Signal processing is an essential tool for geoscientists and in particular for geophysicists. It is used to improve signal-to-noise ratio, extract pertinent information from the raw data, and give quantitative interpretations of geoscience field observations. This course covers the following topics:

• Signals and their properties in the time and frequency domain;
• The Fourier transform (continuous and discrete) and the convolution of discrete functions;
• Tapering, filtering, sampling and correlation.

The course will be taught through lectures and practical sessions and will make extensive use of Python notebooks to analyze real geoscience signals.

By the end of this course, students should be able to:

• describe and analyze a signal both qualitatively and quantitatively;
• create and apply processing sequences to real data sets;
• write signal processing scripts in Python.

Students who have not had previous experience with Python programming and Python Jupyter notebooks are encouraged to work through the following tutorial by Lion Krischer on Seismo-Live (click here)
 

The course requires a grasp of Fourier series and preferably also Fourier transforms, as well as the basics of scientific programming in Python.