CN105182410B - Geological data Lamellar character strengthens object plotting method - Google Patents
Geological data Lamellar character strengthens object plotting method Download PDFInfo
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Abstract
Strengthen object plotting method the invention discloses a kind of geological data Lamellar character, it is characterised in that comprise the following steps S1, judge whether sampled point is extreme point；If S2, sampled point are not extreme points, abandon the sampled point and continue to judge next sampled point；Whether if sampled point is extreme point, it is noise spot to determine whether the extreme point, if not noise spot, then retains the extreme point and show the extreme value point-rendering；If sampled point is noise spot, the noise spot is eliminated.The present invention has good correlation in the horizontal direction using the seed point of same layer position, based on many track datas, carries out noise spot filter operation, can eliminate geological data noise, reach seismic volume data noise filter effect and the enhanced purpose of Lamellar character.
Description
Technical field
The invention belongs to geological data technical field of mapping, more particularly to a kind of geological data Lamellar character enhancing volume drawing
Method.
Background technology
It is direct volume drawing to explore one effective ways of volume data.By Volume Rendering Techniques, what we can become apparent from
Understand the internal structure of object analysis, rather than be limited only to the surface of object.In daily life, multiple fields also use this
Technology carrys out solving practical problems, such as：In medical domain, by visualization technique, doctor is reached by observing the inside of organ
Condition-inference purpose；In meteorological field, forecast personnel utilize visualization technique, analyzed vortex internal structure change, so as to judge
Its future trend；In geological exploration field, geology internal structure is analyzed by visualization technique, depositing for fossil fuel can be predicted
Storage space is put, so as to solve the energy demand of growing tension.
In being visualized on stratum, using Volume Rendering Techniques, it can preferably represent earthquake volume data in three dimensions
Continuity, carries out the tracking of layer bit line and structure interpretation, and provide more three-dimension interactions (translation, rotation).
Traditional object plotting method is, from imaging plane, to be sampled along the reverse direction of tripleplane, and travel through
Element of volume, calculating color are integrated one by one；During integral and calculating.Lost to reduce information as far as possible, it is necessary to greatly carry
High sampling rate.However, extra performance cost will be brought by improving sample rate, and solution is exactly to go out from the angle of algorithm improvement
Hair, using pre-integration Volume Rendering Techniques (pre-integrated volume rendering).Pre-integration volume drawing consider be
The point formed by series of points is to (slab), rather than single element of volume.Ground data are visited with reference to three-dimensional, Castanie etc. makes first
Rendered with pre-integration Volume Rendering Techniques, and with reference to lighting effect；In terms of visualization angle, Ropinski etc. proposes to finish
The Volume Rendering Techniques of Focus+Context thoughts are closed, by simulating spherical camera lens, stereo lens and the effect for blocking camera lens,
Highlight user's region of interest (region of interest, ROI).
However, geological data has the following disadvantages：It is also that most importantly noise is big, signal to noise ratio is low first；Next to that planting
Class is complicated, and substance classes, which are interlocked, to be mixed.These features of ground data are exactly visited, cause traditional method for visualizing can not
Geologic objective is shown well.
In two dimension slicing visualization, core procedure is the extraction of layer bit line；Same thought can also be transplanted to three-dimensional
In visualization, i.e. extract layer bit slice.Can be randomly assigned first spy ground data in an element of volume (voxel) as seed point,
Add it in layer bit slice set；Secondly, point most like therewith is found around the point, a layer bit slice set is also added to
In, iteration, finally obtains whole layer bit slice successively.After layer bit slice is obtained, the curved surface based on level can be further used
Layer bit slice is clustered and is divided into different fritters by partitioning algorithm, and assigns level of hierarchy value, and user can alternatively control to work as
The level of hierarchy value of preceding system, the grade point directly controls the group number finally clustered to pass through the setting of level of hierarchy value, Yong Huke
With alternatively a part of display layer bit slice.
Hollt etc. quotes the concept of cost function (cost function), filters out extreme value seed point, distributes low generation
Value, non-extreme point distributes high cost value.Cost value can be as the reference in drawing process, and cost value is higher, then transparent
Degree is higher, and cost value is lower, then transparency is lower.After having drawn, using explosive view (exploded view) technology by volume drawing
In layer separate so that the internal structure of volume data and relation between layers can be explored more clearly, solve the superposition of layer position
The problem of.
