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2017/18 Taught Postgraduate Module Catalogue

SOEE5116M Computational Inverse Theory

15 creditsClass Size: 40

Module manager: Prof Andy Hooper
Email: a.hooper@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2017/18

Pre-requisite qualifications

Course entrance pre-requisite

Module replaces

SOEE5115M Time Series and Inverse Theory and combines computing component of SOEE5154M and inverse theory component of SOEE5115M.

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

After completing this module, students will be able to:

(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 typeNumberLength hoursStudent hours
In Course Assessment22.004.00
Lecture161.0016.00
Practical162.0032.00
Private study hours98.00
Total Contact hours52.00
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 typeNotes% of formal assessment
In-course AssessmentIn-Class Assessed Test 115.00
In-course AssessmentIn-Class Assessed Test 215.00
Total percentage (Assessment Coursework)30.00

Re-sit is by examination only (see below)


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 00 mins70.00
Total percentage (Assessment Exams)70.00

A student who fails this Module may be offered a resit. The resit mark provides a new alternative mark for the whole Module. The re-sit for this module will be a single unseen examination, of duration 2 hours, covering the same content, and of the same format, as the first-attempt examination.

Reading list

The reading list is available from the Library website

Last updated: 15/05/2017

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