GeostatsPy - Geostatistical Analysis
Quick Reference
python
import geostatspy.GSLIB as GSLIB
import geostatspy.geostats as geostats
import pandas as pd
df = pd.read_csv('data.csv')
df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity') # Transform
lag, gamma, npairs = geostats.gamv(df, 'X', 'Y', 'npor', # Variogram
tmin=-9999, tmax=9999, xlag=50, xltol=25, nlag=15,
azm=0, atol=22.5, bandwh=9999, bandwd=9999)
vario = GSLIB.make_variogram(nug=0.0, nst=1, it1=1, cc1=1.0, # Model
azi1=0, hmaj1=300, hmin1=300)
est, var = geostats.kb2d(df, 'X', 'Y', 'npor', ..., vario=vario) # Krige
Key Functions
| Category | Functions |
|---|---|
| Visualization | locmap, pixelplt, hist |
| Variogram | gamv, vmodel |
| Kriging | kb2d, kb3d |
| Simulation | sgsim, sisim |
| Transforms | nscore, backtr |
| Declustering | declus |
Common Workflows
1. Normal Score Transform
python
df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity') original = geostats.backtr(nscore_data, tvpor, tnspor, zmin=0, zmax=0.3)
2. Experimental Variogram
python
lag, gamma, npairs = geostats.gamv(
df, 'X', 'Y', 'npor',
tmin=-9999, tmax=9999, # Trimming limits
xlag=50, xltol=25, # Lag distance, tolerance
nlag=15, azm=0, atol=22.5, bandwh=9999, bandwd=9999)
3. Variogram Model
python
# Types: 1=spherical, 2=exponential, 3=gaussian
vario = GSLIB.make_variogram(
nug=0.0, nst=1, # Nugget, number of structures
it1=1, cc1=1.0, # Type, sill contribution
azi1=0, hmaj1=300, hmin1=300) # Azimuth, major/minor range
4. Kriging (kb2d)
python
est, var = geostats.kb2d(
df, 'X', 'Y', 'npor', tmin=-9999, tmax=9999,
nx=50, xmn=25, xsiz=50, # Grid X: ncells, origin, size
ny=50, ymn=25, ysiz=50, # Grid Y
nxdis=1, nydis=1, ndmin=1, ndmax=10,
radius=500, ktype=0, skmean=0.0, vario=vario) # ktype: 0=simple, 1=ordinary
5. Sequential Gaussian Simulation
python
sim = geostats.sgsim(
df, 'X', 'Y', 'npor', wcol=-1, scol=-1,
tmin=-9999, tmax=9999, itrans=0,
ismooth=0, dession=0, dmession=0,
zmin=-4, zmax=4, ltail=1, ltpar=0, utail=1, utpar=0,
nsim=1, nx=50, xmn=25, xsiz=50, ny=50, ymn=25, ysiz=50,
nz=1, zmn=0, zsiz=1, seed=73073,
ndmin=1, ndmax=10, nodmax=10, radius=500, radius1=500,
sang1=0, sang2=0, sang3=0, mxctx=10, mxcty=10, mxctz=1,
ktype=0, vario=vario)
6. Declustering
python
wts, cell_size, ncut = geostats.declus(
df, 'X', 'Y', 'porosity', iminmax=1, noff=10, ncell=20, cmin=10, cmax=500)
declustered_mean = np.average(df['porosity'], weights=wts)
Variogram Models
| Code | Model | Use Case |
|---|---|---|
| 1 | Spherical | Most common, finite range |
| 2 | Exponential | Reaches sill asymptotically |
| 3 | Gaussian | Very smooth, parabolic near origin |
| 4 | Power | Unbounded, fractal-like |
Key Parameters
| Parameter | Description |
|---|---|
nug | Nugget effect (measurement error + micro-scale variation) |
sill | Total variance (nugget + structure contributions) |
range | Distance where correlation becomes negligible |
azimuth | Direction of maximum continuity (degrees from N) |
ktype | 0=simple kriging (known mean), 1=ordinary kriging |
Tips
- •Always transform to normal scores - Most methods assume Gaussian
- •Start isotropic - Add anisotropy only if justified by directional variograms
- •Check variogram pairs - Ensure enough pairs at each lag
- •Multiple realizations - Use 50-100+ for uncertainty quantification
- •Back-transform last - Apply to final results only
References
- •GSLIB Programs - GSLIB program reference
- •Simulation Methods - SGSIM, SISIM, and other methods
Scripts
- •scripts/kriging_example.py - Complete kriging workflow