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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Cited articles

Agarap, A. F.: Deep learning using rectified linear units (relu), arXiv [preprint], arXiv:1803.08375,, 2018. a
Akazawa, T.: A technique for automatic detection of onset time of P-and S-phases in strong motion records, in: Proc. of the 13th World Conf. on Earthquake Engineering, vol. 786, p. 786, Vancouver, Canada, (last access: May 2023), 1–4 August 2004, Vancouver B.C., Canada, 2004. a, b
Allen, R. V.: Automatic earthquake recognition and timing from single traces, B. Seismol. Soc. Am., 68, 1521–1532, 1978. a
Bogazici University Kandilli Observatory and Earthquake Research Institute National Earthquake Monitoring Center:, last access: May 2023. a
California Institute of Technology (Caltech): Southern California Seismic Network,, last access: May 2023. a
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.