Practical Machine Learning Methods in the Geosciences
Disciplines: Data Science and Engineering Analytics
Course Description
A selective overview of important ML topics is provided and their practical understanding comes from MATLAB exercises. Machine learning examples are taken from the fields of astronomy, medicine, geosciences, and material sciences.
Learn the high-level principles of five important topics in machine learning: neural networks; convolutional neural networks; support vector machines; principal component analysis; clustering methods. Practical examples in geosciences will be used to show the applications of each method. Practice the execution of these methods on MATLAB and Keras codes. The teaching format will be 50-minute lectures and 1-hour labs to reinforce the principles of each method.
Learning Level
Intermediate
Course Length
1 Day
Why Attend
- Learn how to apply ML methods to geoscience examples
- Understand key principles underlying each of the ML methods
- Practice manipulating MATLAB and Keras ML codes so they can adapt the codes to their own problems
- Understand the limitations and benefits of each ML method.
Who Attends
This short course is for physical scientists who have heard about ML and might know some details but lack enough knowledge to assess ML applications in their specialty.
CEUs
0.8 CEUs are awarded for this 1-day course.
Cancellation Policy
All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.
Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.
SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.
We reserve the right to substitute course instructors as necessary.
Instructor
Gerard Schuster is currently a Professor of Geophysics at King Abdullah University Science and Technology (KAUST) and an adjunct Professor of Geophysics at University of Utah. He was the founder and director of the Utah Tomography and Modeling/Migration consortium from 1987 to 2009, and is now the co-director and founder of the Center for Fluid Modeling and Seismic Imaging at KAUST.
Dr. Schuster helped pioneer seismic interferometry and its practical applications in applied geophysics, through his active research program and through his extensive publications, including his book "Seismic Interferometry" (Cambridge Press, 2009). He also has extensive experience in developing innovative migration and inversion methods for both exploration and earthquake seismology.