LABORATORY OF NUMERICAL DATA ELABORATION IN GEOPHYSICS
Academic year2016/17
CourseAPPLIED AND EXPLORATION GEOPHYSICS
Code099DD
Credits6
PeriodSemester 2
LanguageItalian
| Modules | Area | Type | Hours | Teacher(s) |
| LABORATORIO DI ELABORAZIONE NUMERICA | GEO/11 | LABORATORI | 60 | |
Programma non disponibile nella lingua selezionata
Knowledge
The student who successfully completes the course will have a solid knowledge of the fundamentals of geophysical (signal) data processing from a practical point. This will be achieved through exercises and practical examples in the lab classes.
He/She will have the ability to apply the basic algorithms of data processing on geophysical data and on digital data in general writing programs in Matlab or Octave.
Assessment criteria of knowledge
The student must demonstrate the ability to put into practice and to execute, with critical awareness, the activities illustrated or carried out under the guidance of the teacher during the course. Methods:
- Final laboratory practical demonstration
Further information:
Practical demonstration with an in-depth discussion of the code written to deal with a specific geophysical data problem.
Teaching methods
Delivery: face to face
Learning activities:
- attending lectures
- preparation of oral/written report
- Laboratory work
Attendance: Mandatory
Teaching methods:
- Task-based learning/problem-based learning/inquiry-based learning
- laboratory
- project work
Syllabus
The aim of the Laboratory is to give the student the basic skill in numerical methods for processing geophysical data. The classes consist of exercises on a computer using the Matlab software on actual and synthetic seismic data.
The topics developed are:
Convolutional model: reflectivity function and convolutional trace.
Sampling theorem and Fourier transform; Nyquist frequency.
Bidimentional Fourier transform: examples and applications on real data cases.
Autocorrelation and cross-correlation functions: properties; numerical estimation of the correlation between two functions.
Linear regression fitting on the first arrivals for statics computation.
Nonlinear optimization: a practical example.
Bibliography
Recommended reading includes the following works:
Notes of the course.
Yilmaz (2001): Seismic data Analysis. SEG Book
Updated: 14/11/2016 17:27
