Dear members of ai-geostats,
I am new in geostatistics and this is my first time to use GSTAT.
My knowledge realted to geostatistics came from Burrough's "principles of
GIS" and Issack's "Applied Geostatistics".
I have several questions about anisotropy modeling and I would be very
grateful if someone could help me.
I have an exhaustive dataset of 900*1000 grids with grid size 30 m
I want to use the GSTAT's unconditional simulation to generate random fields
that have similar spatial autocorrelation with my exhaustive dataset.
Here are my steps to generate such a random field:
1) log-transform my dataset since it is highly postively skewed.
2) Set the cutoff = 9000 and width = 30
3) Plot the omnidirectional experimental variogram and fit it.
It has three componets and can be fit by the following equation:
gamma (h) = 0.048 nug(0) + 0.324exp(1101)+ 0.086sph(247)
4) Plot directional experimental variograms of 36 directions with angle
tolerance 10 degree.
Here are the maximum partial range and partial sill for different
componets and total sill I found :
Orientation Partial Sill Partial Sill Range Partial Sill
Range Total
of Nug() of Exp() of Exp() of Sph()
of Exp() Sill
70 0.068 0.322 1604 0.093
413 0.483
30 0.073 0.282 1398 0.111
464 0.467
110 0.066 0.354 1333 0.063
280 0.482
40 0.054 0.289 1318 0.113
357 0.457
80 0.044 0.348 1577 0.100
268 0.491
Here are the minimum partial range and partial sill for different
componets and total sill I found :
Orientation Partial Sill Partial Sill Range Partial Sill
Range Total
of Nug() of Exp() of Exp() of Sph()
of Exp() Sill
170 0.040 0.323 726 0.066
170 0.430
170 0.040 0.323 726 0.066
170 0.430
30 0.073 0.282 1398 0.111
464 0.467
150 0.065 0.332 847 0.046
231 0.442
170 0.040 0.323 726 0.066
170 0.430
5) Perform unconditional simulation.
My questions are:
1) Is that make sense to log-transform my dataset? My intuition is that
since the result I get from
a unconditional simulation is normal distributed. So I shall provide a
spatial information comes from
normal distributed dataset. Is my thougt correct?
2) The nugget varies with different orientations. How can this happen?
(the nugget is omnidirectional as far as I know)
Shall I use the nugget from omnidirectional experimental variogram or
the average nugget from different orientations for the simulation?
3) The orientation of the maximum range for exp() and sph() componets is
different.
Is it correct that I model them independently? for example:
0.322 Exp(1604, 70, 0.47) + 0.11 Sph(464, 30, 0.53)
4) For geometry anisotropy,
the maximum and minimum ranges seems not perpendicular exactly to each
other.
How shall I determine the anisotropy ratio?
(I use the ratio of maximum range and range perpendicular to it)
5) I had read previous dissusions related to anisotropy modelling in
ai-geostats and gstat-info.
But I am afraid that I did not catch the points.
How shall I deal with the zonal anisotrpoy in combination with
geometry anisotropy?
Model it from total sill or model it for each componet independently?
for example: 0.032 Exp(133300, 20, 0.01) + 0.002 Sph(35700, 130, 0.01)
I have also attach my command file and hope you can correct me.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# gstat command file, Win32/Cygwin version 2.3.7 (12 July 2002)
# Fri Oct 18 04:41:45 2002
#
data(ln_zn_dummy): dummy, sk_mean=0, max=10;
variogram(ln_zn_dummy): 0.068 Nug(0) + 0.322 Exp(1604, 70, 0.47) + 0.11
Sph(464, 30, 0.53) +
0.032 Exp(133300, 20, 0.01) + 0.002
Sph(35700, 130, 0.01);
mask: 'mask';
method: gs; # Gaussian simulation instead of kriging
predictions(ln_zn_dummy): 'drandom';
variances(ln_zn_dummy): 'ran_van';
set nsim=100;
set cutoff = 9000;
set width = 30;
set fit = 2;
set output = 'errs.est';
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Thankyou.
Pei-Chun Chang
__________________________________________________
Pei-Chun Chang
Graduate Student
RS/GIS Laboratory tel: +81 (298) 53-4955, +81 (90) 4455-2475
Master's Program in Envionmental Sciences
University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
E-mail: [EMAIL PROTECTED]
__________________________________________________
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