Variography#
import geolime as geo
dh_tom = geo.read_file("../data/dh_tom_comp.geo")
Once the capping value determined, we can create a new property to capp the value above 25.
dh_tom.set_property(name='Zn_cap', data='Zn_pct')
dh_tom.update_property(name='Zn_cap', data='25', region="(Zn_cap > 25)")
Normal Score#
For simplicity in variography analysis, let’s compute the normal scores of the data.
gauss_score function returns a numpy array, so let’s create a new property on the Drillholes.
geo.gauss_score(dh_tom['Zn_cap'])
2026-09-28 01:03:15,940 [WARNING] GeoLime Project - |GEOLIME|.anamorphosis.py : There are NaN in data. They will be ignored.
array([-0.78162595, nan, nan, ..., -0.7347981 ,
-0.97560843, -1.06290788], shape=(3761,))
dh_tom.set_property(name="Zn_gauss", data=geo.gauss_score(dh_tom['Zn_cap']))
2026-09-28 01:03:16,115 [WARNING] GeoLime Project - |GEOLIME|.anamorphosis.py : There are NaN in data. They will be ignored.
Experimental Analysis#
Lag Creation#
For Variography analysis, lag creation can be perfom
implicitly with a number of lags and a length of lag
explicitly with interval values provided.
lags, tol = geo.generate_lags_from_interval([[0, 10], [10, 20], [20, 40]])
lags, tol = geo.generate_lags(lag=40, plag=50, nlags=15)
lags
array([ 0, 40, 80, 120, 160, 200, 240, 280, 320, 360, 400, 440, 480,
520, 560])
tol
20.0
Variography Analysis is perform in 2 step:
finding the main direction
computing the semi variogram 1D in the major/semi-major/minor directions
The finding of the main direction is perform through the analysis of variomap is the different planes.
Analysis around the Z axis for the azimuth using
vario_map/vario_contour;Once found the main azimuth, analysis around the X axis for the dip using
vario_contour_dip;Once found the correspond azimuth and dip, analysis around the Z axis for the pitch using
vario_contour_pitch
Finding main direction#
geo.vario_map(
geo_object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
lags=lags,
tol=tol,
n_az=15,
atol=15,
backend=geo.GeostatsBackend.RUST,
rust_multithread=True
)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[10], line 9
5 lags=lags,
6 tol=tol,
7 n_az=15,
8 atol=15,
----> 9 backend=geo.GeostatsBackend.RUST,
10 rust_multithread=True
11 )
AttributeError: module 'geolime' has no attribute 'GeostatsBackend'
geo.vario_contour(
geo_object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
lags=lags,
tol=tol,
n_az=15,
atol=10,
user_azimuth=145,
backend=geo.GeostatsBackend.RUST,
rust_multithread=True
)
geo.vario_contour_dip(
geo_object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
lags=lags,
tol=tol,
azimuth=145,
n_dip=18,
atol=10,
user_dip=30,
c_step=None,
save_file=None,
backend=geo.GeostatsBackend.RUST,
rust_multithread=True
)
lags, tol = geo.generate_lags(lag=5, plag=50, nlags=15)
geo.vario_contour_pitch(
geo_object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
lags=lags,
tol=tol,
azimuth=145,
dip=30,
n_pitch=15,
atol=10,
user_pitch=40,
c_step=None,
save_file=None,
backend=geo.GeostatsBackend.RUST,
rust_multithread=True
)
Computing Major/Semi-major/minor 1D variograms#
lags, tol = geo.generate_lags(lag=40, plag=50, nlags=15)
vario_exp_major = geo.variogram(
object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
geographic_azimuth=145,
dip=30,
pitch=40,
lags=lags,
tol=tol,
atol=15
)
vario_exp_major
geo.plot_semivariogram(
variograms=[vario_exp_major],
display_npairs=True
)
vario_exp_semi_major = geo.variogram(
object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
geographic_azimuth=145,
dip=30,
pitch=-50,
lags=lags,
tol=tol,
atol=15
)
geo.plot_semivariogram(
variograms=[vario_exp_semi_major],
display_npairs=True
)
lags, tol = geo.generate_lags(lag=2, plag=50, nlags=15)
vario_exp_minor = geo.variogram(
object=dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
geographic_azimuth=145,
dip=-60,
pitch=40,
lags=lags,
tol=tol,
atol=30
)
geo.plot_semivariogram(
variograms=[vario_exp_minor],
display_npairs=True
)
Computing Downhole Variogram#
lags, tol = geo.generate_lags(lag=1., plag=50, nlags=20)
vario_exp_bh = geo.variogram_downhole(
object= dh_tom,
attribute="Zn_gauss",
region="Zn_gauss.notna()",
lags=lags,
tol=tol,
)
geo.plot_semivariogram(
variograms=[vario_exp_bh],
display_npairs=True
)
Automatic Fitting#
Automatic Fitting uses an empty model to fit along experimental variograms. Fitting is usually performed in 2 steps : first fit the nugget and then fit the other structures.
Fitting the Nugget#
nugget_model = geo.Nugget() + geo.Spherical()
The fitting algorithm can be set up to preferrentially fit the small ranges using the INVERSE_DISTANCE or SQUARED_INVERSE_DISTANCE.
geo.model_fit(
variograms=[vario_exp_bh],
cov=nugget_model,
weighting_method=geo.VarioFitWeightingMethod.SQUARED_INVERSE_DISTANCE
)
print(nugget_model)
Model and experimental variogram can be plotted on the same graphic by specifying the parameter model and model_angles. Note that for downhole varigram the model_angles values do not have any importance.
geo.plot_semivariogram(
variograms=[vario_exp_bh],
model=nugget_model,
model_angles=[{"azi":0, "dip":90, "pitch":90}],
display_npairs=True
)
Each structure component can be accessed using the cov_elem_list attribute.
nugget_model.cov_elem_list[0].sill
Fitting the structures#
model = geo.Nugget() + geo.Spherical() + geo.Spherical()
short_range_spherical_ratio = 0.6
long_range_spherical_ratio = 0.4
geo.model_fit(
variograms=[
vario_exp_major,
vario_exp_semi_major,
vario_exp_minor
],
cov=model,
constraints=[
{"sill_fixed":0.13},
{
"sill_max": short_range_spherical_ratio * (vario_exp_major.attrs["variable_var"] - 0.13),
"angle_fixed_0": 145,
"angle_fixed_1": 60,
"angle_fixed_2": 40,
"scale_max_0":100,
"scale_max_1":100,
"scale_max_2":20
},
{
"sill_max": long_range_spherical_ratio * (vario_exp_major.attrs["variable_var"] - 0.13),
"angle_fixed_0": 145,
"angle_fixed_1": 60,
"angle_fixed_2": 40,
}
]
)
geo.plot_semivariogram(
variograms=[vario_exp_major, vario_exp_semi_major, vario_exp_minor],
model=model,
model_angles=[
{"azi":145, "dip":60, "pitch":40},
{"azi":145, "dip":60, "pitch":-50},
{"azi":145, "dip":-30, "pitch":40},
],
display_npairs=True
)
print(model)
dh_tom.to_file("../data/dh_tom_comp_capped.geo")