Is this blob real? Uncertainty quantification of earthquake source parameters using Bayesian inference techniques
Project ID: 2228cd1385 (You will need this ID for your application)
Under Offer
Research Theme: Physical Sciences
UCL Lead department: Earth Sciences
Lead Supervisor: Ana Ferreira
Project Summary:
Why this research is important:
The quantification of uncertainties in earthquake source parameters is crucial for accurate seismic hazard assessments and to discriminate between natural earthquakes and man-made events (e.g., nuclear weapons tests, fracking). Earthquake parameters are typically estimated using seismic data recorded worldwide along with statistical inverse modelling techniques to describe the space-time properties of ruptures. However, the parameters can be highly non-unique and yield large uncertainties, which are rarely quantified.
What you will be doing:
The goal of this project is to address these issues by developing new Bayesian inference approaches to assess the errors of earthquake source parameters. Building on preliminary work by the supervisory team, the reversible jump Markov Chain Monte Carlo technique will be combined for the first time with advanced machine learning emulation methods for advanced 3-D seismic wave propagation simulations. This will enable us to accurately assess which and how many source parameters can be constrained by seismic datasets, including complete uncertainty analyses. The results obtained will be compared with those retrieved from more standard techniques, such as the Neighbourhood Algorithm.
Who you will be working with:
You will receive training in big seismic data analysis from Prof. Ferreira (UCL Earth Sciences), and in statistical modelling and interpretation from Prof. Gillas (UCL Statistical Sciences), which are highly needed skills in the engineering and physical science sector. The project will open the way for a wider interdisciplinary research programme in physical, engineering and statistical sciences on global uncertainties.
Who we are looking for:
We are looking for a highly motivated student with strong quantitative skills (e.g., physics, geophysics, maths), who enjoys programming, and with a strong interest in earthquake source processes and in interdisciplinary research work. Previous experience in seismology is desirable but not essential.