CN107479097A - A kind of fuzzy guarantor side filtering method based on efficient frontier structural scan - Google Patents
A kind of fuzzy guarantor side filtering method based on efficient frontier structural scan Download PDFInfo
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/20—Trace signal pre-filtering to select, remove or transform specific events or signal components, i.e. trace-in/trace-out
- G01V2210/27—Other pre-filtering
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- G—PHYSICS
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- G06T2207/20021—Dividing image into blocks, subimages or windows
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- G—PHYSICS
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- G06T2207/20024—Filtering details
Abstract
The present invention relates to a kind of fuzzy guarantor side filtering method based on efficient frontier structural scan, it is characterised in that comprises the following steps：1) for having window when geological data to be filtered establishes Sliding analysis and scanning, the various efficient frontier architectural features combination of out-of-date window center point is obtained；2) Fuzzy processing is carried out to obtained various efficient frontier architectural features combination, obtains the blurring result of distance between point point in various marginal texture combinations of features；3) fuzzy reasoning is carried out to the blurring result of central point in obtained various marginal texture combinations of features and other distance between two points, the edge weights of various marginal texture combinations of features is calculated；4) sub- filtering process is carried out to various marginal texture features, obtains the sub- filter result of various marginal texture features；5) according to the edge weights of various marginal texture features and sub- filter result, the guarantor side filter result of window center point when this is calculated.The present invention can be widely applied in the guarantor side filtering process of geological data.
Description
Technical field
The present invention relates to a kind of seismic filtering method, especially with regard to a kind of fuzzy guarantor based on efficient frontier structural scan
Side filtering method.
Background technology
Due to the presence of the anomalous geologic bodies such as reservoir interrupting layer, river course, fracture developing zone, it may appear that seismic reflection is discontinuous
Property region, it is marginal information that this, which is reflected on seismic image, also referred to as discontinuity boundary information, these discontinuity sides
Boundary's information helps to understand the geologic feature of underground.But the noise in geological data can cause to detect discontinuity boundary information
When, missing inspection or miss detection occurs, it is therefore desirable to processing is filtered to geological data before detection discontinuity border.Often
The seismic filtering method of rule can destroy discontinuity boundary characteristic while noise is filtered out, therefore generally use protects side filtering side
Method is filtered, and protects one kind that side filtering method refers to that the marginal information in seismic image can be retained while filtering
Special filtering method.
It is developed in, in the 1990s, from the definition at edge, inherently having using the guarantor side filtering method of fuzzy diagnosis
There is ambiguity, therefore protect side filtering using fuzzy theory construction there is advantage.Takashima etc. thinks should there is preferable edge
Holding capacity reach preferably compacting noise effects again, it is necessary to construction is a kind of protect side filtering method when consider it is more multifactor,
Therefore Takashima utilizes the multi-factor comprehensive structure such as the size between each element in fuzzy logic analysis sliding window, distance
Make a kind of effect for filtering and protecting and being filtered when assessing and reaching and protect.Then Muneyasu etc. utilizes a fuzzy systems reasoning identification side
Method construction is a kind of to protect side filtering method, and this method is considered at one in window between the point on both horizontally and vertically
Otherness, if this otherness is larger near edge feature, in non-edge, view data change is slow, otherness
It is smaller.Using fuzzy reasoning discriminance analysis each point relative to other points diversity factor, using this diversity factor as weights, then
Reach and protect side filter effect.Mehmet proposes the filtering method based on two pattern fuzzy logics, wherein two pattern fuzzy logics are subordinate to
Degree is not a number, but an interval range.Relative to a fuzzy systems, two fuzzy systems can preferably handle complexity
Fuzzy enviroment and inaccurate fuzzy relation.This method is that two points are constructed based on two two type fuzzy membership functions
The noise detector with vertical direction is not horizontally oriented, and the type fuzzy membership function of two of which two is obtained by training image
Arrive, window center point is the possibility of the marginal information in noise spot or image when then being judged by fuzzy reasoning, is led to
Dimension-reduction treatment output weight is crossed, finally exports filter result.
