Enhanced earthquake detection from orbit

A new algorithm has been shown to optimize the use of data for sensing earthquakes from global navigation satellite system sensors, such as this one on the Aleutian Peninsula in Alaska. Image credit: Ellie Boyce/UNAVCO, CC BY 4.0

When a major earthquake strikes, nearby seismographs can communicate quick alerts to residents and emergency services that potentially dangerous tremors or tsunamis may be on the way. However, local seismometer measurements are not sufficient to determine the magnitude of the largest earthquakes in real time.

Scientists used high-resolution measurements of Earth’s displacement state from global navigation satellite systems (GNSS), such as the Global Positioning System (GPS), to complement the seismometer observations. GNSS data can differentiate between larger earthquakes but is much noisier than data from conventional seismometers, which has limited its contributions to natural hazard applications.

To process the noisy data, Dittmann and colleagues made two decision choices based on previous GNSS seismology research and machine learning development: they adopted an alternative approach to processing geodetic measurements, and they trained a machine learning model to use GNSS sensor data to detect earthquakes. . The team trained, validated, and tested the model using data from the National Science Foundation’s Geodetic Facility for Geosciences Archives from 77 earthquakes larger than magnitude 4.5 that occurred over a 20-year period.

When matched with existing GNSS earthquake detection methods, the new model detected more real seismic signals and triggered fewer false alarms. In addition, unlike previous approaches, the new model is based on computationally lightweight processing and does not rely on additional corrections to account for false signals.

The researchers suggest that the new model can be widely applied to enhance the role of GNSS sensors in earthquake detection. It also outlines opportunities for future improvement, such as applying more comprehensive data sets to train and validate the model.

The study was published in the journal Geophysical Research: Solid Earth.

Further information: T Dittmann et al., Supervised Machine Learning of High-Speed ​​GNSS Accelerations of Strong Motion Signals for Earthquakes, Journal of Geophysical Research: Solid Earth (2022). doi: 10.1029/2022JB024854

Provided by the American Geophysical Union

This story is republished with permission from Eos, and hosted by the American Geophysical Union. Read the original story here.

Citation: Enhanced Earthquake Detection from Orbit (2022, December 5) Retrieved December 5, 2022 from https://phys.org/news/2022-12-earthquake-orbit.html

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