This file is to demonstrate the use of various types of anisotropies in RGeostats through 2-D examples.
We first create a 2-D grid 100 x100 (square lag=1) called *grid*
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grid = db.create(nx=c(100,100))
We define an isotropic model (
model1) composed of a cubic basic structure with a range of 30 and a sill of 2. Using this model, we perform a non-conditional simulation that we visualize. Finally we calculate the experimental variogram along the two main axes of the grid (50 lags) and draw it together with the model
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model1 = model.create(vartype="Cubic",range=30,sill=2)
res1 = simtub(,grid,model1)
plot(res1,title="Isotropic")
- Grid_isotropic.png (30.22 KiB) Viewed 3538 times
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vario1 = vario.grid(res1,nlag=50)
plot(vario1)
plot(model1,vario=vario1,add=T)
- Vario_Isotropic.png (19.2 KiB) Viewed 3538 times
Now we create a model (
model2) with a cubic basic structure which follows a geometric anisotropy: the rotation of the main axis is 20 degrees (counted from east counterclockwise). The longest range is 30 and the shortest one is 10. Note that the directions for variogram calculations do not coincide with the anisotropy main directions: however the model fitting is reasonably correct.
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model2 = model.create(vartype="Cubic",range=c(30,10),sill=2,aniso.angles=20)
res2 = simtub(,grid,model2)
plot(res2,title="Geometric Anisotropy")
- Grid_Geometric.png (36.8 KiB) Viewed 3538 times
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vario2 = vario.grid(res2,nlag=50)
plot(vario2)
plot(model2,vario=vario2,add=T)
- Vario_Geometric.png (20.95 KiB) Viewed 3538 times
Now we create a model (
model3) with a isotropic cubic component (range of 30 and sill of 1) and a second cubic basic structure which follows a zonal anisotropy with range of 10 and a sill of 1 in the direction 90). The range in the perpendicular direction is set to a huge value (the zonal isotropy is turned into a vary long geometric anisotropy).
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model3 = model.create(vartype="Cubic",range=30,sill=1,aniso.angles=20)
model3 = model.create(vartype="Cubic",range=c(10,10000),sill=1,aniso.angles=90, model=model3)
res3 = simtub(,grid,model3)
plot(res3,title="Zonal Anisotropy")
- Zonal_Grid.png (30.53 KiB) Viewed 3538 times
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vario3 = vario.grid(res3,nlag=50)
plot(vario3)
plot(model3,vario=vario3,add=T)
- Zonal_Variogram.png (23.14 KiB) Viewed 3538 times
The last point is that the Automatic Fitting procedure is not able to fit a zonal model: nevertheless, it will possibly fit a model with a long geometric anisotropy.