Enhancing the resolution of microseismicity through dense array monitoring in complex extensional settings
Francesco Scotto di Uccio, Titouan Muzellec, Antonio Scala, Grazia De Landro, Giovanni Camanni, Francesco Carotenuto, Luca Elia, Matteo Picozzi, Aldo Zollo, Claudio Strumia, Gregory C. Beroza, Gaetano Festa

TL;DR
This study shows how dense seismic arrays and machine learning can improve earthquake detection and fault understanding in complex extensional regions.
Contribution
A novel integration of short-term dense array data and machine learning significantly enhances seismic catalog resolution and fault characterization.
Findings
Seismic catalog size increased nearly tenfold with lower completeness magnitude.
65% of detected events were accurately relocated with ~100m median uncertainty.
Seismicity patterns reveal a 50–60 km curving fault with potential for magnitude 7.0 earthquakes.
Abstract
Characterizing geometry and mechanics of structures hosting moderate-to-large earthquakes is essential for seismic hazard assessment, yet remains challenging in extensional environments, where fault systems include multiple segments and bends. In this study, we demonstrate how a short-term array deployment can provide critical insights into seismicity patterns and fault geometry in Southern Apennines, Italy.We integrated data recorded by arrays during a one-year experiment with machine learning methodologies, producing a seismic catalog that enhances the manual catalog for the same period by nearly an order of magnitude, lowering completeness magnitude by one unit. Approximately 65% of the detected events can be accurately relocated, with median uncertainties of ~ 100 m, comparable to those of long-term catalogs. Our results reveal consistent seismicity properties down to decametric…
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Taxonomy
Topicsearthquake and tectonic studies · Seismic Waves and Analysis · High-pressure geophysics and materials
