SPIN ESR 2.3: Tomographic imaging of the subsurface of New Zealand/Next-Generation Physics-based earthquake forecasts
Host institution: British Geological Survey, UK
We are seeking highly motivated candidates for a fully funded PhD studentship at the British Geological Survey and the University of Edinburgh. Depending on the interests of the successful candidate, the project will either explore the use of physics-based simulations to develop the next generation of earthquake forecast models or generate new images of the Southern Alpine Fault in New Zealand, and use the results to model future hazard and earthquake triggering.
This PhD position is one of the 15 Early Stage Researcher (ESR) positions within the SPIN project (http://spin-itn.eu). SPIN is an Innovative Training Network (ITN) funded by the European Commission under the Horizon 2020 Marie Sklodowska-Curie Action (MSCA).
You will receive structured training in emerging technologies for measuring earthquakes and state-of-the-art methods for data analysis. The SPIN network will enable you to gain international expertise at excellent research institutions, with a meaningful exposure to other disciplines and sectors, thus going far beyond the education at a single PhD program.
The successful candidate will receive a generous stipend that includes living, mobility and family allowances. Depending on qualifications and experience, this will be equivalent to a salary of between £35,000 per annum to £45,000 per annum. In addition, the University of Edinburgh has waived all fees on Marie Curie positions. These would normally amount to £24,675 per annum.
Next-Generation Physics-based earthquake forecasts
In this project, you will develop earthquake forecast models in large scale based on physics-based simulations and/or AI-based earthquake forecasts, both aiming to improve our process-based understanding of earthquake triggering. You will generate, evaluate and optimize these forecast models using robust statistical modelling and validation.
Recent work shows that physics-based models match or even exceed the performance of empirical approaches when applied to forecasting earthquake aftershock sequences. The most important elements of improved performance in these approaches come from the consideration of heterogenous faulting networks and stress states. Here, we seek to push the limits of such physics-based approaches by including improved time-dependent representations of stress (transient deformation, pore pressure effects etc.) to achieve an evolving physics-based model that will inform us about large-scale processes that occur in real Earth. We are especially interested to inform these state-of-the-art models by machine-learning pathways, including supervised and unsupervised learning, to develop new approaches to earthquake forecasting.
For further information see https://spin-itn.eu/esr23/
Combining tomographic imaging, earthquake triggering and seismic hazard: application to the Southern Alpine Fault, New Zealand.
In this project, you will apply novel probabilistic methods to create images of the seismic velocity structure along and around the Alpine Fault that runs almost the entire length of New Zealand's South Island and forms the boundary between the Pacific Plate and the Indo-Australian Plate. This will use data recorded on a revolutionary new array of seismometers that spans the onshore length of the fault.
You will interpret the velocity models together with other geological data and physical measurements to refine models of fault mechanics and potential slip scenarios. You will also use regional data to construct new models of the velocity structure beneath the entire South Island of New Zealand and use these to improve understanding of how the seismic waves resulting from different slip scenarios are focused by structure and how this influences seismic hazard.
Finally, you will assess how stress changes caused by fault slip, and by propagating waves, may result in triggering of earthquakes on other known or unknown faults.
For further information see https://spin-itn.eu/esr23/
Required skills and experience
We seek a mathematically or statistically proficient student with excellent grades from an undergraduate and/or master's degree qualification in Earth Sciences, Geophysics, Physics, Mathematics, or another relevant field, as well as dedication and enthusiasm for research. Computational skills are also desirable. English language skills are essential, and candidates must satisfy all entry requirements for the PhD programme at the University of Edinburgh - see: https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2021&id=69
Please ensure that you fulfill the following eligibility criteria for ESR (Early Stage Researcher) positions in H2020 MSCA-ITNs: https://spin-itn.eu/recruitment/#eligibility-criteria
Applications must include:
Applications should be sent in one single pdf file with filename "SPIN_YourLastname_YourFirstname.pdf" to email@example.com
Application evaluations will start immediately and will continue until the position is filled. We wish to reflect the diversity of society and we welcome applications from all qualified candidates regardless of personal background. The selection will be exclusively based on qualification without regard to gender identity, sexual orientation, religion, national origin or age.
By applying to a PhD position, you agree that all data concerning your application may be stored electronically and distributed among the supervisors involved in the selection procedure within the MSCA ITN SPIN. If you do not agree, your application can not be processed further, due to the project's centralised recruitment process. The data are used solely for the recruitment process and we do not share information about you with any third party.