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Computational Methods in Seismic Data Analysis and Imaging
Last Updated: 2026-02-05 16:08:16
Abstract
Mathematical methods play a fundamental role in seismic data analysis and imaging. The course covers mathematical tools regarding Fourier analysis, inverse problems, sparsity and low rank that are central themes in modern seismic data analysis and imaging. Implementation design and computational efficiency are aspects that are also covered.
Objective
The students are expected to learn to deal with Fourier analysis on unequally spaced data, frequency estimation methods, Radon transforms, rank constraints and splitting methods of complex problems into smaller sub-problems. The students are expected to be able to implement algorithms within the area on their own during the course. Another objective is to be able to adapt and apply these methods to seismic data.
Content
6 (2 hour) lectures followed by 2h lab, Computer laboratory exercises every week. Recap of linear algebra concepts. Duality, norms, eigenvalues and singular value decomposition The Radon transform The FFT and the unequally spaced FFT. Frequency estimation methods Data sparsity Low-rank methods The alternating direction method of multipliers Kirchhoff migration Reverse time migration The adjoint state method GPU programming model. CUDA kernels in C. Computer laboratory exercises covering * The Radon transform and the unequally spaced FFT. Using GPU in MATLAB or Python. * Frequency estimation, data sparsity and the alternating method of multipliers. * Seismic migration.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Computational Methods in Seismic Data Analysis and Imaging |
|
32 h semesterly |
| exercise | Computational Methods in Seismic Data Analysis and Imaging - Exercises |
|
32 h semesterly |
Offered In
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Electives (Courses can be chosen from the complete offerings of the ETH Zurich and University of Zurich (according to prior agreement with the MSc Committee).)
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