Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection

Technical Report · Mon Mar 14 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1860919· OSTI ID:1860919

Kong, Qingkai ^[1]; Beroza, Gregory ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

This project is to explore ground motion spatial distribution using a new approach graph-based network analysis. In this study, we combine a large-N seismic array and graph analytics to explore spatial variability and correlation at a local scale using small local and regional earthquakes. In this method, each seismic station is modeled as a node and the similarities of the waveforms that represent ground motions between two stations are modeled as edges. By analyzing this graph network using the similarity matrices and community detection algorithm, we can group the stations spatially with similar patterns. A random forest algorithm is used to reveal the important features that affect the spatial grouping. The result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements of seismic wavefields.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); Southern California Earthquake Center (SCEC)
• DOE Contract Number: AC52-07NA27344
• OSTI ID: 1860919
• Report Number(s): LLNL-TR-832758; 1050558
• Country of Publication: United States
• Language: English

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