Artificial Intelligence Discovers More Than 86,000 Earthquakes Beneath Yellowstone
In a groundbreaking study published in the journal Science Advances, scientists have used artificial intelligence (AI) to uncover previously undetected earthquake activity in the Yellowstone Caldera. The research, led by researchers from the University of Western Ontario, Universidad Industrial de Santander in Colombia, and the US Geological Survey, has significantly increased our understanding of the region's seismic activity.
The study reveals that AI's ability to analyze vast seismic datasets rapidly and identify subtle patterns has allowed scientists to detect over 86,000 earthquakes between 2008 and 2022, ten times more than previously recorded using traditional methods. This substantial increase in data provides a higher-resolution, longer-term seismic record, helping researchers better understand the interactions of magma, hydrothermal fluids, and faults in this volcanic region.
One of the key findings of the study is the discovery of complex swarm dynamics along rougher, younger, and less-developed fault lines. These findings suggest that seismic activity is concentrated in these evolving geological features, offering much richer and more detailed earthquake catalogs. The rougher faults inside the caldera are likely influenced by a combination of slow-moving underground water and sudden bursts of fluid, which may be triggering the clusters of small quakes.
The study also found that more than half of the earthquakes are clustered into swarm-like families, exhibiting dynamic behaviors such as episodes of hypocenter expansion and migration within the caldera. This AI-driven approach reshapes the scientific understanding of Yellowstone’s underground geological behavior, providing valuable insights into the mechanisms of how one earthquake triggers another in a swarm, although a systematic understanding remains elusive.
The implications of this research are far-reaching. The AI can help improve safety measures, better inform the public about potential risks, and guide geothermal energy development away from danger in areas with promising heat flow. By providing a more accurate and comprehensive understanding of the seismic activity in the Yellowstone Caldera, this study contributes to improved monitoring and hazard assessment of this active supervolcano.
Bing Li, study author and engineering professor at the University of Western Ontario, stated that if the data were analyzed manually, it wouldn't be scalable. The AI's ability to sift through vast amounts of seismic data and detect subtle patterns offers a scalable solution to the challenge of understanding complex seismic activity in regions like the Yellowstone Caldera.
[1] Bing Li et al., "AI-driven seismic analysis of the Yellowstone Caldera reveals previously undetected swarm activity," Science Advances, 10 December 2022, DOI: 10.1126/sciadv.abm1654 [2] University of Western Ontario, "New study reveals previously undetected earthquake swarms in Yellowstone Caldera using AI," 10 December 2022,
- This study, published in Science Advances, employed artificial intelligence (AI) to delve into unprecedented earthquake activity in the Yellowstone Caldera, a field traditionally dominated by engineering and geology.
- The collaboration between researchers from the University of Western Ontario, Universidad Industrial de Santander in Colombia, and the US Geological Survey utilized AI's rapid data analysis capabilities, detecting over 86,000 earthquakes between 2008 and 2022.
- The application of AI in environmental-science research brought forth a wealth of data, which in turn furnished scientists with a higher-resolution, longer-term seismic record, facilitating a deeper understanding of the caldera's seismic events and the interactions of magma, hydrothermal fluids, and faults within.
- The discoveries gleaned from this research offer implications that reach beyond science, as AI's ability to sift through voluminous seismic data could help enhance safety measures, inform the public about potential risks, and guide geothermal energy development away from danger zones in regions with promising heat flow.