For the layered distribution of geologic objective, visual analyzing is carried out, geologic horizon interface generally there are four kinds of situations：Ripple
Peak, trough, 0+, 0-, current achievement in research are that specify that the feature enhancing of crest or trough, do not propose 0+ and 0- ripple
The enhanced method of shape, and existing cost function is the wave character increase based on single track, does not observe waveform level related
Property feature.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of seed point of utilization same layer position in level
There is good correlation on direction, based on many track datas, carry out noise spot filter operation, geological data noise can be eliminated,
Reach the geological data Lamellar character enhancing volume drawing side of seismic volume data noise filter effect and the enhanced purpose of Lamellar character
Method.
The purpose of the present invention is achieved through the following technical solutions：Geological data Lamellar character strengthens volume drawing side
Method, comprises the following steps：
S1, judge whether sampled point is extreme point；
If S2, sampled point are not extreme points, abandon the sampled point and continue to judge next sampled point；If sampled point is pole
It is worth point, then whether be noise spot, if not noise spot if determining whether the extreme point, then retains the extreme point and by the extreme value
Point-rendering is shown；If sampled point is noise spot, the noise spot is eliminated.
Further, the noise spot in described step S2 is eliminated using the noise cancellation method based on density or is based on
The noise cancellation method of coefficient correlation.
The described noise cancellation method concrete methods of realizing based on density is：Noise spot is discrete point, even if noise spot
Exactly extreme point, is also impossible to large area around it and extreme point occurs.Noise cancellation method based on density is special using this
Point, when sampled point is extreme point, judges the individual of extreme point around the single track data where extreme point at least one single track data
Number, if the number of surrounding extreme point is more than default threshold value, the extreme point is just judged to noise spot；Specifically include following sub-step
Suddenly：
S211, the extreme value point data that will need judgement are extended to as origin in the single track data where origin to surrounding
Few 1 single track data area, extracts the single track data of sample；
S212, in each single track data, centered on origin, extract the sample point around origin, count all samples
The total number N of extreme point in point, if N is more than default threshold values MinPts, it is not noise spot to illustrate origin, is otherwise noise
Point；
S213, elimination noise spot.
Further, judge in described step S213 origin whether be noise spot specific cost function g (x, y, z, k,
N it is) as follows：
Wherein：Represent judge origin whether be extreme point function；MinPts is a default threshold value,
It is a positive integer；K is default positive integer；
Wherein,
Or,
Value be 1, it is extreme point to represent current sample point, wherein, formula (2) represents that extreme point is very big
It is worth point, formula (3) represents that extreme point is minimum point, and 0 value represents non-extreme point；F (x, y, z) represents the width of current sampling point
Value, k values are the interval of sample point in a z-direction, are pre-set by user；
Formula (4) is that the number of surrounding extreme point, N represents that origin is sat in the case that calculating current sampling point is extreme point
X, y are marked, z direction extensions are the integers that a user pre-sets；
When certain origin eligible (2) or (3), as the number of extreme point is more than around extreme point, and the extreme point
During MinPts, cost value is g (x, y, z, k, N)=1, and the point is not noise spot, is otherwise noise spot.
The described noise cancellation method principle based on coefficient correlation is：There is height between earthquake volume data per pass data
Related characteristic is spent, if there is noise spot, then correlation coefficient value necessarily rises, so it is extreme point to work as sampled point (x, y, z)
When, count linearly dependent coefficient of that track data with surrounding per track data belonging to the extreme point, final statistically linear phase relation
Shuo Gaozong roads number；It is comprised the following steps that：
S221, selection have the single track data of extreme point, extract in the single track data voxel value a little；
S222, the voxel value of institute a little in remaining single track data is extracted, and calculating has the single track data voxel value of extreme point
With the linearly dependent coefficient between other each single track data voxel values；
S223, the size for judging linearly dependent coefficient and default threshold coefficient that S222 is obtained, if linear correlation system
Number is more than threshold coefficient, then two single track data coefficient correlations are high；
The high single track data total number of S224, the single track data coefficient correlation for counting and having extreme point, and judge this total
Whether number is more than default threshold value MinPts, if the extreme point that total number is more than in threshold value MinPts, the single track data is
Noise spot, otherwise the extreme point (make into for non-noise point：Then the extreme point in the single track data is not noise spot, otherwise the pole
Value point is noise spot)；
S225, eliminate the noise spot.
