Links for Material: Demo Scripts Raw Data MAT Data Ibex_HandsOn
Gate Pass Form (Only for non-KAUST attendees attending in person)
The KAUST Supercomputing Core Laboratory (KSL) is organizing a 1-day workshop on Machine Learning for Engineers using MATLAB. This hybrid event is organized in conjunction with CES, exclusive partner of MATLAB & Simulink in the Middle East.
About this event
This event will cover the fundamentals of machine learning using MATLAB and will give the participants hands-on experience in applying deep learning to an interesting engineering problem – Seismic Facies Classification.
Who can attend?
This workshop is open to all engineers, researchers, students and managers, who are working at KAUST or any other educational institute or industrial company in Saudi Arabia. Non-KAUST attendees may be able to attend in person if they fill out the gate pass form in time and obtain confirmation email from us. Remote attendees will be receiving zoom link from us after registering.
This workshop is targeted at engineers & researchers who have limited or no experience of applying Machine Learning to their specific problems. Attendees with no background in MATLAB and/or machine learning can certainly benefit from this workshop.
To register
Click here to register for this online workshop. You will be receiving more information via emails after your register.
Important: Before attending this workshop
To get the most out of this workshop, we encourage you to do the following free self-paced online certificate, before attending, and/or review the training material from the previous workshop.
For more information, visit the workshop website or contact us at training@hpc.kaust.edu.sa.
We are looking forward to meeting you!
Dr. Rooh Khurram
KAUST Supercomputing Core Lab
Flavio Pol
Application Engineer
CES – exclusive partner of MathWorks in the Middle East
Speakers: Learn more about us
Workshop Zoom Link: Zoom Link
Training Material: Demo Scripts Raw Data MAT Data
Presentation Slides: Coming Soon
Feedback Form: Click here for feedback
Recording of the Workshop: link
MATLAB Download Instructions:
MATLAB Desktop Required Products
Previous Events:
Upcoming Events:
Seismic Facies Classification:
With the dramatic growth and complexity of seismic data, manual annotation of seismic facies has become a significant challenge. One of the challenges lies in classification where interfaces between different rock types inside the Earth are delineated in a seismic image i.e., dividing the subsurface into regions that can be classified as distinct geologic facies. Delineating these facies requires months of efforts by geoscientists.
Can AI algorithms solve this? Can they be trained to recognize distinct geologic facies in seismic images, producing an interpretation that could pass for that of an expert geologist, or be used as a starting point to speed up human interpretation? Recently, deep learning algorithms (particularly CNNs) have been used to simplify this task.
In this workshop, we walk through how MathWorks helped solve this challenge with a unique and innovative approach. We demonstrate the advantage of using advanced signal processing techniques to pre-process signals before feeding them to deep learning algorithms. Our approach combines maximal overall discrete wavelet transform with recurrent neural networks (RNN) to improve the automated seismic facies analysis. This proposed framework generates more accurate results in a more efficient way. In addition, you will learn the following:
For more details about the methodology, please refer to this MathWorks blog.
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