Machine-learning in Sed-Strat: An active discussion
Presented by:
Zane Jobe
Discussion Starts at 12:00 (MT)
Webinar
Abstract
Machine learning (ML) is a hot topic in all of geoscience, and this session will focus on its applications in sed-strat. As a group, we will discuss:
When is ML appropriate and when isn’t it?
What data is already there, and what data do we need to collect? (aka The dreaded facies and hierarchy discussion…)
Utility of outcrop data
Utility of subsurface data
Hopefully we will walk away with the following:
A list of open-source tools and datasets for doing sed-strat ML
Ranking of important sed-strat questions that might be solved by ML
Ideas and sketches for answering these questions
BIO
Zane Jobe is a research professor at Colorado School of Mines and the Director of the Chevron Center of Research Excellence (CoRE). Zane also manages the Earth Resource Data Science program at Mines. Prior to Mines, Zane spent 6 years in the Clastics Research Team at Shell Oil Company. His research interests aim to better understand the stratigraphic architecture, scaling relationships, and sediment budgets for clastic depositional systems, with an emphasis on submarine environments. He also enjoys cycling and thinks that copious amounts of yard work can be cathartic. Zane received a B.S. in Geology from the University of Texas at Arlington in 2004, and a Ph.D. in Geology from Stanford University in 2010 (advisor - Don Lowe).
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