Further, whether it is that the cost function of noise spot is that the extreme point is judged in the step S224：
What ρ (x, y, z) was counted is the single track data and the high single track data of surrounding coefficient correlation described in current extreme value point
Total number, specific formula for calculation is as follows：
Wherein ω (x, y, z) represent have the single track data voxel value of extreme point and other each single track data voxel values it
Between linearly dependent coefficient function：
α (changes for the threshold coefficient that user pre-sets：Least correlativing coefficient value)；
γ is linearly dependent coefficient, and its computing formula is：
Wherein miCurrent single track data are represented in the z directions time window [z-N, z+N], the voxel value f of certain sampled point (x,
y,z+N)；M represents the average value of current single track data voxel value of all sampled points in z directions time window [z-N, z+N]；
niAdjacent single track data are represented in z directions time window [z-N, z+N], the voxel value of certain sampled point；N represents adjacent single track number
According to the voxel value average value of all sampled points in z directions time window [z-N, z+N]；(x1, y1) represents current single track data
X, y-coordinate, (x2, y2) represents the x of adjacent single track data, y-coordinate；The constant that N pre-sets for user.
The beneficial effects of the invention are as follows：Seed point using same layer position has good correlation in the horizontal direction,
Based on many track datas, noise spot filter operation is carried out, geological data noise can be eliminated, reach seismic volume data noise filtering effect
Fruit purpose enhanced with Lamellar character, after the feature enhancing of layer position, can reduce interference, using the teaching of the invention it is possible to provide more preferably for later tracing of horizons
Be available for analysis design sketch.
Brief description of the drawings
Fig. 1 is object plotting method flow chart of the invention；
Fig. 2 is the noise cancellation method flow chart based on density of the invention；
Fig. 3 illustrates for the extension of other extreme points around the extreme point in the noise cancellation method based on density of the present invention
Figure；
The sample single track schematic diagram data that Fig. 4 is extracted in the noise cancellation method based on density for the present invention；
Fig. 5 is the noise cancellation method flow chart based on coefficient correlation of the invention.
Embodiment
Below in conjunction with the accompanying drawings technical scheme is further illustrated with specific embodiment.
As shown in figure 1, geological data Lamellar character strengthens object plotting method, comprise the following steps：
S1, judge whether sampled point is extreme point；
If S2, sampled point are not extreme points, abandon the sampled point and continue to judge next sampled point；If sampled point is pole
It is worth point, then whether be noise spot, if not noise spot if determining whether the extreme point, then retains the extreme point and by the extreme value
Point-rendering is shown；If sampled point is noise spot, the noise spot is eliminated.
Further, the noise spot in described step S2 is eliminated using the noise cancellation method based on density or is based on
The noise cancellation method of coefficient correlation.
As shown in Fig. 2 the noise cancellation method concrete methods of realizing of the present invention based on density is：Noise spot be from
Scatterplot, is also impossible to large area even if noise spot is exactly extreme point, around it and extreme point occurs.Noise based on density is eliminated
Method utilizes this feature, when sampled point is extreme point, judges at least one single track data around the single track data where extreme point
The number of interior extreme point, if the number of surrounding extreme point is more than default threshold value, the extreme point is just judged to noise spot；Tool
Body includes following sub-step：
S211, the extreme value point data judged will be needed as origin, the extreme point a2 in single track data A in such as Fig. 3,
At least one single track data area is extended to surrounding around single track data A where origin a2, the single track data of sample are extracted, such as
B0~B11 in Fig. 4 is respectively the single track data around A；
S212, in each single track data, centered on origin, extract the sample point around origin, the b1 in such as Fig. 3~
B3 (being located in single track data B4), a1 (being located in single track data A), a3 (being located in single track data A), are counted in all sample points
The total number N of extreme point, if N is more than default threshold values MinPts, it is not noise spot to illustrate origin, and otherwise origin is noise
Point；
S213, elimination noise spot.
