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Name: Stanford Center for Earth Resources Forecasting
Type: User
Company: Stanford University
Bio: For more information, please contact Jef Caers @ [email protected]
Blog: scerf.stanford.edu
Name: Stanford Center for Earth Resources Forecasting
Type: User
Company: Stanford University
Bio: For more information, please contact Jef Caers @ [email protected]
Blog: scerf.stanford.edu
ANODI: comparing geostatistical realizations using an analysis of distance
This is the repository for the Auto-BEL implementation in Python
Implementation of Convolved HMM
R implementation of the DGSA method
R implementation of distance based generalized sensitivity analysis (DGSA)
This is a light version of DGSA, written in Python
MATLAB codes for DisPat
An R package that implements the methods of geostatistics for functional data
An R package that implements methods for growing regression trees with functional and multivariate outputs
Mapping of geological structure to graphs
Directly Conditional Facies Modeling Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)
The second repository (GeoModeling_Conditional_ProGAN) is used for conditioning to well data and global features. This repository is for conditioning to geophysics-interpreted probability maps alone and together with well data and global features.
Unconditional Geomodeling related work (codes, data, and results)
teaching material for the book "Modeling Uncertainty in the Earth Sciences", Jef Caers, 2011.
Multi-Scale CCSIM
This is an implementation of Prediction Focused Analysis (PFA) using Canonical Functional Data Analysis (CFCA) for performing dimension reduction on time series response data.
probability perturbation method for inverse modeling
Repository to accompany PyNoddy that includes inversion code
Companion code for Scheidt, C, Li, L, and Caers, J. K. Quantifying Uncertainty in Subsurface Systems, John Wiley & Sons, 2017.
SGEMS-UQ code and data sets
SimPat: SIMulating with PATterns
Standalone Fortran 90 version of snesim written by Sebastien Strebelle
Data for the synthetic reservoir Stanford VI including EM modeling
This is the first version of the Tree-based Direct Sampling (TDS), with 2D Antactica Topography modeling case as example.
Turbidite System Simulation: simulating stacking patterns observed in distributary channel-lobe system using process-based statistics
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.