Dataprocessing and Inverse Theory

Please note: the information in the course manual is binding.
 

 

Understanding the fundamental concepts of data processing and inverse theory and being able to put these concepts into practice.

Earth Sciences is particularly rich in data. To infer processes from such data, advanced processing and optimization techniques are required. Raw data can hide the specific information one is interested in and data processing becomes necessary. We will review the fundamentals of geophysical data processing, starting with a detailed description of sampling continuous functions before progressing to the corresponding discrete Fourier transform. We will introduce the important concept of convolution and present linear filter theory. In the following, simple linear inverse and optimization theory will be discussed and the classical techniques will be introduced. We will show how to design optimal filters using basic inverse theory. During the course and especially in the computer practicals, examples will be taken from various fields of geophysics. Time permitting, we will give a concise introduction to machine learning.

This course contributes to the development of the following transferable skills:

• Ability to work in a team: The computer practicals may be carried out in a team with shared responsibility
• Written communication skills: For each set of exercises, a written report is requested by the teams or individuals
• Problem-solving skills: The lectures provide several solutions to typical problems. The teams or individuals have to choose the best solution for each set of questions.
• Initiative: Discussions amongst students to solve the exercises are encouraged.
• Analytical/quantitative skills: mathematics: fundamental analytical and quantitative skills.
• Technical skills: Python programming