Tutorial: Open source geological uncertainty modeling in Python with GeostatsPy

Michael Pyrcz

Build from zero, learn some spatial data analytics / geostatistics tools and powerful uncertainty modeling workflows in Python. All welcome!

Unsure if You Want to Attend?

I model uncertainty as part of my job, but I actually don't like uncertainty in my life. Let me remove all uncertainty from this choice!

I have posted the entire Tutorial on GitHub: https://github.com/GeostatsGuy/GeostatsPy_Course_2

1. Lectures as PDFs with a fast treatment of fundamental spatial data analytics, geostatistics and uncertainty concepts
2. Interactive workflows in Python Jupyter Notebooks with GeostatPy, ipywidget and matplotlib packages
3. Well-documented workflows in Python Jupyter with GeostatsPy, demonstrated complete workflows to help get you started
4. Realistic, synthetic subsurface/spatial datasets for the demonstrations

Check it out, try it out, then decide if this is a good investment of your valuable time. Hope to see you there!

About the Lecturer

Howdy Folks, I'm Michael Pyrcz, an associate professor at The University of Texas at Austin, appointed in the Cockrell School of Engineering and the Jackson School of Geosciences. I teach and conduct research on spatial data analytics, geostatistics and machine learning and I record all my lectures on my YouTube channel at https://www.youtube.com/GeostatsGuyLectures and post my well-documented demonstrations/exercises at: https://github.com/GeostatsGuy. I work in C++, Python, Fortran, R etc.

GeostatsPy Python Package Background

Geostatistics and spatial data analytics are essential tools for data-drive spatiotemporal modeling workflows that are commonly applied to support decision making for subsurface resource development (e.g. ground water, minerals and hydrocarbons). As an applied branch of statistics, geostatistics and spatial data analytics adds:

1. spatiotemporal context,
2. geoscience information integration,
3. accounting for spatial data and model scales
4. uncertainty models

When I started teaching my graduate level 'Stochastic Subsurface Modeling' course, I failed to find the Python solution to support the students' experiential learning and completion of the semester-long subsurface modeling project. So I spent the weekends writing the GeostatsPy package (just days ahead of the students). I'm excited to see wide use and contributions to open source package.

Tutorial Description

Goals

1. Introduce the Package: Expand familiarity with the functionality of the GeostatsPy package
2. Add it to Your Toolbox: Provide experience with a variety of spatial data analytics uncertainty modeling workflows
3. You're Welcome: Invite community contributions to the GeostatsPy open source project

How Will We Accomplish All of That?

1. brief lectures: a limited amount time for overview lectures to provide basic theory
2. demonstrations: walk-throughs of well-documented workflows
3. exercises: hands-on challenges for experiential learning

Topics Covered

We will cover common topics/workflows for spatial data analytics and geostatistics, including:

1. declustering to mitigate biased statistics
2. geostatistical stochastic simulation
3. communication of geological uncertainty models

Prerequisites

Please have the following installed locally:

1. Anaconda / Jupyter Notebooks with Python up to 3.9, GeostatsPy due to a dependency on Numba
2. GeostatsPy (available at PyPi, use 'pip install GeostatsPy' to install)

More Information
1. Times are in UTC and the location is virtual.
2. You can reach me at [email protected].