Further, judge in described step S213 origin whether be noise spot specific cost function g (x, y, z, k,
N it is) as follows：
Wherein：Represent judge origin whether be extreme point function；MinPts is a default threshold value,
It is a positive integer；K is default positive integer, generally equal to 1, but in order to eliminate saddle point, also can be any in the range of [1,5]
Value；
Wherein,
Or,
Value be 1, it is extreme point to represent current sample point, wherein, formula (2) represents that extreme point is very big
It is worth point, formula (3) represents that extreme point is minimum point, and 0 value represents non-extreme point；F (x, y, z) represents the width of current sampling point
Value, k values are the interval of sample point in a z-direction, are pre-set by user；
Formula (4) is that the number of surrounding extreme point, N represents that origin is sat in the case that calculating current sampling point is extreme point
X, y are marked, z direction extensions are the integers that a user pre-sets；
When certain origin eligible (2) or (3), as the number of extreme point is more than around extreme point, and the extreme point
During MinPts, cost value is g (x, y, z, k, N)=1, and the point is not noise spot, is otherwise noise spot.
Noise cancellation method principle of the present invention based on coefficient correlation is：Between earthquake volume data per pass data
Characteristic with height correlation, if there is noise spot, then correlation coefficient value necessarily declines, so when sampled point (x, y, z) is
During extreme point, linearly dependent coefficient of that track data with surrounding per track data belonging to the extreme point is counted, it is final statistically linear
Coefficient correlation Gao Zong roads number；As shown in figure 5, it is comprised the following steps that：
S221, selection have the single track data of extreme point, extract in the single track data voxel value a little；
S222, the voxel value of institute a little in remaining single track data is extracted, and calculating has the single track data voxel value of extreme point
With the linearly dependent coefficient between other each single track data voxel values；
S223, the size for judging linearly dependent coefficient and default threshold coefficient that S222 is obtained, if linear correlation system
Number is more than threshold coefficient, then two single track data coefficient correlations are high；
The high single track data total number of S224, the single track data coefficient correlation for counting and having extreme point, and judge this total
Whether number is more than default threshold value MinPts, if total number is more than threshold value MinPts, the extreme point in the single track data is not
It is noise spot, otherwise the extreme point is noise spot；
S225, eliminate the noise spot.
Further, whether it is that the cost function of noise spot is that the extreme point is judged in the step S224：
What ρ (x, y, z) was counted is the single track data and the high single track data of surrounding coefficient correlation described in current extreme value point
Total number, specific formula for calculation is as follows：
Wherein ω (x, y, z) represent have the single track data voxel value of extreme point and other each single track data voxel values it
Between linearly dependent coefficient function：
The threshold coefficient that α pre-sets for user；
γ is linearly dependent coefficient, and its computing formula is：
Wherein miCurrent single track data are represented in the z directions time window [z-N, z+N], the voxel value f of certain sampled point (x,
y,z+N)；M represents the average value of current single track data voxel value of all sampled points in z directions time window [z-N, z+N]；
niAdjacent single track data are represented in z directions time window [z-N, z+N], the voxel value of certain sampled point；N represents adjacent single track number
According to the voxel value average value of all sampled points in z directions time window [z-N, z+N]；The x of point inside each single track data,
Y-coordinate is identical, and z coordinate is different, and (x1, y1) represents the x of current single track data, and y-coordinate, (x2, y2) represents adjacent single track
The x of data, y-coordinate；The constant that N pre-sets for user.
One of ordinary skill in the art will be appreciated that embodiment described here is to aid in reader and understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such especially statement and embodiment.This area
Those of ordinary skill can make according to these technical inspirations disclosed by the invention various does not depart from the other each of essence of the invention
Plant specific deformation and combine, these deformations and combination are still within the scope of the present invention.
