An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
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Updated
Aug 17, 2024 - Julia
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
Utilities to read/write extended GSLIB files in Julia
Fast radial basis function interpolation for large scale data
[official] PyTorch implementation of Latent Diffusion Model for Conditional Reservoir Facies Generation
These are python notebooks accompanying Lessons available at GeostatisticsLessons.com
A Rust implementation of the core algorithms of GSTools.
"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.
Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome
Local anisotropies and nonstationary spatial processes for the GeoStats.jl framework
Analysis of digital elevation models (DEMs)
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
GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Multiple experiments on geo-datasets
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
Modelización del postprocesado de los mapas de rendimiento de las cosechadoras en el cultivo de trigo y cebada en España
A High Performance Unified Framework for Geostatistics on Manycore Systems.
A flexible MPS framework
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