Data Analytics, Machine Learning & Artificial Intelligence > Practical Machine Learning Methods in Geoscience - Virtual Instructor Led Training (VILT)
Code Date Format Currency Team of 10
Per Person*
Team of 7
Per Person*
Early Bird Fee
Per Person
Normal Fee
Per Person
PE1613 03 - 07 Oct 2022 Virtual Instructor Led Training (VILT) USD 1854 2624 3,199 3,499

*Fee per person in a team of 7 or 10 participating from the same organisation, registering 6 weeks before the course date
Request for a quote if you have different team sizes, content customisation, alternative dates or course timing requirements
Request for in-person classroom training or online (VILT) training format

Learn in teams and save more! Enjoy group discounts of up to 50% off normal fees for team based learning. Contact us on info@asiaedge.net to learn more today!

Code

PE1613

Date

03 - 07 Oct 2022

Format

Virtual Instructor Led Training (VILT)

Currency

USD

Team of 10
Per Person*

1854

Team of 7
Per Person*

2624

Early Bird Fee
Per Person

3,199

Normal Fee
Per Person

3,499

*Fee per person in a team of 7 or 10 participating from the same organisation, registering 6 weeks before the course date
Request for a quote if you have different team sizes, content customisation, alternative dates or course timing requirements
Request for in-person classroom training or online (VILT) training format

About this Virtual Instructor Led Training (VILT)

Machine Learning methods are making a dramatic improvement in analysing geoscience data, significantly decreasing the time and costs for processing and interpreting large volumes of geophysical and geological data. It also promises to combine a wide variety of data into a consistent and integrated interpretation. Machine Learning technology has moved beyond just hype but to become a reality.

Our 5 half-day Virtual Instructor Led Training (VILT) course introduces key concepts in Machine Learning (ML) and shows how they can be applied to geoscience data. Every method is accompanied by a hands-on ML lab. Chapters from the instructor’s new book Practical Machine Learning Methods in The Geosciences(Schuster, SEG Press 2022) will be used as the guide that provides both theory and examples of the different methods.

After the completion of this VILT course, participants will be able to:

  • Understand and describe the high-level concepts underlying neural networks, convolutional neural networks (CNN), support vector machines (SVI), clustering, Principal Component Analysis and Bayesian analysis
  • Execute the Colab and MATLAB programs applied to data examples
  • Adapt these methods to user-supplied problems
  • Understand the benefits and limits of these ML methods, and how to address their shortcomings with better training
  • Define tools for handling the uncertainty in results

This VILT course is suitable and will greatly benefit for the following specific groups:

  • Geophysicists
  • Geoscientists
  • Physicists
  • Production and processing engineers
  • Petrophysicists

The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day.

Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).

Your expert course leader is currently an adjunct Professor of Geophysics at University of Utah and formerly a Professor of Geophysics at King Abdullah University Science and Technology (KAUST). He was the founder and director of the Utah Tomography and Modeling/Migration consortium from 1987 to 2009. He 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.

He has an MS (1982) and a PhD (1984) from Columbia University and was a postdoctoral researcher there from 1984-1985. He received a number of teaching and research awards while at University of Utah. He was editor of Geophysics from 2004-2005 and was awarded SEG’s Virgil Kauffman gold medal in 2010 for his work in seismic interferometry. SEG published his book Seismic Inversion in late 2017 and is considering publishing his almost completed book Practical Machine Learning Methods in The Geosciences.

He has won Best Teaching award at his former university and was selected by the SEG as their Distinguished Lecturer for 2013. He was also the Distinguished Lecturer for the Society of Petroleum Engineers in 1998. His recent paper on machine learning was one of the top cited Geophysical Prospecting papers for 2021 and he is a keynote speaker at the SEG Data Analytics workshop in 2022.

To further optimise your learning experience from our courses, we also offer individualized “One to One” coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.
Request for further information about post training coaching support and fees applicable for this.

Learn what past participants have said about PetroEdge training courses

A good overview of Machine Learning methods with sufficient depth to build a working understanding of the materials. The lectures and illustrations on K-Means were excellent.

Virtual Practical Machine Learning Methods in the Geosciences, SEC 2021

Great interaction with students and examples of theory application to common problems in geosciences

Virtual Practical Machine Learning Methods in the Geosciences, SEC 2021

Trainer is very knowledgeable and a gifted instructor. His topic is vital and made effective use of the Socratic method delivery of his course

Virtual Practical Machine Learning Methods in the Geosciences, SEC 2021