Prior art mentioned above is good at analysis uncertain problem just with fuzzy reasoning process, but
Be well using fuzzy reasoning process be good at analyze it is multifactor under conditions of the problem of property, more importantly protect side filter
Wave method is not only non-edge and fringe region in image to be distinguished, also to distinguish fringe region and non-edge,
Difference between point point when simply being considered in above method in window, but do not account for discontinuity border and noise and tying
Important difference in structure feature, and this property is to discriminate between the key of noise and discontinuous characteristic information.Cause existing guarantor side
Although filtering method can distinguish fringe region and non-edge, when noise energy is higher, noise and side are but distinguished
Edge feature.In addition, from algorithm structure, the algorithm computing is complicated, computationally intensive.
The content of the invention
In view of the above-mentioned problems, it is an object of the invention to provide a kind of fuzzy guarantor side filtering based on efficient frontier structural scan
Method, data variance between points in window when this method considers not only, it is also contemplated that one of fringe region and noise
Significant differences, i.e. edge have architectural feature, and noise is random distribution, without architectural feature.Utilize fuzzy reasoning method
According to the two properties, you can to distinguish noise and fringe region well, fringe region and non-edge can be distinguished again
Region, so as to reach a kind of more preferable guarantor's side filter effect.
To achieve the above object, the present invention takes following technical scheme：It is a kind of fuzzy based on efficient frontier structural scan
Protect side filtering method, it is characterised in that comprise the following steps：1) when establishing Sliding analysis for existing geological data to be filtered
Window, it is scanned respectively in window in Sliding analysis, obtains the various efficient frontier architectural features combination of out-of-date window center point；2)
Fuzzy processing is carried out to obtained various efficient frontier architectural features combination, obtains point point in various marginal texture combinations of features
Between distance blurring result；3) fuzzy inference rule is built, to central point in obtained various marginal texture combinations of features
Fuzzy reasoning is carried out with the blurring result of other distance between two points, the edge of various marginal texture combinations of features is calculated
Weight；4) the blurring result based on distance between point point in the various marginal texture combinations of features obtained in step 2), to each
Kind marginal texture feature carries out the sub- filtering process of one-dimensional edge holding, obtains the sub- filter result of various marginal texture features；
5) the various marginal texture features obtained in the edge weights and step 4) of the various marginal texture features obtained according to step 3)
Sub- filter result, the guarantor side filter result of window center point when this is calculated.
In the step 2), blurring result acquisition methods comprise the following steps：2.1) all marginal texture features are calculated
The distance between point point in combination；2.2) default threshold value is based on, to putting point in obtained various marginal texture combinations of features
Between distance be normalized；2.3) membership function is built, to putting point after normalization in all marginal texture combinations of features
Between distance carry out Fuzzy processing, obtain its be blurred result.
In the step 2.1), the distance between each marginal texture combinations of features midpoint point is by absolute value poor between a point
It is indicated, its calculation formula A, B, C are defined as：
In formula,It is the central point of sliding window,Other 2 points respectively in marginal texture combinations of features.
In the step 2.2), poor absolute value A, B, C are normalized between being put to obtained point, and normalized function is fixed
Justice is：
In formula：X={ A, B, C }, f (x) are the values after normalization, and m is threshold value, and its calculation formula is：
In formula：G is an integer, and geological data is divided into G rank, A by GmaxAnd AminIt is seismic data cube respectively
Amplitude maximum and minimum value.
In the step 3), the method for calculating the weight of each marginal texture feature comprises the following steps：3.1) create fuzzy
Change rule, the blurring result of window center point and other distance between two points obscures during in each marginal texture combinations of features
Reasoning, obtain the fuzzy reasoning result of linguistic variable form；3.2) the linguistic variable shape of each marginal texture combinations of features will be obtained
The fuzzy reasoning result of formula is converted to digitized fuzzy reasoning result；3.3) calculation is shipped using fuzzy set, be calculated
Degree of membership of the various marginal texture features under every kind of fuzzy inference rule；3.4) according to obtained each marginal texture feature each
Degree of membership under fuzzy inference rule, the edge weights of various marginal texture features are calculated.
