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Bayi Glacier in Qilian Mountain, China (Credit: Xiaoming Wang, distributed via

Job advertisement 3 PhD positions in the Waves and Seismology Group of University of PADOVA - TOPIC: Ambient noise monitoring, long period seismology, microseismicity

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European Geosciences Union

3 PhD positions in the Waves and Seismology Group of University of PADOVA - TOPIC: Ambient noise monitoring, long period seismology, microseismicity

3 PhD positions in the Waves and Seismology Group of University of PADOVA - TOPIC: Ambient noise monitoring, long period seismology, microseismicity




Relevant divisions
Geosciences Instrumentation and Data Systems (GI)
Seismology (SM)
Soil System Sciences (SSS)

Full time

Entry level


Required education

Application deadline
2 June 2024

27 February 2024

Job description

The 3 projects will be developed under supervision of Prof. Piero Poli at the Geoscience department of the University of Padova:

Project 1: Seismic Monitoring of Crustal Water Circulation for Addressing Contemporary Societal Challenges
In the face of significant climatic changes and the associated challenges, monitoring the evolution of water volumes within the Earth’s crust has become a critical societal imperative. The increasing impact of severe droughts on freshwater supply and agricultural activities underscores the need to enhance our understanding of water behavior in the shallow crust. This knowledge is pivotal for devising solutions to mitigate the impact of droughts. In this project, we propose an innovative approach to track the spatiotemporal evolution of water within the crust through the utilization of seismic waves.
Changes in water content manifest as pronounced variations in seismic velocity, presenting an opportunity for measurement through repeated seismic experiments.
Leveraging cutting-edge seismological techniques and existing data, our objectives include: i) constructing time series of velocity changes in the Po plain region and its surrounding areas, ii) developing models to pinpoint temporal variations in velocity, and iii) creating physical models capable of elucidating the spatiotemporal evolution of water content. By achieving these goals, this research aims to contribute valuable insights towards addressing contemporary challenges associated with water circulation in the crust, ultimately aiding in the development of effective solutions for managing and mitigating the impact of droughts.

Project 2: Monitoring Earth’s Dynamic Surface: Unveiling Climate-Induced Phenomena and Volcanic Activities through Long-Period Global Seismic Waves in the context of Environmental Seismology

The Earth’s free surface undergoes significant transformations due to climate change, resulting in various phenomena across remote regions such as Greenland and Antarctica. Rapid glacial movements leading to tsunamis and seiches, along with unprecedented underwater volcanic eruptions in locations like Mayotte and Tonga, are indicative of the complex dynamics at play. Seismic waves generated by both climate and volcanic processes serve as crucial tools for unraveling the underlying physics of these remote yet impactful events. In this PhD project, we aim to analyze continuous seismological data spanning the last 30 years on a global scale. Our objective is to identify novel signals associated with physical processes occurring at the Earth’s free surface. Through meticulous analysis, we intend to create a comprehensive catalog of these events and perform physical modeling to gain insights into their dynamics, including parameters such as the mass of ice and the forces involved in each process. This research will contribute to a deeper understanding of Earth’s surface dynamics in the face of climate change and volcanic activities.

Project 3: Quantitative Analysis of Earthquake Swarms through Advanced Data Analysis Tools

Earthquake swarms, marked by unordered seismic sequences driven by transient forces, present a significant challenge to conventional seismic models. Conventional physical laws struggle to accurately describe the occurrence, duration, and moment release of swarms, emphasizing their complexity within the seismic cycle. Despite their importance in tectonically active regions, earthquake swarms are frequently overlooked in routine seismic hazard assessments. This project endeavors to conduct a thorough quantitative analysis of earthquake swarms, utilizing advanced seismological data analysis tools. The approach involves leveraging cuttingedge artificial intelligence and data mining techniques to extract novel observables from seismological and geodetic data collected from near-fault observatories.
Subsequently, the derived observations will inform the modeling of episodes of aseismic deformation in both the Italian region and globally. This modeling aims to illuminate the intricate interplay between seismic and aseismic deformation within fault zones. The identification of repeating and near-repeating earthquakes from the newly generated catalogs serves as a crucial step in discerning the persistence of seismic sources. This identification acts as an analog for in situ strain meters, providing a precise estimation of slip budgets in seismogenic faults. The novel observables will be instrumental in the development of innovative stacking methods for parsing geodetic data. This approach will yield additional insights into aseismic deformation within faults. Finally, the project will delve into modeling the kinematic and source scaling properties of the studied swarms. This includes examining spatio-temporal migration, duration, and moment release. By advancing our understanding of earthquake swarm occurrences, this project aims to significantly contribute to the evolving field of seismic research. Ultimately, the insights gained will enhance our ability to assess seismic hazards, paving the way for improved earthquake preparedness and mitigation strategies.

How to apply

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