Module manager: Phil Livermore
Email: p.w.livermore@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
SOEE5116M Computational Inverse Theory
This module is not approved as an Elective
This module lays the foundation of digital literacy skills needed for the programme. It comprises three parts: coding, data analysis, and inverse theory. Coding is needed throughout the programme, and it is a vital skill for all students. This element will be taught in the first few weeks of the module, ensuring that the students come rapidly up to speed with this tool. The second component is digital data analysis, which underpins all geophysical data-collection. Students will learn the practice and theory behind converting analog to digital data, and extracting meaningful spectral information from datasets. The third component is inverse theory, in which students learn how to make inferences from indirect geophysical data. Quantification of uncertainty is a key component of this, as it determines the confidence that can be placed in any data-driven conclusion.
The aims of the module are summarised as:
1. To learn how to write and critique computer code. This will be taught in the first few weeks of the module, mostly in computer practicals but also by self-guided learning.
2. Digital data analysis will be taught by practicals, giving the students hands-on experience in reading, processing, appraising and interpreting digital datasets.
3. Inverse theory will be taught in a series of practical sessions, in which the fundamental theory is introduced and then implemented in geophysical contexts.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Appraise the information content of digitally sampled data and how accurately analog data are digitised.
2. Infer unknown geophysical properties from indirect observations of related quantities.
3. Quantify the uncertainty of a geophysical inference based on indirect measurements.
4. Write computer code to provide quantitative geophysical analysis.
5. Critique computer code written by others to assess its applicability to solve a geophysical problem.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Digital proficiency: read, analyse and interpret digital data.
2. Digital proficiency: write code that is easy to read and adaptable.
3. Critical thinking: how to critique data and methodologies.
4. Effective communication: presenting a research study combining both code, results and narrative.
5. Problem solving: using geophysical digital datasets and methods based on analytical thinking.
6. Reflection of the robustness of a geophysical inference, by appraising its uncertainty.
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practical | 5 | 3 | 15 |
| Practical | 12 | 2 | 24 |
| Independent online learning hours | 18 | ||
| Private study hours | 93 | ||
| Total Contact hours | 39 | ||
| Total hours (100hr per 10 credits) | 150 | ||
In all practical sessions staff will circulate while the students are working and be responsive to student questions. Formative feedback will be given both individually by discussing the work with the students, but also class-wide by discussing key problems that have arisen.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 50 |
| Coursework | Coursework | 50 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 29/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team