Geophysics for Data Scientists

Data Analysts, working with geophysical data, must understand what geophysics is about. That doesn’t mean they need to know the ins-and-outs of these subjects, but at least know the terminology and the overall context for which they need to provide the Machine / Deep learning tools. Therefore, this course will be a first step in providing the necessary geophysical background.

As it is assumed that the Data Analysts are familiar with mathematics and statistics, the course will include advanced geophysical subjects. A general overview of seismic and non-seismic acquisition, processing and interpretation will be followed by various uses of Machine / Deep learning for Geophysical Applications. We will predict lithology and pore fluids as well as Facies to learn the Deep Learning workflows and algorithms needed in geophysics. Use will be made of open-source software: TensorFlow and Keras. Power-point presentations and videos will introduce various aspects, but the emphasis is on computer-based exercises. The exercises deal with pre-conditioning the datasets and applying several methods to classify / cluster the data: Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. Non-linear Regression is used to predict porosity. Use will be made of Google Colab and Scikit-Learn. It runs on the Cloud and allows use of a GPU. It is “the way” to learn using a whole range of open-source Deep Learning algorithms for geophysical applications.

The course consists of many exercises as I am a strong believer in the paradigm: Tell me and I will forget, show me and I might remember, involve me (through exercises) and I will truly learn.

At the end of the course participants will have a clear idea of what goes on in Geophysics and how Artificial Intelligence will impact the future of Geosciences. Quizzes are provided to enhance the learning.

This course can be delivered remotely.