  Dynamically triggered earthquakes and tremor generate two classes of weak
seismic signals whose detection, identification, and authentication
traditionally call for laborious analyses. Machine learning (ML) has grown in
recent years to be a powerful efficiency-boosting tool in geophysical analyses,
including the detection of specific signals in time series. However, detecting
weak signals that are buried in noise challenges ML algorithms, in part because
ubiquitous training data is not always available. Under these circumstances, ML
can be as ineffective as human experts are inefficient. At this intersection of
effectiveness and efficiency, we leverage a third tool that has grown in
popularity over the past decade: Citizen science. Citizen science project
Earthquake Detective leverages the eyes and ears of volunteers to detect and
classify weak signals in seismograms from potentially dynamically triggered
(PDT) events. Here, we present the Earthquake Detective data set - A
crowd-sourced set of labels on PDT earthquakes and tremor. We apply Machine
Learning to classify these PDT seismic events and explore the challenges faced
in segregating and classifying such weak signals. We confirm that with an
image- and wavelet-based algorithm, machine learning can detect signals from
small earthquakes. In addition, we report that our ML algorithm can also detect
signals from PDT tremor, which has not been previously demonstrated. The
citizen science data set of classifications and ML code are available online.
