Piero Poli

University of Padova

Department of Goescience

Via Giovanni Gradenigo, 6, 35131 Padova PD

Email: piero.poli@unipd.it

My Research

I am a seismologist with experience in both earthquake source physics, and seismic tomography and imaging using the seismic ambient noise.

My research is based on analysis of seismic data to understand the earthquake sources and the structure of the Earth.

I am mainly interested in fault rheology and deformation style, rupture nucleation induced microseismicity, rupture mechanism of deep and intermediate depth earthquakes, ambient noise for body wave recovery, imaging the core mantle boundary and mantle transition zone, seismic signal sonification, scattering and wave propagation.

To know more about my research and read our last articles please check google scholar here

Figure: Time evolution of precursory signals for the Nuugaatsiaq landslide. A) Cumulative number of event as function of time. B) The 95 detected events ranged as function of time. The stack of these signals gives the reference trace (C) in which clear P and S waves are observed. The amplitude time evolution (D) is in clear agrees with the exponential increment of events seen in (A). From Poli (2017).

Global long period detection

We made a new global catalog of lone period events. with this analysis we found many signals not associated to regular earthquakes present in regular seismic catalogs. You can find this new catalog here: 10.5281/zenodo.8181257

Stay tuned! A SRL article describing the methodology in details will soon be out!!!!

ERC STARTING GRANT - MONIFAULTS

The MONIFAULTS ERC Project focus on “Monitoring real faults towards their critical state”

The aim of this project is to develop novel techniques to analyze and classify seismological data to study the evolution of stress in real faults. Furthermore, we are planning to include independent geophysical observations as GPS and velocity variation from ambient noise correlation to better understand the dynamics of faults during the earthquake cycle, with particular interest to the preparation phase of big earthquakes.

KEYWORDS: Machine learning, earthquake nucleation, slow deformation, ambient seismic noise correlation, monitoring, geodesy, seismology

Here some project related results (for more click here):

Interpreting convolutional neural network decision for earthquake detection with feature map visualisation, backward optimisation and layer-wise relevance propagation methods, J Majstorović, S Giffard-Roisin, P Poli, Geophysical Journal International
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
Tracking the Spatio‐Temporal Evolution of Foreshocks Preceding the Mw 6.1 2009 L’Aquila Earthquake, L Cabrera, P Poli, WB Frank, Journal of Geophysical Research: Solid Earth 127 (3), e2021JB023888

Designing convolutional neural network pipeline for near‐fault earthquake catalog extension using single‐station waveforms, J Majstorović, S Giffard‐Roisin, P Poli, Journal of Geophysical Research: Solid Earth 126 (7), e2020JB021566
The imbricated foreshock and aftershock activities of the Balsorano (Italy) Mw 4.4 normal fault earthquake and implications for earthquake initiation, H Sánchez‐Reyes, D Essing, E Beaucé, P Poli, Seismological Research Letters 92 (3), 1926-1936