Machine Learning in Geosciences
By: Gerard Schuster
Instructor
Gerard Schuster
(Geology and Geophysics Research Professor at University of Utah)
Duration
30-31 May 2024:
16:00PM - 20:00PM CEST
4 hours/day
Disciplines
Data Science
Course Format
The EAGE Interactive Online Short Courses bring carefully selected courses of experienced instructors from industry and academia online to give participants the possibility to follow the latest education in geoscience and engineering remotely. The courses are designed to be easily digested over the course of two or three days. Participants will have the possibility to interact live with the instructor and ask questions.
Education Packages
To help you save on registration fees and better organize your learning path, we are offering Education Packages for all Interactive Online Short Courses and Online EETs. The packages are valid for a period of 12 months, choose between packages of 3, 5 and 10 credits.
Course Description
Participants will learn the high-level principles of several important topics in machine learning: neural networks, convolutional neural networks, and support vector machine. They will practice the execution of these methods on MATLAB codes (free for 30 days after downloading it from the MATLAB site) and Python-related codes (can be uploaded during the course). Applications include fracture detection in photos, fault delineation in seismic images and picking NMO velocities in semblance gathers.
Course Outline
About 66% of the time will be for 50-minute lectures and the remaining time will be devoted to lab exercises.
Participants' Profile
The course is designed for geoscientists who have heard about Machine Learning and might know some details, but lack enough knowledge to test ideas or make the next step in understanding. This limitation will be mitigated after a day of diligent attendance and effort. A selective overview of important ML topics is provided and their practical understanding comes from MATLAB and Python-related exercises applied to geoscience problems.
Prerequisites
Participants should have casual familiarity with linear algebra and calculus.
About the Instructor
Gerard Schuster
Gerard T. Schuster received his M.Sc. in 1982 and his Ph.D in 1984 from Columbia University, both in Geophysics. From 1984-1985 he was a postdoctoral fellow at Columbia University, after which he assumed a faculty position in Geophysics at University of Utah from 1985 to 2009. In that time he won several teaching and research awards, founded and directed the UTAM consortium, was chief editor of Geophysics for several years, and supervised more than fifty students to their graduate degrees. He was given EAGE’s Eotvos Award in 2007, awarded SEG’s Kauffman gold medal in 2010, and is the 2013 SEG Distinguished Lecturer for spring 2013. In the summer of 2009 he moved to KAUST (King Abdullah University of Science and Technology) as a Professor of Earth Science just north of Jeddah. He was a Professor of Geophysics from 2009 to 2021 at KAUST, and in 2022 returned to the University of Utah as the Geology and Geophysics Research Professor in Salt Lake City. His primary interests are in seismic migration and modeling, interferometry, waveform inversion, and a fondness for solving geological problems with modest-sized seismic experiments. Since 2018, he also also been teaching courses on machine learning.