2015/16 Taught Postgraduate Module Catalogue
SOEE5116M Computational Inverse Theory
15 creditsClass Size: 50
Module manager: Prof Andy Hooper
Email: a.hooper@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2015/16
Pre-requisite qualifications
Course entrance pre-requisiteModule replaces
SOEE5115 Time Series and Inverse Theory and combines computing component of SOEE5154 and inverse theory component of SOEE5115.This module is not approved as an Elective
Module summary
In this module, students will learn to use unix/linux operating systems for scientific computation applications, scientific programing and plotting using Matlab, and the application of inverse theory to geophysical data.Objectives
Computing: skills in the use of computers and programming in Matlab (with integration of these skills into other modules).Inverse theory: formulate inverse problems, explain the difficulties inherent in inverse problems, solve linear inverse problems using least-squares, linearise and solve non-linear inverse problems, describe and implement methods for regularization of ill-posed problems, formulate inverse problems in terms of probability distributions, derive solutions to inverse problems using Markov chain Monte Carlo algorithms
Learning outcomes
After completing this module, students will be able to: (1) formulate inverse problems, explain difficulties inherent in inverse problems, solve linear inverse problems using least-squares, linearize and solve non-linear inverse problems, describe and implement methods for regularization of ill-posed problems, formulate inverse problems in terns of probability distributions, and derive solutions to inverse problems using Markov chain Monte Carlo algorithms, and (2) use and perform tasks on computer workstations having unix/linux operating systems and use Matlab for basic algorithm development and plotting of geophysical data. The learning outcomes related to inverse theory will be assessed using a 2 hour unseen exam and those from computing will be assessed using 2x2 hour in-class computer test.
Syllabus
Computing: overview of computers and UNIX/LINUX operating system. Programming in Matlab: the user interface, syntax, variables, matrices, plotting, script design, conditional statements, loops, input/output, functions.
Inverse theory: formulation of inverse problems, linear least-squares, best linear unbiased estimator (BLUE), propagation of errors, maximum likelihood solutions, linearisation of non-linear problems, Monte Carlo error propagation, ill-posed problems, resolution matrix, regularization, cross validation, Bayesian inference, Markov chain Monte Carlo algorithms, neighbourhood algorithms.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| In Course Assessment | 2 | 2.00 | 4.00 |
| Lecture | 16 | 1.00 | 16.00 |
| Practical | 16 | 2.00 | 32.00 |
| Private study hours | 98.00 | ||
| Total Contact hours | 52.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Private study
Completion of practicals and assessments, computer exercises, literature search, reading text books, and revision for examination.Opportunities for Formative Feedback
Assessment and feedback during practicals.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| In-course Assessment | In-Class Assessed Test 1 | 25.00 |
| In-course Assessment | In-Class Assessed Test 2 | 25.00 |
| Total percentage (Assessment Coursework) | 50.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Exams
| Exam type | Exam duration | % of formal assessment |
| Standard exam (closed essays, MCQs etc) | 2 hr 00 mins | 50.00 |
| Total percentage (Assessment Exams) | 50.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
The reading list is available from the Library websiteLast updated: 22/05/2015
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- Undergraduate module catalogue
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