Cargando…

Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space

Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measu...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yunyue Elita, Nilot, Enhedelihai Alex, Zhao, Yumin, Fang, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920942/
https://www.ncbi.nlm.nih.gov/pubmed/36772362
http://dx.doi.org/10.3390/s23031322
_version_ 1784887194237796352
author Li, Yunyue Elita
Nilot, Enhedelihai Alex
Zhao, Yumin
Fang, Gang
author_facet Li, Yunyue Elita
Nilot, Enhedelihai Alex
Zhao, Yumin
Fang, Gang
author_sort Li, Yunyue Elita
collection PubMed
description Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management.
format Online
Article
Text
id pubmed-9920942
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99209422023-02-12 Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space Li, Yunyue Elita Nilot, Enhedelihai Alex Zhao, Yumin Fang, Gang Sensors (Basel) Article Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management. MDPI 2023-01-24 /pmc/articles/PMC9920942/ /pubmed/36772362 http://dx.doi.org/10.3390/s23031322 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Yunyue Elita
Nilot, Enhedelihai Alex
Zhao, Yumin
Fang, Gang
Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title_full Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title_fullStr Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title_full_unstemmed Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title_short Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space
title_sort quantifying urban activities using nodal seismometers in a heterogeneous urban space
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920942/
https://www.ncbi.nlm.nih.gov/pubmed/36772362
http://dx.doi.org/10.3390/s23031322
work_keys_str_mv AT liyunyueelita quantifyingurbanactivitiesusingnodalseismometersinaheterogeneousurbanspace
AT nilotenhedelihaialex quantifyingurbanactivitiesusingnodalseismometersinaheterogeneousurbanspace
AT zhaoyumin quantifyingurbanactivitiesusingnodalseismometersinaheterogeneousurbanspace
AT fanggang quantifyingurbanactivitiesusingnodalseismometersinaheterogeneousurbanspace