  Earthquakes can be detected by matching spatial patterns or phase properties
from 1-D seismic waves. Current earthquake detection methods, such as waveform
correlation and template matching, have difficulty detecting anomalous
earthquakes that are not similar to other earthquakes. In recent years,
machine-learning techniques for earthquake detection have been emerging as a
new active research direction. In this paper, we develop a novel earthquake
detection method based on dictionary learning. Our detection method first
generates rich features via signal processing and statistical methods and
further employs feature selection techniques to choose features that carry the
most significant information. Based on these selected features, we build a
dictionary for classifying earthquake events from non-earthquake events. To
evaluate the performance of our dictionary-based detection methods, we test our
method on a labquake dataset from Penn State University, which contains
3,357,566 time series data points with a 400 MHz sampling rate. 1,000
earthquake events are manually labeled in total, and the length of these
earthquake events varies from 74 to 7151 data points. Through comparison to
other detection methods, we show that our feature selection and dictionary
learning incorporated earthquake detection method achieves an 80.1% prediction
accuracy and outperforms the baseline methods in earthquake detection,
including Template Matching (TM) and Support Vector Machine (SVM).
