CN110413712A - A kind of fault plane automatic identifying method based on DEM - Google Patents
A kind of fault plane automatic identifying method based on DEM Download PDFInfo
- Publication number
- CN110413712A CN110413712A CN201910500373.8A CN201910500373A CN110413712A CN 110413712 A CN110413712 A CN 110413712A CN 201910500373 A CN201910500373 A CN 201910500373A CN 110413712 A CN110413712 A CN 110413712A
- Authority
- CN
- China
- Prior art keywords
- triangular facet
- fault plane
- density
- point
- triangular
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 23
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 claims abstract description 10
- 238000003860 storage Methods 0.000 claims description 8
- 238000003325 tomography Methods 0.000 claims description 7
- BAINIUMDFURPJM-UHFFFAOYSA-N oxatomide Chemical compound O=C1NC2=CC=CC=C2N1CCCN(CC1)CCN1C(C=1C=CC=CC=1)C1=CC=CC=C1 BAINIUMDFURPJM-UHFFFAOYSA-N 0.000 claims description 3
- 108090000623 proteins and genes Proteins 0.000 claims description 3
- 238000013519 translation Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 238000011835 investigation Methods 0.000 abstract description 4
- 238000000605 extraction Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of fault plane automatic identifying method based on DEM, specific steps include: that dem data is switched to TIN triangular facet by (1), carry out the gradient to triangular facet and range is screened；(2) occurrence consistency and propinquity based on triangular facet carry out the category division of triangular facet with recursive algorithm；(3) density processing in space is carried out to the triangular facet after division, the three-dimensional coordinate point after obtaining density；(4) it is based on three-dimensional coordinate point set, generates fitting fault plane, and then generate fault plane fiducial confidence ellipse；(5) invalid fault plane is rejected, generates fault plane element.Compared with prior art, the present invention the degree of automation is higher, extraction effect is good, almost the same with field investigation result.
Description
Technical field
The present invention relates to geographical information technology application field more particularly to a kind of fault plane automatic identification sides based on DEM
Method.
Background technique
Fault plane refers to the triangle cliff that fault escarpment is formed after river or coombe cutting corrode.Tomography is to areal geology
The development of construction has great influence, and also has decisive role, therefore tomography to the distribution of petroleum, natural gas and underground water
The identification in face is very important link in geological exploration.
Most fault interpretation work is to have been parsed manually based on field investigation or remote sensing image by parsing personnel at present
At.The mode duty cycle of this manual interpretation is longer, parsing result it is subjective, quality is difficult to ensure, and is difficult to
Handle complicated tomography plane system.Therefore, it realizes the semi-automatic or full-automatic identification of tomography, and establishes and break from three-dimensional angle
FEM layer model shows fault structure, has important research significance and practical value.
Summary of the invention
Goal of the invention: in view of the problems of the existing technology the present invention, provides a kind of fault plane automatic identification based on DEM
Method can automatically and quickly extract fault plane, and can show fault structure based on tomography surface model.
Technical solution: the fault plane automatic identifying method of the present invention based on DEM includes:
(1) it will include that the dem data in region to be identified is converted to TIN triangle network data, and according to setting boundary and slope
Degree threshold value R1 is screened, and the triangular facet set S that the gradient in region to be identified is greater than R1 is obtained；
(2) according to occurrence discrepancy threshold R2 and distance difference threshold value R3 is preset, triangular facet set S, which is divided into, multiple to be had
The triangular facet cluster of different occurrences, and store to triangular facet gathering and close in ClassifyS；
(3) it reads triangular facet gathering and closes any one triangular facet cluster cs in ClassifySi, to csiIn all triangular facets
Density processing is carried out, the density point set P containing XY coordinate is obtainedi；
(4) for set PiMiddle each point searches the height value of the point in dem data and is assigned to the Z coordinate value of the point, raw
At the density point set P3d containing XYZ coordinatei；
(5) density point set P3d is usedi, it is fitted and obtains fault plane platei；
(6) according to fitting fault plane plateiWith density point set P3di, generate fiducial confidence ellipse fault plane eplatei；
(7) circulation executes step (3) to (6), until completing triangular facet gathering closes all triangular facet clusters in ClassifyS
Processing, and the storage of all fitting fault planes is arrived all fiducial confidence ellipse fault plane storages into fitting fault plane set Plate
In fiducial confidence ellipse fault plane set Eplate；
(8) it is based on preset area threshold value, the invalid fault plane in set Plate and set Eplate is rejected, using rejecting
Set Plate and set Eplate afterwards generates three-dimension disclocation face element.
