Two PhD projects available at the Geosciences department of the University of Padova. Interest students can submit their material here: https://www.unipd.it/en/phd-programmes-calls-and-admissions

For more information please contact piero.poli@unipd.it

Project 1: Monitoring water circulation in the crust with seismology

Monitoring the evolution of water volumes in the crust is nowadays a major societal challenge, in the framework of drastic climatic changes we experience. Indeed, severe droughts are nowadays impacting the freshwater supply and agricultural activity.  Improving our understanding on how water behaves in the shallow crust is thus fundamental in this context, to plan solutions aimed at reducing the impact of droughts. We here propose to track the spatiotemporal evolution of water into the crust by using seismic waves. Changes in water content manifests as a strong variation of seismic velocity, which can be measured in time with repeated seismic experiments, based on cutting edge seismological techniques and existing data. We will thus focus on i) building time series of velocity change in the Po plain region and surrounding areas, ii) construct a model to locate velocity variation in time, and ii) develop a physical model able to explain the spatiotemporal evolution of water content.

Project 2: Quantitative analysis of earthquakes swarms from data mining and modeling

Earthquake swarms are magnitude-unordered seismic sequences driven by a transient forcing (fluid redistribution, aseismic slip or combination of the two) that superimpose the long-term tectonic loading. They lack constitutive governing laws in terms of duration and moment release (1), cannot be described by Omori-Utsu and Båth law, and sometimes the Gutenberg-Richter model does not reproduce satisfactorily the frequency-magnitude distribution of earthquakes. However, earthquake swarms are increasingly recognized as an important part of the seismic cycle in all tectonically active regions (1, 2), although not yet routinely included in seismic hazard assessment study.   They occur in fluid-permeated volumes and usually herald physical and rheological discontinuities of faults, where a complex interplay between seismic and aseismic deformation occurs (3). Therefore, assessing the relative contribution of earthquake swarms in faults is fundamental to understand how stress accumulates or is released during the seismic cycle, and in ultimate analysis how large earthquakes may occur. The analysis of spatial and temporal occurrence and size distribution of earthquakes in swarm sequences is diagnostic to single out the relevant source process behind their occurrence (4).  In order to shed new light into the physics of earthquake swarm occurrence, we aim at: i) Producing novel observables by means of artificial intelligence and data mining applied to seismological and geodetic data from near fault observatories, and ii) Model the derived observations, during episodes of aseismic deformation in the Italian region and worldwide. In more details, the project target to investigate relevant swarm sequences occurred in the Italian territory (2, 5) and/or worldwide (5) in the past years. To that scope, it will be produced high resolution seismic catalogs by using machine learning methods and template matching approaches. These new and high-resolution catalogs will permit to track the spatial and temporal evolution of swarms which is a byproduct of the transient external forcing (e.g. aseismic slip, fluids, combination of the two). Moreover, our new catalogs will be used to extract repeating and near-repeating earthquakes, which indicates persistence of seismic sources and are a perfect tracker of aseismic deformation, and will thus be used as analogue of in situ strain meter to precisely estimate the slip budget in seismogenic faults.  Our novel seismological observation will be also used to parse geodetic data using novel stacking methods, able to estimate the aseismic deformation in faults. Moreover, the modeling of kinematic and source scaling properties (e.g. spatio-temporal migration, duration, and moment released) of the studied swarms will help us to infer the physics of the processes driving the seismicity and will be compared with worldwide compilation of swarm source scaling (2, 6). 

1.     L. Passarelli, E. Rivalta, S. Jónsson, M. Hensch, S. Metzger, S. S. Jakobsdóttir, F. Maccaferri, F. Corbi, T. Dahm, Scaling and spatial complementarity of tectonic earthquake swarms. Earth Planet Sci Lett. 482, 62–70 (2018).

2.     L. Passarelli, P. A. Selvadurai, E. Rivalta, S. Jónsson, The source scaling and seismic productivity of slow slip transients. Sci Adv. 7 (2021), doi:10.1126/sciadv.abg9718.

3.     S. Holtkamp, M. R. Brudzinski, Megathrust earthquake swarms indicate frictional changes which delimit large earthquake ruptures. Earth Planet Sci Lett. 390, 234–243 (2014).

4.      L. Passarelli, S. Hainzl, S. Cesca, F. Maccaferri, M. Mucciarelli, D. Roessler, F. Corbi, T. Dahm, E. Rivalta, Aseismic transient driving the swarm-like seismic sequence in the Pollino range, Southern Italy. Geophys J Int. 201, 1553–1567 (2015).

5.      Spatiotemporal Evolution of the Seismicity in the Alto Tiberina Fault System Revealed by a High‐Resolution Template Matching Catalog, D Essing, P Poli, Journal of Geophysical Research: Solid Earth 127 (10), e2022JB024845

6.     P. DANRE, L. de Barros, F. Cappa, J.-P. Ampuero, Fluid-induced anthropogenic and natural earthquake swarms driven by aseismic slip. Earth and Space Science Open Archive, 23 (2021).