Poster #214, Seismology
A tale of urban seismology: ambient seismic noise, machine learning methods, and seismic hazard analysis at the Seattle basin edge
Poster Image:
![](https://files.scec.org/s3fs-public/publication_11436.jpg?wA87GklohlnabI3NUpdyCvmj2C1XFyep)
Poster Presentation
2021 SCEC Annual Meeting, Poster #214, SCEC Contribution #11436 VIEW PDF
hosted by Seattle residents, and installation included educational involvement with residents and students. We process and perform auto-correlation and cross-correlation analysis of the continuous ambient seismic noise data. Correlations are contaminated by anthropogenic activities, particularly highways, which causes spurious arrivals. To select the optimal correlations to generate the stack, we perform Gaussian mixture model clustering. We automate the clustering of correlations that we attribute to daytime and quiet times (nighttime and weekends). We further investigate if the re-constructed single-station correlations enable us to observe subsurface geological interfaces. In particular, we attempt to characterize the structure of the Seattle fault zone.
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