Further, step (1) specifically includes:
(1-1) for include region to be identified dem data, according to setting boundary threshold edge_limit generate wait know
Other regional scope Region；
(1-2) is based on GIS data translation interface, and dem data is converted to TIN triangle network data；
(1-3) traverses each triangular facet in TIN triangle network data, and according to gradient threshold value R1, rejects the gradient and be less than R1
Or the not triangular facet in range Region, the triangular facet set S after being screened.
Further, step (2) specifically includes:
(2-1) reads any one triangular facet s in triangular facet set St, based on occurrence discrepancy threshold between setting triangular facet
R2, distance difference threshold value R3 search triangular facet stSimilar triangular facet, i.e., will be with triangular facet stBetween occurrence difference be less than R2 and distance
Similar triangular facet less than R3 is added in set T；
(2-2) is again based on threshold value R2, R3 and carries out similar triangular facet lookup for each of set T triangular facet, and
The similar triangular facet found is added in set T, until completing the lookup of all triangular facets in T；
(2-3) is by triangular facet stAnd all similar triangular facets in triangular facet set T, it is added in a triangular facet cluster；
(2-4) circulation step (2-1) to (2-3), until completing all triangular facet s in TIN set StProcessing, and will place
It manages result storage and closes ClassifyS={ cs to triangular facet gatheringi| i=0 ..., m } in, csiIndicate that i-th of triangular facet cluster, m are
The quantity of triangular facet cluster.
Further, step (3) specifically includes:
(3-1) reads triangular facet gathering and closes any one triangular facet cluster cs in ClassifySi；
(3-2) is for triangular facet cluster csiIn any one triangular facet, based on setting density threshold value R4 in triangular facet most
Short side se carries out a density processing；
(3-3) by se by all density points for handling of point density respectively with while se to angular vertex line,
And density is carried out to all lines based on density threshold value R4 and is handled, all density that will be handled in the se of side by point density
Point set P is written in the XY coordinate for all density points that point and line are handled by point densityiIn；
(3-4) circulation step (3-2) to (3-3), until completing triangular facet cluster csiIn all triangular facets density processing；
(3-5) circulation step (3-1) to (3-4), until completing the density processing of all triangular facet clusters in ClassifyS.
Further, step (5) specifically includes:
(5-1) is from density point set P3di3 points of middle random selection fit a fault plane platei: Ax+By+Cz+D
=0, A, B, C, D are plane parameter in formula；
(5-2) is to fault plane plateiRepresentative degree analyzed, i.e. set of computations P3diIn all the points to plane
Distance is less than the ratio tempRatio of given threshold R5；
(5-3) determines fault plane plate if tempRatio is greater than goal-selling and is fitted than threshold value RatioiIt can use, hold
Row step (6)；Otherwise (5-1) is returned to step, until reaching maximum number of iterations max_iteration.
