Seismic Background Noise of Italian Strong Motion Network
Description:
Italian strong motion network monitors Italian territory and its surrounding with more than 700 seismic stations. Recent development of the strong motion network, now it is possible to determine the noise level of the network. In this study, background noise of the network has been processed in three different time ranges that are 2019, covid-19 lockdown period, and 2022. To do that, power spectrum density is calculated for the continuous stations. Due to the nature of the seismic instrument and the periods that are significant for the purpose of the network, periods lower than 5s are analyzed. Since the Italian strong motion network is deployed in order to detect the effects of the seismic events in urban areas, stations are mostly installed in towns and cities. Hence, the background noise is dominated by the human activity. This effect can be seen up to 14 decibel noise level changed between day time and night time. It is found that stations located in the city centers are suffered from the anthropogenic sources. During the covid-19 lockdown, background noise levels are dropped up to 6.5 decibels in day time and overall noise are reduced significantly. Due to the deployment of the stations in urban areas, stations are also affected by the vehicles. Their effect can be seen in several distinctive period ranges.
Session: Network Seismology: Recent Developments, Challenges and Lessons Learned [Poster]
Type: Poster
Date: 4/20/2023
Presentation Time: 08:00 AM (local time)
Presenting Author: Deniz Ertuncay
Student Presenter: No
Invited Presentation:
Authors
Deniz Ertuncay Presenting Author Corresponding Author dertuncay@units.it Universita Degli Studi Di Trieste |
Simone Francesco Fornasari simonefrancesco.fornasari@studenti.units.it Universita Degli Studi Di Trieste |
Giovanni Costa costa@units.it Universita Degli Studi Di Trieste |
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Seismic Background Noise of Italian Strong Motion Network
Category
Network Seismology: Recent Developments, Challenges and Lessons Learned