The identification of patterns in, and the classification of, different types of environmental signals is crucial for understanding the processes that generate them. Machine learning can significantly improve our ability to identify signal from noise and increase our capacity to mine large volumes of environmental data, meaning it is possible to extract more information than is possible with traditional methodologies.
This project will initially use continuous broadband seismic data recorded in Borneo, Turkey and the Faroe Islands from projects that have previously been funded by NERC or supported through the NERC Geophysical Equipment Facility. These seismological datasets record continuous time series measurements of naturally occurring environmental processes and are ideal training data for testing and developing machine learning techniques.
Detection and classification algorithms applied to seismological data typically focus on earthquake signals due to the hazards that they pose. However, other natural phenomena that can be detected, such as landslides and rock falls, can also represent a significant risk. With increased rainfall due to a changing climate in some parts of the world, these are phenomena that are likely to become more common in the coming years. Improving our detection of such phenomena, and better understanding of the mechanisms that cause them, is vital to mitigate against the hazards they pose. Our understanding may be deepened through the use of seismological data, however one challenge at present is that catalogues of such signals are relatively sparse in comparison to catalogues of earthquakes.
Using the datasets from Borneo, Turkey and the Faroes, the student will generate annotated datasets of different types of signals, such as rockfall, landslides, quarry blasts, transport, and animal movement. These annotated datasets will then be used with supervised machine learning algorithms to generate catalogues of different types of natural and anthropogenic signals detected seismically in different locations. Given that the three locations have different noise profiles, different instrumentation, and potentially different sources of signals, this will provide an opportunity to develop understanding of how best to apply such algorithms on a global scale to publicly-available seismological data. The new, expanded, catalogues of environmental signals will then be used to investigate the processes that generate them.
Once the machine learning methodology is developed and tested on seismological data, the student will be able to apply it to further case studies across the spectrum of continuous environmental signals, both onshore and offshore, to extract better quality information in different settings.
The student will develop familiarity with seismological data, learn how to manage and manipulate large data sets, and write and develop code, primarily using Python, for Machine Learning applications.
Candidates should have (or expect to achieve) a minimum of a 2.1 Honours degree in a relevant subject. Applicants with a minimum of a 2.2 Honours degree may be considered providing they have a Distinction at Master’s level.
• Apply for Degree of Doctor of Philosophy in Geosciences
• State name of the lead supervisor as ‘Name of Proposed Supervisor’ on application
• State ‘QUADRAT DTP’ as Intended Source of Funding
• Select the https://www.abdn.ac.uk/pgap/login.php
to apply now
Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., & Gerstoft, P. (2018). Machine learning in seismology: Turning data into insights. Seismological Research Letters, 90(1), 3-14.
Lin, C. H., Kumagai, H., Ando, M., & Shin, T. C. (2010). Detection of landslides and submarine slumps using broadband seismic networks. Geophysical Research Letters, 37(22).