Further, step (6) specifically includes:
(6-1) reads set Pi, set of computations P according to the following formulaiCenter point coordinate valueCentralization with each point is sat
Mark
N is set P in formulaiPoints, xk、ykRespectively indicate set PiIn k-th point of X, Y coordinates value, It indicates
Set PiIn X, Y coordinates value after k-th of dot center；
(6-2) is according to set PiThe centralization coordinate of each pointThe side clockwise of fiducial confidence ellipse is calculated according to the following formula
To rotation angle θ:
(6-3) will set PiThe centralization coordinate of each pointθ is rotated clockwise, after obtaining each point centralization and rotating
Coordinate
(6-4) is according to each point centralization and postrotational coordinateX, the side Y of fiducial confidence ellipse are calculated separately using following formula
To standard deviation sigma1、σ2:
(6-5) calculates the major semiaxis length a of fiducial confidence ellipse and short according to the 3-sigma principle of 2-D data according to the following formula
Half shaft length b:
Numerical value 4.24 is the 3-sigma Dynamic gene value of 2-D data in formula；
(6-6) obtains plane fiducial confidence ellipse side according to the major semiaxis a, semi-minor axis length b and rotation angle θ of fiducial confidence ellipse
Journey, in conjunction with fitting fault plane platei, generate the fiducial confidence ellipse fault plane eplate determined by following equations groupi:
Change the equation of fiducial confidence ellipse, x in formula centered on equation (1)0、y0Centered on change the X of fiducial confidence ellipse, Y axis coordinate becomes
Amount, equation (2), (3), the three-dimensional coordinate x that (4) are fiducial confidence ellipse fault plane1、y1、z1Expression formula, A, B, C, D are fitting fault plane
plateiEquation Ax+By+Cz+D=0 in parameter.
Further, step (8) specifically includes:
(8-1) reads any one fiducial confidence ellipse fault plane eplate in fiducial confidence ellipse fault plane set Eplatei；
(8-2) is if eplateiArea be less than preset area threshold value A rea, then by eplateiWith corresponding fitting fault plane
platei, rejected from corresponding set；
(8-3) circulation executes step (8-1) to (8-2), until completing to own in fiducial confidence ellipse fault plane set Eplate
The processing of fault plane；
(8-4) is using the fitting fault plane set Plate and fiducial confidence ellipse fault plane set after rejecting invalid fault plane
Eplate generates fault plane element respectively.
The utility model has the advantages that compared with prior art, the present invention its remarkable advantage is: the present invention can be extracted automatically and quickly
Fault plane out, and fault structure can be shown based on tomography surface model, and execution efficiency is higher, extraction effect is good, with field
Investigation result is almost the same.
Detailed description of the invention
Fig. 1 is the flow chart of one embodiment of the present of invention；
Fig. 2 is Mount Lushan DEM and mountain peak distribution map；
Fig. 3 is Mount Lushan DEM and division triangle mapping；
Fig. 4 is density method schematic diagram；
Fig. 5 is using the density effect picture after density of the present invention；
Fig. 6 is the point group fit Plane schematic diagram obtained using present invention fitting；
Fig. 7 is fiducial confidence ellipse fit procedure schematic diagram in the present invention；
Fig. 8 is the fiducial confidence ellipse fitting result chart obtained using the present invention；
Fig. 9 is the fault plane fiducial confidence ellipse flat distribution map obtained using the present invention；
Figure 10 is the fault plane flat distribution map obtained using the present invention；
Figure 11 is the fault plane fiducial confidence ellipse plane obtained using the present invention and mountain peak distribution map；
Figure 12 is the fault plane 3D fiducial confidence ellipse expression figure obtained using the present invention.
Specific embodiment
A kind of fault plane automatic identifying method based on DEM is present embodiments provided, as shown in Figure 1, specifically including following
Step:
Step 1: will include that the dem data in region to be identified is converted to TIN triangle network data, and according to setting boundary and
Gradient threshold value R1 is screened, and the triangular facet set S that the gradient in region to be identified is greater than R1 is obtained.
Wherein, the experimental data of the present embodiment is using In The Lushan Area 5m resolution ratio dem data, as shown in Fig. 2, projection
For WGS84 Mercator projection, XY offset is respectively -12888513.279990086m, -3402357.4105560426m.
Step 1 specifically includes:
(1-1) for include region to be identified dem data, according to setting boundary threshold edge_limit generate wait know
Other regional scope Region；In the present embodiment, edge_limit 2000m, four vertex for generating Region are respectively
(12707.5,12667.5), (12707.5,30032.5), (29692.5,30032.5), (29692.5,12667.5)；
(1-2) is based on GIS data translation interface, and dem data is converted to TIN triangle network data；It is used in the present embodiment
ArcMap10.6 is converted；
(1-3) traverses each triangular facet in TIN triangle network data, and according to gradient threshold value R1, rejects the gradient and be less than R1
Or the not triangular facet in range Region, the triangular facet set S after being screened.R1 is set as 45.13 ° in embodiment, sieve
Triangular facet set S intermediate cam face number after choosing is 581.
