Module GEOL2321: Earth Systems Modelling and Analysis

Department: Earth Sciences

GEOL2321: Earth Systems Modelling and Analysis

Prerequisites

• GEOL1061 Mathematical Methods in Geoscience OR GEOL1081 Further Mathematical Methods for Geoscientists, OR GEOL1061 Mathematical Methods in Geosciences or pass at A Level in Mathematics, grade B or above or the equivalent .
• GEOL1151 Introductory Data Science for Geoscientists.

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To introduce fundamental concepts of Earth observation and remote data acquisition.
• To introduce concepts of data manipulation, processing, visualisation and modelling.
• To select and apply data manipulation, visualisation and modelling methods to example Earth structures and processes.
• To understand the application of numerical and inverse modelling, as tools for investigating Earth processes and for predicting Earth systems behaviour.
• To provide data processing, analysis, modelling and visualisation skills-based training to underpin subsequent dissertation research at level 3 and level 4.
• To develop the quantitative and computational skills introduced at Level 1 and illustrate how these can be used to study a variety of Earth systems.

Content

• This module will be IT-based and enable students to develop skills and software usage experience compatible with the demands of modern Earth Science applications.
• Students will gain experience and understanding of a diversity of computer programming approaches and systems usages to suit specific problem solving, set in both Python programming and Linux scripting contexts.
• It will introduce students to the concepts of sampling and focus on scales of observation from satellite-based through to ground-based observation level.
• An understanding of the selection of appropriate method of observation will be developed together with the skills of processing, manipulation, modelling, display and interpretation of resulting datasets.
• Students will gain an understanding of key concepts of numerical and inverse modelling.
• Practical applications and examples will vary but may include, but will not be exclusively limited to: satellite remote sensing, resource exploration, Earth process imaging, environmental applications, earthquakes, large-scale surface topography, heat flow, chemical reactions, groundwater flow, wave propagation, and the sourcing and use of online global data repositories.

Learning Outcomes

• Will have acquired subject knowledge and understanding in Earth's natural resources, systems and processes and the techniques used to locate, understand and explore them.
• Will have acquired knowledge and understanding of numerical and inverse models and modelling.
• Will have acquired knowledge and understanding of data processing and visualisation approaches and how to select the appropriate method for data type.

• By the end of the module students will be able to enact visualization techniques at all scales of observation.
• Will be able to programme using a variety of approaches.
• Will be able to process and manipulate Earth Sciences data into a format to convey an interpretation to others.
• Will be able to determine mathematical descriptions of physical and chemical processes, and incorporate these into a numerical or inverse model.
• Will be able to critically evaluate models in terms of fit to data, uncertainties, resolution, uniqueness, and model or inversion instability.
• Will be able to analyse and visualise geospatial and time series data.
• Will be able to use computational tools to simulate Earth systems.
• Will be able to use such simulations, combined with observational data, to advance our understanding of geoscientific systems.

• Receive and respond to a variety of information sources.
• Communicate effectively to a variety of audiences in written, verbal and graphical forms.
• Prepare, process, interpret, model and present data using appropriate qualitative and quantitative techniques and packages.
• Evaluate the relationship between model predictions and observations.
• Self-learn and time manage to meet targets, goals and deadlines.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Computer-based classes encompassing both lecture-like information delivery and practical-like problem-solving approaches.
• Problem-based learning built around 42 x 2 hr slots, two per week.
• Each problem will build upon knowledge from a previous exercise and address a fundamental issue in the Earth Sciences using a range of data processing, visualisation, programming and/or modelling applications.
• Each session will contain a mixture of practical IT-based activities, including but not exclusively limited to: guidance, feedback and technical explanations.
• The students will be tested on their skills during in-person practical sessions.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Will be embedded within in-person sessions and coupled with proactive feedback and self-learning and self-study mechanisms, practise assessments and regular discussions and reviews with peers and teachers.

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.