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Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/se-15-197-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-15-197-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Method article
 | 
09 Feb 2024
Method article |  | 09 Feb 2024

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Wei Li, Megha Chakraborty, Jonas Köhler, Claudia Quinteros-Cartaya, Georg Rümpker, and Nishtha Srivastava

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Latest update: 13 Dec 2024
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Short summary
Seismic phase picking and magnitude estimation are crucial components of real-time earthquake monitoring and early warning. Here, we test the potential of deep learning in real-time earthquake monitoring. We introduce DynaPicker, which leverages dynamic convolutional neural networks for event detection and arrival-time picking, and use the deep-learning model CREIME for magnitude estimation. This workflow is tested on the continuous recording of the Turkey earthquake aftershock sequences.
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