In the step 3.1), the fuzzy rule of establishment is：
B is that small THEN marginal textures are characterized in that the possibility degree at edge is " height " while IF A are small；
B is the possibility degree " general " that big THEN marginal textures are characterized in edge while IF A are small；
B is the possibility degree " general " that small THEN marginal textures are characterized in edge while IF A are big；
B is that big THEN marginal textures are characterized in edge possibility degree " low " while IF A are big.
In the step 3.3), the calculation formula of the degree of membership under every kind of fuzzy inference rule is：
In formula：It is the output language change under r-th of IF-THEN of the n marginal texture combinations of features of scanning regular
The degree of membership of amount, and n=1,2 ..., 12, r=1,2,3,4；It is the n marginal texture feature of scanning respectively
In r-th of IF-THEN rule of combination, the degree of membership of input language variable corresponding to A, B difference.
In the step 3.4), the calculation formula of the edge weights of various marginal texture features is：
In formula：wnIt is the edge weights of n marginal texture combinations of features；yiIt is the digitlization of corresponding output language variable
Represent.
In the step 4), the sub- filter result acquisition methods of various marginal texture combinations of features comprise the following steps：
4.1) blurring rule is created, mould is carried out to the otherness in the combination of any edge architectural feature between any point and other 2 points
Reasoning is pasted, obtains the fuzzy reasoning result of its linguistic variable form；4.2) become the language for obtaining each marginal texture combinations of features
The fuzzy reasoning result of amount form is converted to digitized fuzzy reasoning result；4.3) using the calculation of shipping of fuzzy set, calculate
Obtain the degree of membership of the marginal texture feature under each fuzzy inference rule；4.4) according to the obtained marginal texture feature each
Degree of membership under fuzzy inference rule, the weight of the point in the marginal texture combinations of features is calculated using gravitational law；4.5) weight
4.4) multiple step 4.1) is to, calculating other 2 points weights in the marginal texture combinations of features；4.6) it is special according to the marginal texture
The weight of each point in sign combination, the submodule paste filter result of the marginal texture combinations of features is calculated；4.7) above-mentioned step is repeated
Rapid 4.1~4.6), the submodule paste filter result of all marginal texture combinations of features is calculated：
In formula：ηiIt is the submodule paste filter result of i-th kind of marginal texture combinations of features；It is correspondingWeight.
In the step 5), when window center point the calculation formula of filter result be：
For the present invention due to taking above technical scheme, it has advantages below：1st, the present invention is provided with for characterizing edge
With the threshold value of the difference in noise structure, consider when window in data variance between points while, it is contemplated that edge and
Difference of the noise in structure so that filter result can distinguish noise and fringe region well.2nd, the present invention is according to side
The property of edge structure, i.e., the slow feature of data variation on edge, the edge for analyzing various marginal texture combinations of features successively are weighed
Weight, obtains it and belongs to the possibility at edge so that filter result can be good at distinguishing fringe region and non-edge.This
Invention may determine that the data of wave point to be filtered are on edge or the data by noise pollution, thus the present invention can be with
It is widely used in the guarantor side filtering process of geological data.
Brief description of the drawings
Fig. 1 is the flow chart of the fuzzy guarantor side filtering method of the invention based on efficient frontier structural scan；
Fig. 2 (a)~(l) is 12 kinds of efficient frontier architectural features that present invention scanning obtains；
Fig. 3 is marginal texture feature schematic diagram of the present invention；
Fig. 4 is the membership function of the present invention.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
As shown in figure 1, a kind of fuzzy guarantor side filtering method based on efficient frontier structural scan provided by the invention, specifically
Step is as follows：
1) window when establishing Sliding analysis for existing geological data to be filtered, is swept respectively in Sliding analysis in window
Retouch, obtain the various efficient frontier architectural features combination of out-of-date window center point.
