Kriging Toolkit for Python

GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more

Analysis of digital elevation models (DEMs)

Geostatistics in Python

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

Use SGeMS (Stanford Geostatistical Modeling Software) within Python.

Process case studies on DEM uncertainty analysis at the Mont-Blanc massif and Northern Patagonian Icefield: Hugonnet et al. (2022).

Spatial interpolation python package

ore body reserve report using geological block model, topographical surface and region solids

[official] PyTorch implementation of Latent Diffusion Model for Conditional Reservoir Facies Generation

"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.

GeoKrige is a Python package designed for spatial interpolation using Kriging Methods. While primarily tailored for geospatial analysis, it is equally applicable to other spatial analysis tasks.

A basic geostatistical python package

Jupyter Notebook GeoScientific Python Stack - Dockerfile

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

A Python module to apply sequential 2D multiple-point simulation with conditioning data

Two-point connectivity statistics computation for hydrological patterns

A Python library to implement a Co-Simulation Method with Copulas and Multi-Objective Optimization.

Some geostatistical exercises on the Herten aquifer analogue.

Geostatistical simulation for the homogenisation and interpolation of climate data