Citizen Scientists Help Detect and Classify Dynamically Triggered Seismic Activity in Alaska

• Main Authors: Vivian Tang, Boris Rösler, Jordan Nelson, JaCoya Thompson, Suzan van der Lee, Kevin Chao, Michelle Paulsen
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-08-01
• Series: Frontiers in Earth Science
• Subjects: earthquake detective; citizen sciences; triggered seismic events; machine-learning algorithm; audible pitches; Alaska
• Online Access: https://www.frontiersin.org/article/10.3389/feart.2020.00321/full

In this citizen science project, we ask citizens to listen to relevant sections of seismograms that are converted to audible frequencies. Citizen scientists helped identify local seismic events whose recorded signals are much smaller than those associated with the surface waves that have triggered these local events. The local events include small earthquakes as well as tectonic tremor. While progress has been made in understanding how these events might be triggered by surface waves from large teleseismic earthquakes around the world, there is no consensus on its physical mechanism. The aim of our project is to engage the help of citizen scientists to increase general knowledge of triggered seismic events that may or may not occur during transient strain changes, such as from propagating surface waves. A better understanding of triggered seismic events is expected to provide important clues toward a fundamental understanding of how earthquakes nucleate and the physical mechanisms that connect different earthquakes and other slip events. From the volunteers’ classifications we determined that citizen scientists achieve a higher reliability in detecting earthquakes and noise than in detecting tremor or other signals and that citizen scientists more accurately identify earthquake signals than a trained machine-learning algorithm. For tremor classifications we currently depend entirely on humans as no machine has yet learned to detect triggered tremor.