Title: Introduction to Machine Learning for Geophysical Applications
Instructor: J.C. (Jaap) Mondt, Breakaway
Disciplines: Data Science - Machine Learning
Level: Foundation
CPD points: 10
Format: this course consists of video lectures, reading material, quizzes and 4 live interactive webinars with the instructor. The webinars will take place in the first two weeks as per the schedule below. During this time the instructor will be available for Q&A. Participants will then have time for independent study.
Next delivery dates: 27 September - 27 October 2021
Registration |
Early fee until 19 Sept |
Regular fee from 20 Sept |
|---|---|---|
| EAGE Bronze/Silver/Gold Member | 295 EUR | 345 EUR |
| EAGE Platinum Member | 295 EUR | 295 EUR |
| EAGE Green Member | 345 EUR | 395 EUR |
| EAGE Bronze/Silver/Gold Student Member | 150 EUR | 175 EUR |
| EAGE Green Student Member | 175 EUR | 200 EUR |
| Non Member* | 395 EUR | 445 EUR |
| Education Package | 2 credits | 2 credits |
*EAGE Membership for the remainder of the year is included in the non-member fee
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| Date | Time | Description |
|---|---|---|
| Monday 27 Sept 2021 | 15:00 - 17:00 CEST | Introduction webinar |
| Wednesday 29 Sept 2021 | 15:00 - 17:00 CEST | Q&A webinar |
| Thursday 30 Sept 2021 | 15:00 - 16:00 CEST | Q&A webinar |
| Thursday 7 Oct 2021 | 15:00 - 16:00 CEST | Wrap up and final Q&A webinar |
| 8 - 27 Oct 2021 | Independent study | |
| 27 Oct 2021 | End of the course |
Duration: 14 hours
Note this is an estimate of the time required to go through the course, including watching/reading lectures, attending webinars and completing quizzes.
After purchasing this course, you will have access to the course material. Your access will expire at the end of the course. Make sure to complete all the requirements for the achievement of the certificate before this date.
Certificate
A certificate of completion will be available upon completion of all course requirements. After the end of the course, your certificate will remain available for download in your Profile page.
Short description
The aim of the course is to introduce how Machine Learning (ML) is used in predicting fluids and lithology. It will give an understanding of the “workflows” used in ML. The used algorithms can be studied separately using references. Power-point presentations and videos will introduce various aspects of ML, but the emphasis is on computer-based exercises using open-source software.
Topic covered: The lectures and exercises deal with pre-conditioning the datasets (balancing the input classes, standardization & normalization of data) and applying several methods to classify the data: Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. Non-linear Regression is used to predict porosity.
Learning methods and tools
At the end of the course participants will have a clear idea how Machine learning, being part of Artificial Intelligence will impact the future of Geosciences. This will be evident from the examples of Machine Learning discussed and applied to the case of predicting lithology and pore fluids.
Intended Audience
All those interested in understanding the impact Machine Learning will have on the Geosciences and then as an example the impact on lithology and pore-fluid prediction. Hence, geologists, geophysicists and engineers, involved in exploration and development of hydrocarbon or mineral resources
Pre-requisites
A basic understanding of Geophysics and Statistics. A Pre-requirement quiz can be taken by participants to check whether their knowledge of Geophysics and Statistics is sufficient to follow the course.
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