There are 16 repositories under geostatistics topic.
Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Kriging Toolkit for Python
GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
GeostatsPy Python package for spatial data analytics and geostatistics. Started as a reimplementation of GSLIB, Geostatistical Library (Deutsch and Journel, 1992) from Fortran to Python, Geostatistics in a Python package. Now with many additional methods. I hope this resources is helpful, Prof. Michael Pyrcz
Geostatistical variogram estimation expansion in the scipy style
Analysis of digital elevation models (DEMs)
Geospatial Data Science with Julia
A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz
Geostatistics in Python
These are python notebooks accompanying Lessons available at GeostatisticsLessons.com
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.
GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in May, 6th: Drift analysis.
Fast image quilting simulation solver for the GeoStats.jl framework
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
A High Performance Unified Framework for Geostatistics on Manycore Systems.
Geostatistical utilities and tutorial in R. For the tutorials I have included Rmarkdown html files.
Implicit 3D geological modeling and geostatistics
Turing patterns simulation solver for the GeoStats.jl framework
Training images for geostastical simulation
Mapping of groundwater level for realistic flow flowpaths using semi-automated kriging.
Multiple experiments on geo-datasets
Sequential Gaussian Simulation for generating Gaussian fields.
Process case studies on DEM uncertainty analysis at the Mont-Blanc massif and Northern Patagonian Icefield: Hugonnet et al. (2022).
[official] PyTorch implementation of Latent Diffusion Model for Conditional Reservoir Facies Generation
Generate stocastic Gaussian realization constrained to a coarse scale image.