Computational Methods Applied to Geophysics

Recommended Prerequisites

N/A

Teaching Methods

Learning contents are presented and discussed in lecture classes (2 hours/week). In supervised computer laboratory classes (1.5 hours/week), students are expected to formulate the solutions of proposed problems and implement them in the form of Python scripts. Weekly problem sets and computer exercises will be provided.

Learning Outcomes

Upon completion of this course, students will be able to:

- Understand the basic physical principles of geophysical methods.

- Understand the numerical methods applicable to geophysical methods.

- Analyse time series.

- Numerically solve partial differential equations using finite-differences.

- Formulate and implement modelling and inversion methods for the solution of geophysical problems.

- Write clear and efficient Python scripts in serial or parallel computing architectures.

- Use open source Python modules for geophysical applications.

 

Secondary skills:

- Physics and computational skills to solve problems.

- Autonomy to learn and articulate concepts.

- Ability to search and use specialized bibliography.

Work Placement(s)

No

Syllabus

1. Python for Geophysics

- Introduction to Python

- Visualization tools for geophysical data

- The SciPy ecosystem

- Fast array processing

- Parallel computing

- Python modules for Geosciences

2. Time series analysis

- Practical estimation of spectra

- Processing of time sequences

- Time series analysis with Pandas

3. Finite-differences solution of partial differential equations

- Finite-differences discretization

- Application to heat flow

- Application to wave propagation

4. Inversion methods

- Linear parameter estimation

- Nonlinear inverse problems

5. Modelling and inversion with open source Python modules

6. Gravimetric modelling and inversion

- Modelling of gravimetric anomalies

- Gravimetric inversion

7. Geoelectric modelling and inversion

- 2D Modelling of electrical resistivity sounding

- 2D electrical resistivity tomographic inversion

8. Seismic modelling and inversion

- 1D Modelling, synthetic seismograms

- Reflection and refraction seismology

Head Lecturer(s)

Manuel António Salgueiro da Silva

Assessment Methods

Assessment
Resolution Problems: 40.0%
Project: 60.0%

Bibliography

1. Fundamentals of Geophysics, W. Lowrie, Cambridge University Press, 2007.

2. Field Geophysics, J. Milsom, Wiley, 2011.

3. Introduction to Seismology, P. Shearer, Cambridge University Press, 2009.

4. An Introduction to Seismology, Earthquakes, and Earth Structure, S. Stein and M. Wysession, Wiley-Blackwell, 2002.

5. Geophysical Data Analysis: Discrete Inverse Theory, W. Menke, Academic Press, 2018.

6. Univariate Time Series in Geosciences, Theory and Examples, H, Gilgen, Springer-Verlag, 2006.

7. Introduction to Numerical Geodynamic Modelling, T. V. Gerya, Cambridge University Press, 2010.

8. Pythonic Geodynamics, Implementations for Fast Computing (Lecture Notes in Earth System Sciences), Gabriele Morra, Springer, 2018.

9. Computational Methods for Geodynamics, A. Ismail-Zadeh and P. Tackley, Cambridge University Press, 2010.

10. Parameter Estimation and Inverse Problems, 3rd edition, Richard C. Aster, Brian Borchers and Clifford H. Thurber, Elsevier, 2018.