Bayesian Gutenberg-Richter Analysis for understanding Seismic Processes

Understanding seismic hazard is essential to reducing the risks posed by earthquakes. Our current knowledge comes in part from the study of earthquake nucleation processes and analysing available data on the timing, location and magnitude of past earthquakes. Unfortunately, these seismological observations are limited because we can only detect and use earthquakes above a certain size, making it difficult to estimate critical parameters for probabilistic seismic hazard assessment (PSHA). One of these critical parameters is the b-value, which describes how often earthquakes of different magnitudes occur. Accurate estimation of the spatial and temporal distribution of the b-value is essential for reliable seismic hazard assessment and for understanding the seismic processes that drive seismicity. However, traditional methods for b-value estimation can be biased by subjective decisions, such as which earthquakes to include based on their magnitude. The B-GRASP project aims to overcome these limitations by developing an unbiased, probabilistic method for estimating spatio-temporal variations in b-value. Using original Bayesian approaches, the project will (1) investigate how the b-value changes over time during foreshock sequences and what this reveals about broader seismic processes of earthquake nucleation, and (2) produce updated b-value maps to improve PSHA in regions of low to moderate seismicity, such as Spain and France. The research is highly innovative, taking advantage of recent advances in the use of the b-value, and multidisciplinary, using Bayesian approaches for the first time to achieve better resolution of the b-value and its uncertainties. The results will ultimately be shared with the academic and private sectors involved in the production of seismic hazard maps. This will help to further develop my combined expertise in statistical seismology and seismotectonics, and improve my prospects of securing a permanent research position.