Title: Introduction to Machine Learning for Geophysical Applications
Instructor: J.C. (Jaap) Mondt, Breakaway
Level: Foundation
CPD points: 4
Format: this extensive and interactive course consists of videos, presentations, (reading) assignments, quizzes and 4 live webinars with the instructor
Next delivery dates: 25 January - 25 February 2021
| Date | Time | Description |
|---|---|---|
| 25 January 2021 | 15:00 - 16:00 CET | Introduction webinar |
| 26 January 2021 | 15:00 - 15:30 CET | Q&A webinar |
| 27 January 2021 | Independent study | |
| 28 January 2021 | 15:00 - 15:30 CET | Q&A webinar |
| 29 January - 3 February 2021 | Independent study | |
| 4 February 2021 | 15:00 - 15:30 CET | Conclusion webinar |
| 25 February 2021 | End of the course |
Duration: 8 hours
Note this is an estimate of the time required to go through the course, including watching/reading lectures and attending webinars.
After purchasing this course, you will have access to the intake quiz and introduction material. Lectures and exercises will become available from 25 January 2021 as per the schedule above. You will have access to the course material until 25 February 2021. Make sure to complete all the requirements for the achievement of the certificate by 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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