Feature Engineering and Clustering for Single-Station Seismic Waveform Classification in an Urban Environment
To improve seismic event detection and classification in our increasingly urban and often sparsely-instrumented environment, it is important to expand our single-station methods to detect small seismic events in highly-fluctuating, high-amplitude background noise. In this study, we aim to identify effective waveform features which can detect and discriminate small local earthquakes, explosions such as quarry blasts, recurring industrial activity, and other sources of environmental and anthropogenic noise in urban seismic data. Some explored features include measures of power spectral density (PSD) misfit and modified STA/LTA ratios using skewness and kurtosis in the frequency domain. To assess the ability of our features in detecting transient events in urban seismic data, we apply a simple unsupervised learning model (K-means clustering) to continuous feature data from a single broadband station in the Chicago area. We systematically investigate how the findings of our clustering model change with additional features and processing steps. For instance, we explore how filtering our waveforms at characteristic frequency bands of environmental and anthropogenic noise can improve our model performance. We will present a few notable clusters of seismic events in the Chicago area and discuss their characterizing features and possible sources. To assess the efficacy of our features in different urban environments, we also apply our clustering model to continuous data from a single station in Singapore and present our preliminary findings. We conclude by discussing additional features and methods that will be explored in the future to improve our model performance and analysis.
Session: Detecting, Locating, Characterizing and Monitoring Non-earthquake Seismoacoustic Sources
Type: Oral
Room: 209C
Date: 4/19/2023
Presentation Time: 02:45 PM (local time)
Presenting Author: Ann Mariam Thomas
Student Presenter: Yes
Additional Authors
Ann Mariam Thomas Presenting Author Corresponding Author annthomas2025@u.northwestern.edu Northwestern University |
Omkar Ranadive omkar.ranadive@u.northwestern.edu Northwestern University |
Suzan van der Lee suzan@northwestern.edu Northwestern University |
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Feature Engineering and Clustering for Single-Station Seismic Waveform Classification in an Urban Environment
Category
Detecting, Locating, Characterizing and Monitoring Non-earthquake Seismoacoustic Sources
Description