Claims (4)
1. geological data Lamellar character strengthens object plotting method, it is characterised in that comprise the following steps：
S1, judge whether sampled point is extreme point；
If S2, sampled point are not extreme points, abandon the sampled point and continue to judge next sampled point；If sampled point is extreme value
Point, then whether be noise spot, if not noise spot if determining whether the extreme point, then retain the extreme point and by the extreme point
Drafting is shown；If sampled point is noise spot, the noise spot is eliminated, noise spot is eliminated to be eliminated using the noise based on density
Method or the noise cancellation method based on coefficient correlation；
The described noise cancellation method principle based on coefficient correlation is：There is height phase between earthquake volume data per pass data
The characteristic of pass, if there is noise spot, then correlation coefficient value necessarily declines, so when sampled point (x, y, z) is extreme point, uniting
Linearly dependent coefficient of that track data with surrounding per track data belonging to the extreme point is counted, final statistically linear coefficient correlation is high
Total road number；The described noise cancellation method based on coefficient correlation is comprised the following steps that：
S221, selection have the single track data of extreme point, extract in the single track data voxel value a little；
S222, extract the voxel value of institute a little in remaining single track data, and calculating have extreme point single track data voxel value and its
Linearly dependent coefficient between his each single track data voxel value；
S223, the size for judging linearly dependent coefficient and default threshold coefficient that S222 is obtained, if linearly dependent coefficient is big
In threshold coefficient, then two single track data coefficient correlations are high；
The high single track data total number of S224, the single track data coefficient correlation for counting and having extreme point, and judge that the total number is
It is no to be more than default threshold value MinPts, if the extreme point that total number is more than in threshold value MinPts, the single track data is not made an uproar
Sound point, otherwise the extreme point is noise spot；
S225, eliminate the noise spot.
2. geological data Lamellar character according to claim 1 strengthens object plotting method, it is characterised in that it is described based on
The noise cancellation method concrete methods of realizing of density is：Judge at least one single track data around the single track data where extreme point
The number of interior extreme point, if the number of surrounding extreme point is more than default threshold value, the extreme point is just judged to noise spot；Tool
Body includes following sub-step：
S211, the extreme value point data that will need judgement extend at least one in the single track data where origin as origin to surrounding
Single track data area, extracts the single track data of sample；
S212, in each single track data, centered on origin, extract the sample point around origin, count in all sample points
The total number N of extreme point, if N is more than default threshold values MinPts, it is not noise spot to illustrate origin, is otherwise noise spot；
S213, elimination noise spot.
3. geological data Lamellar character according to claim 2 strengthens object plotting method, it is characterised in that described step
Judge in S213 origin whether be noise spot specific cost function g (x, y, z, k, N) it is as follows：
Wherein：Represent judge origin whether be extreme point function；MinPts is a default threshold value, is one
Individual positive integer；K is default positive integer；
Wherein,
Or,
Value be 1, it is extreme point to represent current sample point, wherein, formula (2) represents that extreme point is maximum point,
Formula (3) represents that extreme point is minimum point, and 0 value represents non-extreme point；F (x, y, z) represents the amplitude of current sampling point, k values
For the interval of sample point in a z-direction, pre-set by user；
Formula (4) is that the number of surrounding extreme point, N represents origin x in the case that calculating current sampling point is extreme point,
Y, z direction extension, are the integers that a user pre-sets；
When certain origin eligible (2) or (3), the number of extreme point is more than MinPts as around extreme point, and the extreme point
When, cost value is g (x, y, z, k, N)=1, and the point is not noise spot, otherwise eliminates the point.
4. geological data Lamellar character according to claim 1 strengthens object plotting method, it is characterised in that the step
Whether it is that the cost function of noise spot is that the extreme point is judged in S224：
That ρ (x, y, z) is counted is total of the single track data described in current extreme value point and the high single track data of surrounding coefficient correlation
Number, specific formula for calculation is as follows：
Wherein ω (x, y, z), which is represented, to be had between the single track data voxel value of extreme point and other each single track data voxel values
The function of linearly dependent coefficient：
The threshold coefficient that α pre-sets for user；
γ is linearly dependent coefficient, and its computing formula is：
Wherein miCurrent single track data are represented in z directions time window [z-N, z+N], voxel value f (x, y, the z+ of certain sampled point
N)；Represent the average value of current single track data voxel value of all sampled points in z directions time window [z-N, z+N]；niTable
Show adjacent single track data in z directions time window [z-N, z+N], the voxel value of certain sampled point；Represent adjacent single track data
The voxel value average value of all sampled points in z directions time window [z-N, z+N]；(x1, y1) represents current single track data
X, y-coordinate, (x2, y2) represents the x of adjacent single track data, y-coordinate；The constant that N pre-sets for user.
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