As shown in Fig. 2 belong to the degree at edge, it is necessary to ask for the numerical value change in the vertex neighborhood of center to characterize central point
Change direction and intensity of variation, can as the derivative along 2 directions according to the difference typically taken in discrete series between consecutive points
Know, the minimum neighbourhood scope that can be found be 3 × 3 when window, in 3 × 3 time windows, be labeled as 1 be calculate architectural feature
Three directions, combine and obtain 12 kinds of efficient frontier architectural features.When this 12 kinds of architectural features can be regarded as 3 × 3
One group of base of a rim space of all edge compositions in window, any marginal texture at this in window can be by 12 kinds
Efficient frontier architectural feature characterizes.
2) Fuzzy processing is carried out to obtained various efficient frontier architectural features combination, obtains various marginal texture features
The blurring result of distance between the interior point point of combination.
As shown in figure 3, being i-th group of marginal texture combinations of features schematic diagram, include three points in combination altogether, wherein,It is
When window central point,Other 2 points respectively in marginal texture feature.The combination of various marginal textures is blurred
Method, comprise the following steps：
2.1) the distance between point point in all marginal texture combinations of features is calculated.
The distance between point point is indicated with absolute value poor between point point in each marginal texture combinations of features, and it is calculated
Formula A, B, C are defined as：
2.2) default threshold value is based on, the distance between point point in obtained various marginal texture combinations of features is returned
One changes.
Poor absolute value A, B, C are normalized between being put to obtained point, and normalized function is defined as：
In above formula：X={ A, B, C }, f (x) are the values after normalization, and m is threshold value, and it is determined by below equation：
In formula：G is an integer, and geological data is divided into G rank, A by GmaxAnd AminIt is seismic data cube respectively
Amplitude maximum and minimum value.Threshold value m setting is used to distinguish the difference on edge and noise structure, that is, determines to filter out noise
The yardstick of yardstick and Protect edge information characteristic information., will be related to threshold value m settings when signal to noise ratio is relatively low, it is necessary to when iterating to calculate
Series G is set as a less value, to protect more discontinuity features；It is when threshold value setting is larger, then first smooth
The noise of large scale, very little is influenceed on small yardstick noise and edge feature.
2.3) membership function is built, to the distance between point point is carried out in all marginal texture combinations of features after normalization
Fuzzy processing, obtain it and be blurred result.
As shown in figure 4, be utilized respectively two on linguistic variable " big ", each point after the membership function of " small " will normalize
Between poor absolute value f (x) be mapped in two fuzzy subsets, two membership functions are as follows：usIt is to become on language
Measure the membership function of " small "；ubIt is the membership function on linguistic variable " big "；α, β are to construct this two degree of membership letters
Several parameters, α spans size is close to 0, β spans size close to 1.Because " big " and " small " is in language description
In antagonistic relations, then two membership functions meet following relation：
us+ub=1 (4)
3) fuzzy inference rule is built, in obtained various marginal texture combinations of features between central point and at other 2 points
The blurring result of distance carries out fuzzy reasoning, and the edge weights of various marginal texture combinations of features are calculated.
From figure 2 it can be seen that A, B size have reacted the difference between central point and in marginal texture feature at other 2 points
DRS degree, can express whether the change of data on marginal texture is violent, and severe degree can reflect the marginal texture feature
Belong to the degree at edge.Fuzzy inference rule is built according to above-mentioned analysis, to the mould of obtained various marginal texture combinations of features
It is gelatinized result and carries out fuzzy reasoning, and calculate the weight of each marginal texture feature, comprises the following steps：
3.1) blurring rule is created, window center point and other distance between two points during in each marginal texture combinations of features
Blurring result carry out fuzzy reasoning, obtain the fuzzy reasoning result of linguistic variable form.
Above analysis result is write as IF-THEN forms according to fuzzy reasoning mode, is：
B is that small THEN marginal textures are characterized in that the possibility degree at edge is " height " while IF A are small；
B is the possibility degree " general " that big THEN marginal textures are characterized in edge while IF A are small；
B is the possibility degree " general " that small THEN marginal textures are characterized in edge while IF A are big；
B is that big THEN marginal textures are characterized in edge possibility degree " low " while IF A are big；
Under four kinds of IF-THEN rules, the linguistic variable of the description edge possibility of output is " height ", " general ", " low ".
