Monitoring Time Variations in Seismic Noise Amplitude at Permanent Seismic Networks

The quality control process usually followed at broad-band seismic networks includes the calculation of the power spectral density and their probability density functions. These results do not make possible a quick estimation of temporal variations that can result from non-continuous sources of noise, meteorologic phenomena, etc. We propose the use of the SeismoRMS package, originally developed to analyze the seismic amplitude variations associated with the COVID19 lockdown, to monitor the time evolution of seismic noise sources in a permanent network, using as a case example the dataset collected during 2023 by the CA network in NE Iberia. Frequencies above 1 Hz show remarkable differences between the stations, despite sharing similar installation settings. Most of the sites show day/night and working day/weekend variations, suggesting a relevant contribution of anthropic sources, but the amplitude of such variations differs strongly among the sites. Our study allows us to identify specific sources of noise affecting some sites during short and regular time periods, an aspect that needs to be taken into account when evaluating the overall quality of each site. We conclude that a systematic analysis of the amplitude variations at different frequency bands can be a tool of interest for the management of a broad-band seismic network.

Authors

Publication Details

Journal
Seismica
Published
2025-04-07
DOI
https://doi.org/10.26443/seismica.v4i1.1408
Citations
1
Primary Topic
Seismology and Earthquake Studies
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monitoring Time Variations in Seismic Noise Amplitude at Permanent Seismic Networks

T. Frontera, José-Antonio Jara, Thomas Lecocq, Helena Seivane et al.
1 citations
Seismica
Seismology and Earthquake Studies
2.48
article

Monitoring Time Variations in Seismic Noise Amplitude at Permanent Seismic Networks

T. Frontera, José-Antonio Jara, Thomas Lecocq, Helena Seivane, Jordi Díaz, Pilar Sánchez‐Pastor, Samuel Jorde
article en
1 citations

Abstract

The quality control process usually followed at broad-band seismic networks includes the calculation of the power spectral density and their probability density functions. These results do not make possible a quick estimation of temporal variations that can result from non-continuous sources of noise, meteorologic phenomena, etc. We propose the use of the SeismoRMS package, originally developed to analyze the seismic amplitude variations associated with the COVID19 lockdown, to monitor the time evolution of seismic noise sources in a permanent network, using as a case example the dataset collected during 2023 by the CA network in NE Iberia. Frequencies above 1 Hz show remarkable differences between the stations, despite sharing similar installation settings. Most of the sites show day/night and working day/weekend variations, suggesting a relevant contribution of anthropic sources, but the amplitude of such variations differs strongly among the sites. Our study allows us to identify specific sources of noise affecting some sites during short and regular time periods, an aspect that needs to be taken into account when evaluating the overall quality of each site. We conclude that a systematic analysis of the amplitude variations at different frequency bands can be a tool of interest for the management of a broad-band seismic network.

SeismicaVol. 4(1)
Ministerio de Ciencia e Innovación
Openalex Percentile: Top 11%
Seismology and Earthquake Studies
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.