Agent skill

geostatspy

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.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/geostatspy

SKILL.md

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

  1. Always transform to normal scores - Most methods assume Gaussian
  2. Start isotropic - Add anisotropy only if justified by directional variograms
  3. Check variogram pairs - Ensure enough pairs at each lag
  4. Multiple realizations - Use 50-100+ for uncertainty quantification
  5. 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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results