Step 2: according to default occurrence discrepancy threshold R2 and distance difference threshold value R3, triangular facet set S being divided into multiple
Triangular facet cluster with different occurrences, and store to triangular facet gathering and close in ClassifyS.
The step specifically includes:
(2-1) reads any one triangular facet s in triangular facet set St, based on occurrence discrepancy threshold between setting triangular facet
R2, distance difference threshold value R3 search triangular facet stSimilar triangular facet, i.e., will be with triangular facet stBetween occurrence difference be less than R2 and distance
Similar triangular facet less than R3 is added in set T.R2 is set as 50 ° in the present embodiment, and R3 is set as 100m；
(2-2) is again based on threshold value R2, R3 and carries out similar triangular facet lookup for each of set T triangular facet, and
The similar triangular facet found is added in set T, until completing the lookup of all triangular facets in T；
(2-3) is by triangular facet stAnd all similar triangular facets in triangular facet set T, it is added in a triangular facet cluster；
(2-4) circulation step (2-1) to (2-3), until completing all triangular facet s in TIN set StProcessing, and will place
It manages result storage and closes ClassifyS={ cs to triangular facet gatheringi| i=0 ..., m } in, csiIndicate that i-th of triangular facet cluster, m are
The quantity of triangular facet cluster.M is 79 in the present embodiment, and the triangular facet after division is as shown in Figure 3.
Step 3: reading triangular facet gathering and close any one triangular facet cluster cs in ClassifySi, to csiIn all triangles
Face carries out density processing, obtains the density point set P containing XY coordinatei。
The step specifically includes:
(3-1) reads triangular facet gathering and closes any one triangular facet cluster cs in ClassifySi；
(3-2) is for triangular facet cluster csiIn any one triangular facet, based on setting density threshold value R4 in triangular facet most
Short side se carries out a density processing；R4 is set as 10m in the present embodiment, i.e., increases a point every 10m and realize density processing；
(3-3) by se by all density points for handling of point density respectively with while se to angular vertex line,
And density is carried out to all lines based on density threshold value R4 and is handled, all density that will be handled in the se of side by point density
Point set P is written in the XY coordinate for all density points that point and line are handled by point densityiIn；
(3-4) circulation step (3-2) to (3-3), until completing triangular facet cluster csiIn all triangular facets density processing；
(3-5) circulation step (3-1) to (3-4), until completing the density processing of all triangular facet clusters in ClassifyS.
Density handles specific method, and as shown in figure 4, carrying out density to certain part in the present embodiment, treated that effect is as shown in Figure 5.
Step 4: for set PiMiddle each point searches the height value of the point in dem data and is assigned to the Z coordinate of the point
Value generates the density point set P3d containing XYZ coordinatei。
Step 5: using density point set P3di, it is fitted and obtains fault plane platei。
The step specifically includes:
(5-1) is from density point set P3di3 points of middle random selection fit a fault plane platei: Ax+By+Cz+D
=0, A, B, C, D are plane parameter in formula；Plate is obtained in the present embodiment for the first time1Expression formula are as follows: -4.2652231*10-5x-
1.53380836*10-5y+3.97838462*10-5Z+0.999999998=0；
(5-2) is to fault plane plateiRepresentative degree analyzed, i.e. set of computations P3diIn all the points to plane
Distance is less than the ratio tempRatio of given threshold R5, and it is 0.645 that P3di obtains tempRatio for the first time in the present embodiment；
(5-3) determines fault plane plate if tempRatio is greater than goal-selling and is fitted than threshold value RatioiIt can use, hold
Row step (6)；Otherwise (5-1) is returned to step, until reaching maximum number of iterations max_iteration.In the present embodiment
Ratio is set as 0.8, max_iteration and is set as 100, and the fitting example of a point group is as shown in Figure 6.
