To facilitate identification of conditions that lead to the dynamic triggering of seismic events as catalogs of these events keep growing, we applied a machine-learning algorithm (decision tree) to a published data set of known instances of dynamically triggered seismic tremor in central California. To investigate the possible universality of our findings and to further test the algorithm, we also applied it to new observations, presented here, of potentially dynamically triggered seismic activity in three intraplate regions: Raton Basin (CO), Yellowstone, and central Utah. We report potential tremor or local earthquake signals from here during the propagation of surface waves from the 2012 Mw 8.6 Sumatra earthquake. These surface waves also triggered seismic activity along the western boundary of the North American plate and did not trigger seismic activity in the central and eastern USA. We report additional potential dynamic triggering in the three aforementioned intraplate regions from an investigation of seismograms from 37 additional large earthquakes, recorded between 2004 to 2017. Our findings show that transient stresses generated by surface waves from large earthquakes and arriving from favorable directions generally lead to triggered tremor in seismically, volcanically, and hydrothermally active regions like central California and possibly Yellowstone. These stresses do not appear to be decisive factors for the potentially dynamically triggered local earthquakes reported for the Raton Basin and central Utah, while surface waves’ incidence angles do appear to be important there.

We are engaging citizen scientists in an experiment to test if many human ears can replace the process of a professional seismologist in identifying dynamically triggered seismic events. Ordinarily, this process involves interactive data processing and visualization efforts on a volume of earthquake recordings (seismograms) that exploded during the recent big-data revolution, for example through EarthScope. In this citizen seismology project, we ask citizens to listen to relevant sections of seismograms that are accelerated to audible frequencies. This approach has five advantages: 1) The human ear implicitly performs a time-frequency analysis and is capable of discerning a wide range of different signals, 2) Many human ears listening to the same data provides statistics that rank seismograms in order of their likelihood to contain a recording of a triggered event, which is helpful to researchers’ analysis of this data as well as to 3) the ability of a deep-learning algorithm to model the boolean identifications or bulk statistics of the analyses, 4) the project has the potential to enhance informal learning because of the online platform that hosts the project, Zooniverse, is available to people of all identities and hosts many other citizen science projects, and 5) it helps prepare our team for diverse post-graduation careers as part of IDEAS, an NRT program at Northwestern University. The events we are asking citizens to help identify via listening are small seismic events such as local earthquakes and tectonic tremor, that occur in response to transient stresses from passing seismic surface waves from a large, distant earthquake. While much research progress has been made in understanding how these events are triggered, there is no reliable deterministic recipe for their occurrence. The aim of our project is to enlist the help of citizens to increase the data set of known triggered seismic events and known absences of triggered events in order to help researchers unravel key aspects of that recipe. A better understanding of triggered seismic events is expected to provide important clues towards a fundamental understanding of all seismic activity, including damaging earthquakes.

Dynamically triggered earthquakes and tremor generate weak seismic signals whose detection, identification, and authentication call for a laborious analysis. 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 such weak signals. The algorithm confirms that machine learning can detect signals from small earthquakes, and newly demonstrates that this specific algorithm can also detect signals from PDT tremor. The data set and code are available online.

To facilitate identification of conditions that lead to the dynamic triggering of seismic events as catalogs of these events keep growing, we applied a machine-learning algorithm (decision tree) to a published data set of known instances of dynamically triggered seismic tremor in central California. To investigate the possible universality of our findings and to further test the algorithm, we also applied it to new observations, presented here, of potentially dynamically triggered seismic activity in three intraplate regions: Raton Basin (CO), Yellowstone, and central Utah. We report potential tremor or local earthquake signals from here during the propagation of surface waves from the 2012 M8.6 Sumatra earthquake. These surface waves also triggered seismic activity along the western boundary of the North American plate and did not trigger seismic activity in the central and eastern USA. We report additional potential dynamic triggering in the three aforementioned intraplate regions from an investigation of seismograms from 37 additional large earthquakes, recorded between 2004 to 2017. Our findings show that transient stresses generated by surface waves from large earthquakes and arriving from favorable directions generally lead to triggered tremor in seismically, volcanically, and hydrothermally active regions like central California and possibly Yellowstone. These stresses do not appear to be decisive factors for the potentially dynamically triggered local earthquakes reported for the Raton Basin and central Utah, while surface waves’ incidence angles do appear to be important there.