Rapid Identification of Rupture Depth in Subduction Zone Earthquakes Based on High-Rate GNSS Using Deep Learning

Destructive tsunamis are often triggered by shallow coseismic ruptures in subduction zones, making the rapid determination of rupture depth crucial for issuing timely tsunami warnings and mitigating associated hazards. To address this challenge, we propose a deep learning framework for the rapid classification of rupture depth (shallow or deep) based on high-rate GNSS data.

Using the Alaska subduction zone as a case study, we generated nearly 10,000 synthetic earthquake scenarios to overcome the scarcity of real-world megathrust earthquake records. From these simulations, we constructed a comprehensive near-field GNSS three-component displacement waveform database. Leveraging this dataset, we designed a deep learning neural network that extracts critical seismic signal features from high-rate GNSS data to accurately classify rupture depth. The model achieved over 90% accuracy, precision, and recall on the test set.

We applied the model to the 2021 Mw 8.2 Alaska earthquake and successfully identified it as a deep rupture, with a processing time of approximately 20 ms. Additionally, through transfer learning, we extended the model to the Sumatra subduction zone and successfully identified the 2010 Mw 7.8 Mentawai earthquake as a shallow rupture. This study provides a valuable reference for enhancing the reliability of tsunami early warning systems.