Step 6: according to fitting fault plane plateiWith density point set P3di, generate fiducial confidence ellipse fault plane eplatei。
The step specifically includes:
(6-1) reads set Pi, set of computations P according to the following formulaiCenter point coordinate valueCentralization with each point is sat
Mark
N is set P in formulaiPoints, xk、ykRespectively indicate set PiIn k-th point of X, Y coordinates value, It indicates
Set PiIn X, Y coordinates value after k-th of dot center；It is obtained for the first time in the present embodiment
(6-2) is according to set PiThe centralization coordinate of each pointThe clockwise direction of fiducial confidence ellipse is calculated according to the following formula
Rotation angle θ:
θ=- 0.200107rad is obtained in the present embodiment for the first time；
(6-3) will set PiThe centralization coordinate of each pointθ is rotated clockwise, after obtaining each point centralization and rotating
Coordinate
(6-4) is according to each point centralization and postrotational coordinateX, the side Y of fiducial confidence ellipse are calculated separately using following formula
To standard deviation sigma1、σ2:
σ is obtained in the present embodiment for the first time1=9.988793, σ2=50.0050135；
(6-5) calculates the major semiaxis length a of fiducial confidence ellipse and short according to the 3-sigma principle of 2-D data according to the following formula
Half shaft length b:
Numerical value 4.24 is the 3-sigma Dynamic gene value of 2-D data in formula；A=is obtained in the present embodiment for the first time
84.704965 b=424.042515；
(6-6) obtains plane fiducial confidence ellipse side according to the major semiaxis a, semi-minor axis length b and rotation angle θ of fiducial confidence ellipse
Journey, in conjunction with fitting fault plane platei, generate the fiducial confidence ellipse fault plane eplate determined by following equations groupi:
Change the equation of fiducial confidence ellipse, x in formula centered on equation (1)0、y0Centered on change the X of fiducial confidence ellipse, Y axis coordinate becomes
Amount, equation (2), (3), the three-dimensional coordinate x that (4) are fiducial confidence ellipse fault plane1、y1、z1Expression formula, A, B, C, D are fitting fault plane
plateiEquation Ax+By+Cz+D=0 in parameter.
Eplate is obtained in the present embodiment for the first time1Expression formula are as follows:
Fiducial confidence ellipse fit procedure is as shown in fig. 7, a fiducial confidence ellipse fitting effect is as shown in Figure 8 in the present embodiment.
Step 7: circulation executes step 3~6, until completing triangular facet gathering closes all triangular facet clusters in ClassifyS
Processing, and the storage of all fitting fault planes is arrived all fiducial confidence ellipse fault plane storages into fitting fault plane set Plate
In fiducial confidence ellipse fault plane set Eplate.
Step 8: being based on preset area threshold value, reject the invalid fault plane in set Plate and set Eplate, using picking
Set Plate and set Eplate after removing generate three-dimension disclocation face element.
The step specifically includes:
(8-1) reads any one fiducial confidence ellipse fault plane eplate in fiducial confidence ellipse fault plane set Eplatei；
(8-2) is if eplateiArea be less than preset area threshold value A rea, then by eplateiWith corresponding fitting fault plane
platei, rejected from corresponding set；Area is set as 50000m in the present embodiment2；
(8-3) circulation executes step (8-1) to (8-2), until completing to own in fiducial confidence ellipse fault plane set Eplate
The processing of fault plane；
(8-4) is using the fitting fault plane set Plate and fiducial confidence ellipse fault plane set after rejecting invalid fault plane
Eplate generates fault plane element respectively.
The present embodiment interrupts level fiducial confidence ellipse plane expression as shown in figure 9, fault plane plane expression is as shown in Figure 10, breaks
As shown in figure 11, the expression of fiducial confidence ellipse fault plane is as shown in figure 12 for level fiducial confidence ellipse plane and mountain peak distribution.As can be seen that disconnected
Level extracts result and the field investigation result of fault plane is almost the same.
