geostatspy

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GSLIB-inspired geostatistics library for variogram analysis, kriging, and simulation. Use when Claude needs to: (1) Calculate experimental variograms, (2) Fit variogram models, (3) Perform simple/ordinary kriging, (4) Run sequential Gaussian simulation (SGSIM), (5) Apply normal score transforms, (6) Decluster spatial data, (7) Generate multiple realizations for uncertainty.

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Added on2026-05-16

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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 Operations

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

When to Use vs Alternatives

Use Case	Tool	Why
GSLIB-style workflows	GeostatsPy	Direct port of GSLIB programs to Python
SGSIM / SISIM simulation	GeostatsPy	Full GSLIB simulation engine
Declustering spatial data	GeostatsPy	Built-in `declus` function
Modern variogram API	scikit-gstat	Cleaner API, sklearn integration
Kriging only (no simulation)	pykrige	Focused API, universal kriging support
Random field generation	gstools	Flexible covariance models, field generation
Large-scale 3D geomodelling	SGeMS / Petrel	GUI-based, industrial workflows
Indicator simulation	GeostatsPy ( `sisim` )	Categorical property simulation

Choose GeostatsPy when: You need GSLIB-compatible workflows in Python, especially for sequential simulation (SGSIM/SISIM), declustering, or if you are familiar with GSLIB parameter conventions. Best for reservoir characterization workflows.

Choose scikit-gstat when: You prefer a modern scikit-learn-style API for variogram analysis and kriging, with better integration into Python data science workflows.

Choose pykrige when: You only need kriging interpolation (no simulation) and want universal kriging with external drift or regression kriging capabilities.

Common Workflows

Variogram Analysis and Kriging Interpolation

Load spatial data into a pandas DataFrame
Explore data with
```
GSLIB.locmap()
```
and
```
GSLIB.hist()
```
Check for clustering and decluster with
```
geostats.declus()
```
if needed
Apply normal score transform with
```
geostats.nscore()
```
Compute experimental variogram with
```
geostats.gamv()
```
(isotropic first)
Check directional variograms for anisotropy (azimuths 0, 45, 90, 135)
Fit variogram model with
```
GSLIB.make_variogram()
```
Overlay model on experimental variogram to verify fit
Run kriging with
```
geostats.kb2d()
```
(ktype=1 for ordinary)
Run SGSIM for uncertainty quantification (50-100 realizations)
Back-transform results with
```
geostats.backtr()
```
Validate with cross-validation or holdout data

Common Issues

Issue	Solution
Variogram doesn't reach sill	Increase `nlag` or `xlag` to capture full range
Kriging produces negative values	Back-transform after kriging, not before
SGSIM artifacts	Check grid definition (xmn, xsiz) matches data extent
Too few variogram pairs	Increase `atol` (angular tolerance) or `xltol` (lag tolerance)
Hole effect in variogram	May indicate periodicity; try nested structures

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

geostatspy

NPX Install

Tags

SKILL.md Content

GeostatsPy - Geostatistical Analysis

Quick Reference

Key Functions

Common Operations

1. Normal Score Transform

2. Experimental Variogram

3. Variogram Model

4. Kriging (kb2d)

5. Sequential Gaussian Simulation

6. Declustering

Variogram Models

Key Parameters

When to Use vs Alternatives

Common Workflows

Variogram Analysis and Kriging Interpolation

Common Issues

Tips

References

Scripts