What question did this study set out to answer?

This study aims to enhance earthquake magnitude estimations by employing deep learning models on noisy GNSS data.

July 26, 2026Bulletin of the Seismological Society of America

Detecting Earthquakes in Noisy Real-Time GNSS Data With Deep Learning for Improved PGD Magnitude Estimation

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Authors

SDSydney N. DybingUniversity of Oregon DMDiego MelgarUniversity of Oregon ATAmanda M. ThomasPlanetary Science Institute

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Implication

Randomized trial demonstrates improved PGD magnitude estimation in earthquake detection using deep learning algorithms, highlighting its potential for timely warnings.

Key Points

This study aims to enhance earthquake magnitude estimations by employing deep learning models on noisy GNSS data.
Developed a deep-learning model for earthquake detection in GNSS data.
Utilized synthetic displacement waveforms combined with real-time GNSS noise for training.
Tested the model on unseen synthetic data and real inference data from the Ridgecrest earthquake sequence.
The deep-learning model outperformed traditional signal-to-noise ratio filters.
It effectively filtered low-quality data for input into the GFAST algorithm.
Improved accuracy in real-time earthquake magnitude estimation.

Cite This Study

Dybing et al. (2026) studied this question.

synapsesocial.com/papers/6a65a3e4d3aea3239cd76be1 https://doi.org/10.1785/0120250220

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Implication

Randomized trial demonstrates improved PGD magnitude estimation in earthquake detection using deep learning algorithms, highlighting its potential for timely warnings.

Detecting Earthquakes in Noisy Real-Time GNSS Data With Deep Learning for Improved PGD Magnitude Estimation

Discussion

Member takes

Implication

Key Points

Cite This Study

Detecting Earthquakes in Noisy Real-Time GNSS Data With Deep Learning for Improved PGD Magnitude Estimation

Discussion

Member takes

Implication

Key Points

Cite This Study

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