Above disclosed is only a preferred embodiment of the present invention, and the right model of the present invention cannot be limited with this
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (7)
1. a kind of fault plane automatic identifying method based on DEM, it is characterised in that this method comprises:
(1) it will include that the dem data in region to be identified is converted to TIN triangle network data, and according to setting boundary and gradient threshold
Value R1 is screened, and the triangular facet set S that the gradient in region to be identified is greater than R1 is obtained；
(2) according to occurrence discrepancy threshold R2 and distance difference threshold value R3 is preset, triangular facet set S is divided into multiple with difference
The triangular facet cluster of occurrence, and store to triangular facet gathering and close in ClassifyS；
(3) it reads triangular facet gathering and closes any one triangular facet cluster cs in ClassifySi, to csiIn all triangular facets carry out
Density processing, obtains the density point set P containing XY coordinatei；
(4) for set PiMiddle each point searches the height value of the point in dem data and is assigned to the Z coordinate value of the point, and generation contains
There is the density point set P3d of XYZ coordinatei；
(5) density point set P3d is usedi, it is fitted and obtains fault plane platei；
(6) according to fitting fault plane plateiWith density point set P3di, generate fiducial confidence ellipse fault plane eplatei；
(7) circulation executes step (3)~(6), until completing the place that triangular facet gathering closes all triangular facet clusters in ClassifyS
Reason, and the storage of all fitting fault planes is stored all fiducial confidence ellipse fault planes to setting into fitting fault plane set Plate
Believe in oval fault plane set Eplate；
(8) it is based on preset area threshold value, the invalid fault plane in set Plate and set Eplate is rejected, after rejecting
Set Plate and set Eplate generates three-dimension disclocation face element.
2. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (1) is specifically wrapped
It includes:
(1-1) for include region to be identified dem data, area to be identified is generated according to setting boundary threshold edge_limit
Domain range Region；
(1-2) is based on GIS data translation interface, and dem data is converted to TIN triangle network data；
(1-3) traverses each triangular facet in TIN triangle network data, and according to gradient threshold value R1, the rejecting gradient is less than R1 or not
Triangular facet in range Region, the triangular facet set S after being screened.
3. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (2) is specifically wrapped
It includes:
(2-1) reads any one triangular facet s in triangular facet set St, based on setting triangular facet between occurrence discrepancy threshold R2, away from
Triangular facet s is searched from discrepancy threshold R3tSimilar triangular facet, i.e., will be with triangular facet stBetween occurrence difference be less than R2 and distance and be less than
The similar triangular facet of R3 is added in set T；
(2-2) is again based on threshold value R2, R3 and carries out similar triangular facet lookup for each of set T triangular facet, and will look into
The similar triangular facet found is added in set T, until completing the lookup of all triangular facets in T；
(2-3) is by triangular facet stAnd all similar triangular facets in triangular facet set T, it is added in a triangular facet cluster；
(2-4) circulation step (2-1) to (2-3), until completing all triangular facet s in TIN set StProcessing, and will processing knot
Fruit storage closes ClassifyS={ cs to triangular facet gatheringi| i=0 ..., m } in, csiIndicate that i-th of triangular facet cluster, m are triangle
The quantity of face cluster.
4. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (3) is specifically wrapped
It includes:
(3-1) reads triangular facet gathering and closes any one triangular facet cluster cs in ClassifySi；
(3-2) is for triangular facet cluster csiIn any one triangular facet, based on setting density threshold value R4 to most short side in triangular facet
Se carries out a density processing；
(3-3) by se by all density points for handling of point density respectively with while se to angular vertex line, and base
Density is carried out to all lines in density threshold value R4 to handle, by all density points handled in the se of side by point density with
Point set P is written in the XY coordinate for all density points that line is handled by point densityiIn；
(3-4) circulation step (3-2) to (3-3), until completing triangular facet cluster csiIn all triangular facets density processing；
(3-5) circulation step (3-1) to (3-4), until completing the density processing of all triangular facet clusters in ClassifyS.
5. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (5) is specifically wrapped
It includes:
(5-1) is from density point set P3di3 points of middle random selection fit a fault plane platei: Ax+By+Cz+D=0,
A, B, C, D are plane parameter in formula；
(5-2) is to fault plane plateiRepresentative degree analyzed, i.e. set of computations P3diIn all the points to plane distance
Ratio tempRatio less than given threshold R5；
(5-3) determines fault plane plate if tempRatio is greater than goal-selling and is fitted than threshold value RatioiIt can use, execute step
Suddenly (6)；Otherwise (5-1) is returned to step, until reaching maximum number of iterations max_iteration.
6. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (6) is specifically wrapped
It includes:
(6-1) reads set Pi, set of computations P according to the following formulaiCenter point coordinate valueWith the centralization coordinate of each point
N is set P in formulaiPoints, xk、ykRespectively indicate set PiIn k-th point of X, Y coordinates value, Indicate set
PiIn X, Y coordinates value after k-th of dot center；
(6-2) is according to set PiThe centralization coordinate of each pointBeing rotated clockwise for fiducial confidence ellipse is calculated according to the following formula
Angle θ:
(6-3) will set PiThe centralization coordinate of each pointθ is rotated clockwise, each point centralization and postrotational seat are obtained
Mark
(6-4) is according to each point centralization and postrotational coordinateX, the Y-direction mark of fiducial confidence ellipse are calculated separately using following formula
Quasi- difference σ1、σ2:
(6-5) calculates the major semiaxis length a and semi-minor axis of fiducial confidence ellipse according to the 3-sigma principle of 2-D data according to the following formula
Length b:
Numerical value 4.24 is the 3-sigma Dynamic gene value of 2-D data in formula；
(6-6) obtains plane fiducial confidence ellipse equation according to the major semiaxis a, semi-minor axis length b and rotation angle θ of fiducial confidence ellipse, then
In conjunction with fitting fault plane platei, generate the fiducial confidence ellipse fault plane eplate determined by following equations groupi:
Change the equation of fiducial confidence ellipse, x in formula centered on equation (1)0、y0Centered on change fiducial confidence ellipse X, Y axis coordinate variable, side
Journey (2), (3), the three-dimensional coordinate x that (4) are fiducial confidence ellipse fault plane1、y1、z1Expression formula, A, B, C, D are fitting fault plane
plateiEquation Ax+By+Cz+D=0 in parameter.
7. the fault plane automatic identifying method according to claim 1 based on DEM, it is characterised in that: step (8) is specifically wrapped
It includes:
(8-1) reads any one fiducial confidence ellipse fault plane eplate in fiducial confidence ellipse fault plane set Eplatei；
(8-2) is if eplateiArea be less than preset area threshold value A rea, then by eplateiWith corresponding fitting fault plane
platei, rejected from corresponding set；
(8-3) circulation executes step (8-1) to (8-2), until completing all tomographies in fiducial confidence ellipse fault plane set Eplate
The processing in face；
(8-4) uses the fitting fault plane set Plate and fiducial confidence ellipse fault plane set Eplate after rejecting invalid fault plane,
Fault plane element is generated respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910500373.8A CN110413712B (en) | 2019-06-11 | 2019-06-11 | DEM-based fault plane automatic identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910500373.8A CN110413712B (en) | 2019-06-11 | 2019-06-11 | DEM-based fault plane automatic identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110413712A true CN110413712A (en) | 2019-11-05 |
| CN110413712B CN110413712B (en) | 2021-09-28 |
Family
ID=68358950
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910500373.8A Active CN110413712B (en) | 2019-06-11 | 2019-06-11 | DEM-based fault plane automatic identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110413712B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5136552A (en) * | 1990-10-24 | 1992-08-04 | Amoco Corporation | Metod of geophysical exploration |
| EP1810194A1 (en) * | 2004-11-09 | 2007-07-25 | MAZERY, Patrick | Visualization of the results of a 3d by 2d stratum search engine |