3.2) the fuzzy reasoning result for the linguistic variable form for obtaining each marginal texture combinations of features is converted into digitlization
Fuzzy reasoning result.
Calculated in order to participate in, three kinds of linguistic variables are digitized, it is as shown in table 1 below.
The output language of table 1 digitizes
3.3) calculation is shipped using fuzzy set, various marginal texture features is calculated under every kind of fuzzy inference rule
Degree of membership.
Degree of membership on the output language variable under every kind of fuzzy inference rule is obtained by shipping for fuzzy set：
In formula：It is the output language change under r-th of IF-THEN of the n marginal texture combinations of features of scanning regular
The degree of membership of amount, and n=1,2 ..., 12, r=1,2,3,4.It is the n marginal texture feature of scanning respectively
In r-th of IF-THEN rule of combination, the degree of membership of input language variable corresponding to A, B difference.
3.4) degree of membership according to obtained each marginal texture feature under each fuzzy inference rule, is calculated using gravity model appoach
Obtain the edge weights of various marginal texture features.
The calculation formula of the edge weights of various marginal texture features is：
In above formula：wnIt is the edge weights of n marginal texture combinations of features, expresses the marginal texture combinations of features category
Degree in edge；yiIt is the digitized representations of corresponding output language variable.
4) the blurring result based on distance between point point in the various marginal texture combinations of features obtained in step 2), is adopted
With submodule paste filtering method obtained various marginal texture combinations of features are carried out with the sub- filtering process of one-dimensional edge holding, is obtained
The sub- filter result of various marginal texture combinations of features.
Submodule pastes filtering method thought substantially with judging that edge weights are similar, comprises the following steps：
4.1) blurring rule is created, to the difference in a certain marginal texture combinations of features between certain point and at other 2 points
Property carry out fuzzy reasoning, obtain the fuzzy reasoning result of its linguistic variable form.
As can be seen from Figure 2：A, C can be reflectedWithBetween difference；A, B can be reflectedWithBetween difference, B, C can be reflectedWithBetween difference.That is, each marginal texture combinations of features
The interior otherness between other 2 points represents that it may be constructed (A, B), (B, C) with combining form, (A, C) three groups
Close, four kinds of IF-THEN rules can be respectively constituted for each combination.To be introduced exemplified by combination (A, C) in the present invention, other
Two kinds of combinations are similar, will not be repeated here.Characterize point in the marginal texture combinations of featuresWithBetween otherness
Combination (A, C) four kinds of IF-THEN rules be respectively：
C is small THEN while IF A are smallIt is " big " with other 2 points othernesses；
C is small THEN while IF A are bigWith other 2 points of othernesses " general "；
C is big THEN while IF A are smallWith other 2 points of othernesses " general "；
C is big THEN while IF A are bigWith other 2 points of othernesses " small ".
4.2) the fuzzy reasoning result for the linguistic variable form for obtaining each marginal texture combinations of features is converted into digitlization
Fuzzy reasoning result.
Four kinds of IF-THEN rules based on establishment, and the number language digitized representations side in step 3) shown in table 1
Method, degree of membership output language variable " big ", " general ", " small " are converted into numeral output.
4.3) calculation is shipped using fuzzy set, person in servitude of the marginal texture feature under each fuzzy inference rule is calculated
Category degree.
4.4) according to the obtained degree of membership of the marginal texture feature under each fuzzy inference rule, using gravitational law meter
Calculate the weight of the point in the marginal texture combinations of features.
4.5) repeat step 4.1) to 4.4), calculating other 2 points weights in the marginal texture combinations of features.
4.6) according to the weight of each point in the marginal texture combinations of features, the son of the marginal texture combinations of features is calculated
Fuzzy filter result：
In formula：ηiIt is the submodule paste filter result of i-th kind of marginal texture combinations of features；It is correspondingWeight.
4.7) 4.1~4.6 are repeated the above steps), the sub- fuzzy filter knots of all marginal texture combinations of features is calculated
Fruit.
