Awesome
<h1 align="center"<p> <img src="https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png" width="220" height="200" /> </p></h1> <h1 align="center">GeoDataSets: Synthetic Subsurface Data Repository (0.0.1)</h1> <h3 align="center">Open-data multivariate, spatiotemporal datasets to support education and research!</h3>To support education and repeatable research we need open-data, data that is openly accessible, exploitable, editable and shared by anyone for any purpose, licensed under an open license. For multivariate, spatiotemporal problems these datasets are not widely available. Also, it is very helpful to have access to the 'inaccessible', exhaustive truth model (the population from which samples are extracted). So I have used my geostatistics skills to make a wide variety of synthetic truth populations an sample datasets to support my educational content and research and in the spirit of open-data, I share it here for anyone to use.
Michael Pyrcz, Professor, The University of Texas at Austin, Data Analytics, Geostatistics and Machine Learning
Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn
Cite As:
Pyrcz, Michael J. (2021). GeoDataSets: Synthetic Subsurface Data Repository (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.5564874
Repository Summary
A collection of synthetic subsurface datasets to support education, publications, and prototyping. This repository includes a wide variety of synthetic, subsurface datasets with a variety of:
Data Dimensionality
To support education with easy visualization and interactivity the datasets are 1D and 2D.
- 1D cores from wells and 2D seismic maps.
Number of Features
For multivariate analysis some of the datasets include up to 6 features with a variety of structures.
- linear and nonlinear, homoscedastic and heteroscedastic, and multivariate constraints
Data Issues
The datasets attempt to include typical issues such as non-physical values, random and structured noise
I hope this is helpful,
Michael
The Repository Author:
Michael Pyrcz, Professor, The University of Texas at Austin
Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions
With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.
For more about Michael check out these links:
Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn
Want to Work Together?
I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.
-
Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you!
-
Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!
-
I can be reached at mpyrcz@austin.utexas.edu.
I'm always happy to discuss,
Michael
Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin