as corresponding cluster on the identical sampled point is stated, cluster seismic channel data is formed；
Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the linear superposition vector
Determine sedimentation setting data；
By in the seismic data of the domain higher-dimension Wheeler seismic channel data and the sedimentation setting data make it is poor, obtain reservoir to be measured
Lithology seismic data.
2. the method for the lower removal sedimentation setting of higher-dimension seismic data input according to claim 1, which is characterized in that described
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, is obtained each in the seismic data of the domain the higher-dimension Wheeler
Seismic channel generic, comprising:
Class center is initialized according to preset classification number, it will be in the seismic data of the domain the higher-dimension Wheeler according to initial classes center
Seismic channel be based on the first preset rules carry out preliminary classification；
Execute following iterative step:
It updates step and updates class center of all categories based on the second preset rules according to the classification results of last time；
Step is redistributed, the seismic channel in the seismic data of the domain higher-dimension Wheeler is based on by institute according to the class center of current iteration
The first preset rules are stated to carry out redistributing classification；
Judgment step, judges whether cost function restrains；
Until the cost function is restrained, terminates above-mentioned iterative step, export various regions in the seismic data of the domain the higher-dimension Wheeler
Shake road generic.
3. the method for the lower removal sedimentation setting of higher-dimension seismic data input according to claim 1 or 2, which is characterized in that
It is described that linear superposition processing acquisition linear superposition vector is carried out to the cluster seismic channel data, comprising:
Covariance data are sought to the cluster seismic channel data, calculate the covariance data characteristic value and feature to
Amount, the eigen vector correspond；
By the characteristic value by arranging from big to small, feature vector is accordingly arranged, and obtains characteristic vector data；
Using described eigenvector data as coefficient, the cluster seismic channel data is overlapped, obtain linear superposition to
Amount.
4. the method for the lower removal sedimentation setting of higher-dimension seismic data input according to claim 3, which is characterized in that described
Sedimentation setting data are determined according to the linear superposition vector, comprising:
First component of the linear superposition vector is determined as sedimentation setting data.
5. the method for the lower removal sedimentation setting of higher-dimension seismic data input according to claim 1, which is characterized in that described
Linear combination is carried out to the cluster seismic channel data and obtains linear superposition vector, is determined and is deposited according to the linear superposition vector
Before background data, further includes:
The cluster seismic channel data is normalized, normalization seismic data is obtained, is based on the normalization earthquake
Data obtain linear superposition vector.
6. the method for the lower removal sedimentation setting of higher-dimension seismic data input according to claim 1, which is characterized in that described
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, after obtaining the domain higher-dimension Wheeler seismic data,
Further include:
Filtering processing is diffused to the domain higher-dimension Wheeler seismic data, the higher-dimension Wheeler domain earthquake after being denoised
Data obtain reservoir lithology seismic data to be measured based on the higher-dimension Wheeler domain seismic data after denoising.
7. a kind of device of the lower removal sedimentation setting of higher-dimension seismic data input characterized by comprising
Conversion module obtains higher-dimension for the high-dimensional original earthquake data of reservoir to be measured to be carried out Wheeler conversion
The domain Wheeler seismic data；
Cluster Analysis module obtains cluster result for higher-dimension Wheeler domain seismic data to be carried out clustering；
Dimensionality reduction module, in the cluster result, calculating separately seismic channel data of the single cluster on identical sampled point
Average value, the seismic data using the average value as corresponding cluster on the identical sampled point forms cluster seismic channel
Data；
Deposit data determining module, for the cluster seismic channel data carry out linear superposition processing acquisition linear superposition to
Amount, determines sedimentation setting data according to the linear superposition vector；
Lithology data determining module, for by the seismic data of the domain higher-dimension Wheeler seismic channel data and the sedimentation setting
It is poor that data are made, and obtains reservoir lithology seismic data to be measured.
8. the device of the lower removal sedimentation setting of higher-dimension seismic data input according to claim 7, which is characterized in that described
Deposit data determining module, comprising:
Covariance unit is calculated, for seeking covariance data to the cluster seismic channel data；
Feature vector determination unit, for calculating the characteristic value and feature vector of the covariance data, the characteristic value with
Feature vector corresponds, and by the characteristic value by arranging from big to small, feature vector is arranged accordingly, obtains special
Levy vector data；
Linear superposition vector determination unit is used for using described eigenvector data as coefficient, by the cluster seismic channel number
According to being overlapped, linear superposition vector is obtained.
9. a kind of device of the lower removal sedimentation setting of higher-dimension seismic data input, which is characterized in that including processor and for depositing
Store up processor-executable instruction memory, when described instruction is executed by the processor realization the following steps are included:
The high-dimensional original earthquake data of reservoir to be measured is subjected to Wheeler conversion, obtains the domain higher-dimension Wheeler seismic data；
Higher-dimension Wheeler domain seismic data is subjected to clustering processing, obtains cluster result；
In the cluster result, the average value of seismic channel data of the single cluster on identical sampled point is calculated separately, with institute
Seismic data of the average value as corresponding cluster on the identical sampled point is stated, cluster seismic channel data is formed；
Linear superposition processing is carried out to the cluster seismic channel data and obtains linear superposition vector, according to the linear superposition vector
Determine sedimentation setting data；
By in the seismic data of the domain higher-dimension Wheeler seismic channel data and the sedimentation setting data make it is poor, obtain reservoir to be measured
Lithology seismic data.
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