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Unit information: Geophysical Data Analysis and Modelling in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Geophysical Data Analysis and Modelling
Unit code EASC30054
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 1A (weeks 1 - 6)
Unit director Dr. Werner
Open unit status Not open




School/department School of Earth Sciences
Faculty Faculty of Science


This unit will introduce students to a range of methodologies used for the transformation and interpretation of geophysical digital data. Using a combination of lectures and computer-based practicals (using MATLAB) both the mathematic principles behind and the practical applications of these methodologies will be taught.

The course has three components. Firstly, common methodologies applied to geophysical data (including spectral methods) will be covered. The second component will introduce forward modelling, including analytical and commonly used numerical techniques such as finite-difference and finite-element models. Finally, the course will introduce the concept of inversion, and cover basic inverse theory as well as the practical aspects of its application.

Intended learning outcomes

On completion of the course students will:

  • Understand the principles behind common time-series data processing techniques
  • Understand the concept of forward modelling.
  • Understand the principles behind some common forward modelling methods.
  • Understand the basic principle of inversion
  • Understand the mathematical basis of linear inversion
  • Appreciate some of the situations which arise in the practice of inversion
  • Be able to practically apply (in MATLAB) a range of common data processing algorithms
  • Be able to translate a simple analytical model into code
  • Be able to apply a provided more complex forward model
  • Be able to write code to assess the fit of a model to some data
  • Be able to run a simple iterative linear inversion to determine best fitting parameter.

Teaching details

15 Lectures and 5 practicals

Assessment Details

  • Extended practical (30%). This will be a formative MATLAB programming assignment, which will involve implementing an inversion of real geophysical data and critically assessing the results.
  • 2-hour written examination (70%). This will assess the student’s understanding of the theoretical aspects of the course.

Reading and References


  • David Gubbins: ‘Time Series and Inverse Theory’, CUP, 2006.
  • Frank Scherbaum: ‘Of poles and zeros’, Springer, 2001