5) the various edges obtained in the edge weights and step 4) of the various marginal texture features obtained according to step 3)
The sub- filter result of architectural feature, the guarantor side filter result of window center point when this is calculated using gravity model appoach.
When window center point the calculation formula of filter result be：
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic,
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
1. a kind of fuzzy guarantor side filtering method based on efficient frontier structural scan, it is characterised in that comprise the following steps：
1) window when establishing Sliding analysis for existing geological data to be filtered, is scanned respectively in Sliding analysis in window,
Obtain the various efficient frontier architectural features combination of out-of-date window center point；
2) Fuzzy processing is carried out to obtained various efficient frontier architectural features combination, obtains various marginal texture combinations of features
The blurring result of distance between interior point point；
3) fuzzy inference rule is built, to central point in obtained various marginal texture combinations of features and other distance between two points
Blurring result carry out fuzzy reasoning, the edge weights of various marginal texture combinations of features are calculated；
4) the blurring result based on distance between point point in the various marginal texture combinations of features obtained in step 2), to various
Marginal texture feature carries out the sub- filtering process of one-dimensional edge holding, obtains the sub- filter result of various marginal texture features；
5) the various marginal textures obtained in the edge weights and step 4) of the various marginal texture features obtained according to step 3)
The sub- filter result of feature, the guarantor side filter result of window center point when this is calculated.
A kind of 2. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 1, it is characterised in that：
In the step 2), blurring result acquisition methods comprise the following steps：
2.1) the distance between point point in all marginal texture combinations of features is calculated；
2.2) default threshold value is based on, normalizing is carried out to the distance between point point in obtained various marginal texture combinations of features
Change；
2.3) membership function is built, to the distance between point point obscures in all marginal texture combinations of features after normalization
Change is handled, and is obtained it and is blurred result.
A kind of 3. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 2, it is characterised in that：
In the step 2.1), the distance between each marginal texture combinations of features midpoint point carries out table by absolute value poor between a point
Show, its calculation formula A, B, C are defined as：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>A</mi>
<mo>=</mo>
<mo>|</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
<mo>|</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>B</mi>
<mo>=</mo>
<mo>|</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>3</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
<mo>|</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>C</mi>
<mo>=</mo>
<mo>|</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mn>3</mn>
</msubsup>
<mo>|</mo>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>;</mo>
</mrow>
In formula,It is the central point of sliding window,Other 2 points respectively in marginal texture combinations of features.
A kind of 4. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 2, it is characterised in that：
In the step 2.2), poor absolute value A, B, C are normalized between being put to obtained point, and normalized function is defined as：
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mfrac>
<mi>x</mi>
<mi>m</mi>
</mfrac>
</mtd>
<mtd>
<mrow>
<mi>x</mi>
<mo><</mo>
<mi>m</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mi>x</mi>
<mo>&GreaterEqual;</mo>
<mi>m</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
In formula：X={ A, B, C }, f (x) are the values after normalization, and m is threshold value, and its calculation formula is：
<mrow>
<mi>m</mi>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>A</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<mi>G</mi>
</mfrac>
<mo>;</mo>
</mrow>
In formula：G is an integer, and geological data is divided into G rank, A by GmaxAnd AminIt is seismic data cube amplitude respectively
Maximum and minimum value.
A kind of 5. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 1, it is characterised in that：
In the step 3), the method for calculating the weight of each marginal texture feature comprises the following steps：
3.1) blurring rule is created, the mould of window center point and other distance between two points during in each marginal texture combinations of features
It is gelatinized result and carries out fuzzy reasoning, obtains the fuzzy reasoning result of linguistic variable form；
3.2) the fuzzy reasoning result for the linguistic variable form for obtaining each marginal texture combinations of features is converted into digitized mould
Paste the reasoning results；
3.3) calculation is shipped using fuzzy set, person in servitude of the various marginal texture features under every kind of fuzzy inference rule is calculated
Category degree；
3.4) degree of membership according to obtained each marginal texture feature under each fuzzy inference rule, is calculated various edge knots
The edge weights of structure feature.
