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

Data sets

Southern California Seismic Network California Institute of Technology (Caltech)

A Global Data Set of Seismic Signals for AI S. M. Mousavi

INSTANCE The Italian Seismic Dataset For Machine Learning A. Michelini

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.