Inversion, Signal Processing, and Image
Course Code
FI4161
Number of Credits
2
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Earth's physical system modeling concept | Expert |
| Future modeling of gravity fields, magnetic fields, E.M. fields, seismic travel time | Expert |
| Inversion basis, inversion regulation, inversion with Conjugate Gradient, Inversion with Steepest Descent | Expert |
| Least Squares Inversion, Damped Least Squares Inversion | Expert |
| Vectors and matrices, Arrays, Cells and structures, Matrix operations | Expert |
| Signal and system concepts, Discrete Time Signals and Sampling, Sampling theorem, Spectral representation, Random Signals, Random phenomena in signal processing | Expert |
| Fast Fourier Transform, Spectral accuracy and resolution | Expert |
| Short Term Fourier Transform, application examples, for example noise removal in sound signals | Expert |
| The z-transform, Transforms and linear filtering, Difference equations and rational TF filters | Expert |
| Connection between gain and polish/zero, Minimum phase filters, Filter design methods | Expert |
| Correlation, Convolution, Deconvolution, Wiener Filter | Expert |
| Demultiple filtering and Surface Related Multiple Attenuation in Marine Seismic Data | Expert |
| Wavelet Transform, theory, operation and types (Mexican Hat, Ricker Wavelet, etc.). STFT vs Wavelet transform | Expert |
| F-K filter, Kalman Filter | Expert |
| 2-D FFT and IFFT for noise and diffraction removal | Expert |
| 2-D Wavelet Transform | Expert |
| Geometric transformations of images, and other image operations. JPEG lossy compression, Watermarking, Blurring or Smoothing, Sharpening or Enhancing edges and details, Convolution: Applying filters (e.g., Gaussian, Sobel) to modify pixel values, Halftoning | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Able to use inversion techniques for various applications |
Learning Method
- Lectures, group discussions
Learning Modality
- Synchronous offline, asynchronous mixed/online
Assessment Methods
- Midterm Exam, Final Exam, Assignments, and Quizzes