The course is ideal for geoscientists, engineers, and data analysts at all experience levels. Concepts are supported with ample illustrations and case studies, complemented by mathematical rigor befitting the subject. Aspects of supervised learning, unsupervised learning, classification, and reclassification are introduced to illustrate how these methods apply to seismic data. The course is presented in English only. The video recording was originally produced as part of a GSH webinar.
Machine Learning Essentials can be taken either with or Machine Learning Essentials for Seismic Interpretation (without certification). The certification requires correct answers to 80% or greater of the questions at the end of each of the four major sections; however, you may take the quizzes as often as you like. The course contains a total of 88 questions divided up in four sections, one for each unit. The certificate of completion will be presented in printable form upon successful completion of the course. Please select the without certification course option if you prefer to take the course without the certification.
- What is machine learning and how does it apply to seismic exploration and unconventional resource development?
- What is the difference between supervised and unsupervised machine learning?
- When is an analysis statistical and when is it machine learning?
- What is attribute space and what is the mathematical foundation of this technology?
- How do you know if the results are any good?
- What are some case histories that illustrate machine learning principles?
- What are some practical tips?