Monitoring Local Earthquakes in Central Italy Using 4C Single Station Data.

Andreino Simonelli,Angela Di Virgilio,Matteo Desiderio,Gaetano De Luca,Aladino Govoni

doi:10.3390/s21134297

Abstract

In this study, performed on a set of twenty-two earthquakes that occurred in central Italy between 2019 and 2020, we will explore the possibility to locate the hypocenter of local events by using a ring laser gyroscope observing the vertical ground rotation and a standard broadband seismometer. A picking algorithm exploiting the four components (4C) polarization properties of the wavefield is used to identify the first shear onset transversely polarized (SH). The wavefield direction is estimated by correlation between the vertical rotation rate and the transverse acceleration. The picked times for Pg and Sg onsets are compared to the ones obtained after manual revision on the GIGS station seismometer. The results are compared with the location provided by the national monitoring service of the INGV.

Highlights

The possibility to measure the rotation of the ground induced by seismic waves or seismic noise opened the way to new studies in seismology
We collected twenty-two local earthquakes from July 2019 to February 2020 whose magnitudes are ranging from ML 2.5 to ML 3.8 and distances from 23 to 59 km from the GIGS station
We provide a comparison between the automatic picking routine with the picking values published by the INGV after their final revision in Figures 4 and 5b

Summary

Introduction

The possibility to measure the rotation of the ground induced by seismic waves or seismic noise opened the way to new studies in seismology. The Gingerino gyroscope has been developed as a pathfinder for general relativity tests [5,6] This ensemble of instruments constitutes a four-component (4C) seismic station, and they are available on EIDA under the station name GIGS of the INGV network [7]. The theory underneath rotations in seismology is largely explored in literature [12,13] and new processing studies are available [14,15] as well as new instruments [16]. For this reason, we avoid retreating to the mathematical derivation of fundamental equations for data interpretation. A learning process is used to convert time differences into distances by comparing the location provided by the INGV

Methods

Results

Conclusion