TRANSFORM 2020

Tutorial: Open Source Spatial Data Analytics in Python with GeostatsPy
 
'Build from zero, learn some spatial data analytics / geostatistics tools 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 all of the Tutorial on GitHub @ https://github.com/GeostatsGuy/GeostatsPy_Intro_Course:

1. Lectures as PDFs with a fast treatment of fundamental spatial data analytics 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 (ground water, minerals and hydrocarbons).  As an applied branch of statistics they add:

1. spatiotmeporal 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 term-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 familarity with the functionality of the GeostatsPy package
2. Add it to Your Toolbox: Provide experience with a variety of spatial data analytics workflows
3. You're Welcome: Invite community contributions to the GeostatsPy open source project

How Will We Accomplish All of That?

1. 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, including:

1. Variogram Calculation
2. Variogram Modeling
3. Spatial Estimation

 Prerequisites

Please have the following installed locally:

1. Anaconda / Jupyter Notebooks
2. GeostatsPy (available at PyPi, use 'pip install GeostatsPy' to install)

More Information

• Times are in UTC and the location is virtual.
• You can reach me at mpyrcz@austin.utexas.edu.

I'm happy to discuss,

Michael

MICHAEL J. PYRCZ, Ph.D., P.Eng.,  Associate Professor
H.B. Harkings, Jr. Professor of Petroleum Engineering
Hildebrand Department of Petroleum and Geosystems Engineering
Jackson School of Geosciences and Bureau of Economic Geology
The University of Texas at Austin  |  512-471-3252  |  www.pge.utexas.edu
Twitter (@GeostatsGuy) | YouTube | LinkedIn | Webpage | Book | GoogleScholar