| CN102567702A (en) * | 2010-12-08 | 2012-07-11 | 中国科学院地理科学与资源研究所 | Method for automatically identifying valleys and ridge lines based on ChangE DEM (Dynamic Effect Model) data |
| CN106934357A (en) * | 2017-02-28 | 2017-07-07 | 南京师范大学 | A kind of automatic identifying method of parallel fault |
| CN107368839A (en) * | 2017-06-22 | 2017-11-21 | 南京师范大学 | A kind of extraction method of the fault plane based on DEM |
-
2019
- 2019-06-11 CN CN201910500373.8A patent/CN110413712B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5136552A (en) * | 1990-10-24 | 1992-08-04 | Amoco Corporation | Metod of geophysical exploration |
| EP1810194A1 (en) * | 2004-11-09 | 2007-07-25 | MAZERY, Patrick | Visualization of the results of a 3d by 2d stratum search engine |
| CN102567702A (en) * | 2010-12-08 | 2012-07-11 | 中国科学院地理科学与资源研究所 | Method for automatically identifying valleys and ridge lines based on ChangE DEM (Dynamic Effect Model) data |
| CN106934357A (en) * | 2017-02-28 | 2017-07-07 | 南京师范大学 | A kind of automatic identifying method of parallel fault |
| CN107368839A (en) * | 2017-06-22 | 2017-11-21 | 南京师范大学 | A kind of extraction method of the fault plane based on DEM |
Non-Patent Citations (2)
| Title |
|---|
| SANG-HO YUN: "Lineament extraction from DEM using drainage network", 《IEEE》 * |
| 仲浩宇: "基于三维混沌序列的DEM置乱与还原方法", 《测绘通报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110413712B (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2019201268B2 (en) | Segmentation of an image based on color and color differences | |
| CN110059694B (en) | Intelligent identification method for character data in complex scene of power industry | |
| Grilli et al. | A review of point clouds segmentation and classification algorithms | |
| CN110929607B (en) | Remote sensing identification method and system for urban building construction progress | |
| CN103870845B (en) | Novel K value optimization method in point cloud clustering denoising process | |
| CN100595782C (en) | Classification method for syncretizing optical spectrum information and multi-point simulation space information | |
| RU2603979C1 (en) | Identification and selection of layers of fluid and fluid-saturated formations of one or more geological solid masses representing geological structure | |
| CN110322464A (en) | A kind of small curvature thin-walled parts boundary extraction method based on three-dimensional point cloud | |
| CN106529469A (en) | Unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient | |
| CN104573705A (en) | Clustering method for building laser scan point cloud data | |
| CN107688201A (en) | Based on RBM earthquake prestack signal clustering methods | |
| CN110443862A (en) | Lithologic map filling method and system based on unmanned aerial vehicle and electronic equipment | |
| US20140058713A1 (en) | Seismic modeling system and method | |
| CN113223042B (en) | Intelligent acquisition method and equipment for remote sensing image deep learning sample | |
| CN106503047B (en) | A kind of image crawler optimization method based on convolutional neural networks | |
| CN104268581A (en) | Remote sensing sub-pixel map-making method based on integrated pixel level and sub-pixel level spatial correlation characteristics | |
| CN108629762A (en) | A kind of stone age evaluation and test model reduces the image pre-processing method and system of interference characteristic | |
| CN110672072A (en) | Information multivariate interpretation and characterization method for fractured rock mass structure | |
| CN114120094A (en) | Water pollution identification method and system based on artificial intelligence | |
| CN109344850A (en) | A kind of water meter automatic testing method based on YOLO | |
| CN116129262A (en) | Cultivated land suitability evaluation method and system for suitable mechanized transformation | |
| CN109255004A (en) | The online interaction formula method for visualizing of accuracy guarantee towards geographical spatial data | |
| CN110413712A (en) | A kind of fault plane automatic identifying method based on DEM | |
| CN109934906B (en) | Crude oil saturation acquisition method and device, computer equipment and storage medium | |
| CN106529601A (en) | Image classification prediction method based on multi-task learning in sparse subspace |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |