What question did this study set out to answer?

The aim is to develop a deep learning model for rapid magnitude estimation of strong earthquakes using high-rate GNSS data.

July 26, 2026Remote SensingOpen Access

Rapid Strong Earthquake Magnitude Estimation Based on Near-Field High-Rate GNSS Data Using Deep Learning

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Randomized trial demonstrates rapid and accurate earthquake magnitude estimation in near real-time, suggesting improved disaster response.

The aim is to develop a deep learning model for rapid magnitude estimation of strong earthquakes using high-rate GNSS data.
Simulated numerous earthquake ruptures to generate HR-GNSS displacement waveforms.
Trained a deep learning model for near real-time magnitude estimation.
Coded testing with the 2019 Ridgecrest earthquake sequence for validation.
Estimated magnitudes for Mw 7.0+ earthquakes stabilize within 30 seconds with an error of less than 0.15 magnitude units.
Achieved 99% accuracy for Mw 7.2+ events within 40 seconds post-earthquake.
Superior performance compared to broadband seismic peak displacement and HR-GNSS regression methods.

Zhang et al. (2026) studied this question.

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Randomized trial demonstrates rapid and accurate earthquake magnitude estimation in near real-time, suggesting improved disaster response.