A kind of 6. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 5, it is characterised in that：
In the step 3.1), the fuzzy rule of establishment is：
B is that small THEN marginal textures are characterized in that the possibility degree at edge is " height " while IF A are small；
B is the possibility degree " general " that big THEN marginal textures are characterized in edge while IF A are small；
B is the possibility degree " general " that small THEN marginal textures are characterized in edge while IF A are big；
B is that big THEN marginal textures are characterized in edge possibility degree " low " while IF A are big.
A kind of 7. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 5, it is characterised in that：
In the step 3.3), the calculation formula of the degree of membership under every kind of fuzzy inference rule is：
In formula：It is the output language variable under r-th of IF-THEN of the n marginal texture combinations of features of scanning regular
Degree of membership, and n=1,2 ..., 12, r=1,2,3,4；It is the n marginal texture combinations of features of scanning respectively
R-th of IF-THEN rule in, A, B respectively corresponding to input language variable degree of membership.
A kind of 8. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 5, it is characterised in that：
In the step 3.4), the calculation formula of the edge weights of various marginal texture features is：
<mrow>
<msup>
<mi>w</mi>
<mi>n</mi>
</msup>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</munderover>
<msubsup>
<mi>&mu;</mi>
<mi>n</mi>
<mi>i</mi>
</msubsup>
<msup>
<mi>y</mi>
<mi>i</mi>
</msup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</munderover>
<msubsup>
<mi>&mu;</mi>
<mi>n</mi>
<mi>i</mi>
</msubsup>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
In formula：wnIt is the edge weights of n marginal texture combinations of features；yiIt is the digitized representations of corresponding output language variable.
A kind of 9. fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 1, it is characterised in that：
In the step 4), the sub- filter result acquisition methods of various marginal texture combinations of features comprise the following steps：
4.1) blurring rule is created, the otherness in the combination of any edge architectural feature between any point and other 2 points is entered
Row fuzzy reasoning, obtain the fuzzy reasoning result of its linguistic variable form；
4.2) the fuzzy reasoning result for the linguistic variable form for obtaining each marginal texture combinations of features is converted into digitized mould
Paste the reasoning results；
4.3) calculation is shipped using fuzzy set, the marginal texture feature being subordinate under each fuzzy inference rule is calculated
Degree；
4.4) according to the obtained degree of membership of the marginal texture feature under each fuzzy inference rule, being calculated using gravitational law should
The weight of the point in marginal texture combinations of features；
4.5) repeat step 4.1) to 4.4), calculating other 2 points weights in the marginal texture combinations of features；
4.6) according to the weight of each point in the marginal texture combinations of features, the submodule that the marginal texture combinations of features is calculated is pasted
Filter result；
4.7) 4.1~4.6 are repeated the above steps), the submodule paste filter results of all marginal texture combinations of features is calculated：
<mrow>
<msup>
<mi>&eta;</mi>
<mi>i</mi>
</msup>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>j</mi>
</msubsup>
<msubsup>
<mi>&epsiv;</mi>
<mi>i</mi>
<mi>j</mi>
</msubsup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msubsup>
<mi>&epsiv;</mi>
<mi>i</mi>
<mi>j</mi>
</msubsup>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
In formula：ηiIt is the submodule paste filter result of i-th kind of marginal texture combinations of features；It is correspondingWeight.
10. a kind of fuzzy guarantor side filtering method based on efficient frontier structural scan as claimed in claim 1, its feature exist
In：In the step 5), when window center point the calculation formula of filter result be：
<mrow>
<mi>X</mi>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msup>
<mi>w</mi>
<mi>i</mi>
</msup>
<msup>
<mi>&eta;</mi>
<mi>i</mi>
</msup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msup>
<mi>w</mi>
<mi>i</mi>
</msup>
</mrow>
</mfrac>
<mo>.</mo>
</mrow>
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| CN110766705A (en) * | 2019-09-11 | 2020-02-07 | 集美大学 | Color image edge detection method based on interval two-type fuzzy similarity |
| CN112444880A (en) * | 2019-08-30 | 2021-03-05 | 中国石油化工股份有限公司 | Fast filtering method for suppressing clutter and storage medium |
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