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The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11958803 | PMC |
http://dx.doi.org/10.1038/s41467-025-57997-y | DOI Listing |
Sci Rep
August 2025
Earthquake Monitoring Center, Sultan Qaboos University, PC: 123 Al Khoudh, Muscat, Oman.
This study presents a cutting-edge framework for assessing earthquake vulnerability and risk in residential areas of Al-Seeb, Muscat Governorate (Sultanate of Oman). Drawing upon a rich dataset encompassing seismic, geotechnical, structural, environmental, and socioeconomic parameters, thematic vulnerability maps were developed using a GIS-based analytic hierarchy process (GIS-AHP). These were systematically integrated to produce comprehensive risk matrices.
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August 2025
Department of Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, Korea.
Local Intensive Precipitation (LIP), intensified by climate change, has increasingly caused severe urban flooding. Although traditional hydrodynamic models such as SWMM and FLO-2D offer high accuracy in flood prediction, their computational demands hinder real-time application. This study introduces a rapid flood depth prediction model based on a Support Vector Machine (SVM), trained with data generated from a physically-based 1D-2D coupled simulation.
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August 2025
Department of Architecture and Architectural Engineering, Yonsei University, Seoul, 03722, Korea.
Structural health monitoring (SHM) systems play a critical role in ensuring the safety of buildings during seismic events. However, their effectiveness is limited when sensor data is lost due to malfunction, damage, or communication failure. To address this issue, this study proposes a bi-directional urban safety network that utilizes LSTM models to predict the dynamic structural responses of buildings based on the responses of adjacent structures within the network.
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October 2025
College of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China.
Landslides pose significant threats to human life and infrastructure globally. In China, the intensification of urbanization and human activities has exacerbated loess landslide risks, making monitoring and mitigation efforts increasingly critical. Rainfall, surface displacement, pore pressure, and seismic waves as key parameters for landslide monitoring.
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August 2025
Senseable City Lab, MIT, Cambridge, MA, USA.
Despite the variability of urban infrastructure, unreinforced masonry buildings remain globally prevalent. Constructed from brick, hollow concrete blocks, stone, or other masonry materials, these structures account for a significant proportion of fatalities during seismic events-particularly in regions with limited access to early warning systems. Due to the complex behavior of masonry, accurately assessing structural vulnerabilities is highly dependent on the chosen modeling strategy.
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