Deep learning (DL) methods have a high potential for earthquake detection applications because of their high efficiency at processing measurement data, such as picking seismic phases. However, the performance of DL methods must be evaluated to ensure that they can replace conventional methods so that full automation can be achieved. State-of-art DL methods incorporate advanced techniques and train with large global datasets to enhance their earthquake detection capabilities. In this study, we tested a representative DL model on the 2016 Gyeongju earthquake sequence in the Korean Peninsula and compared the results with a previously established catalog and with the results of the conventional Short Time Average/Long Time Average (STA/LTA) method. The DL model demonstrated reasonable improvements in efficiency and performance by detecting more and smaller earthquakes within a much shorter running time than the other methods. In addition, the DL algorithms generally provided precise pickings of P- and S-wave phases. The DL model showed good generalization because it appropriately detected earthquakes in the study area that were not included in the training dataset. However, our results did suggest possible errors that should be accounted for, such as inconsistent phase picking, missing large earthquakes, and detecting non-natural earthquake signals. From the result of tests, local optimization may be important for realizing fully automatic earthquake monitoring, such as retraining with a local dataset, fine-tuning, or transfer learning. In addition, incorporating post-processing techniques such as phase association and discrimination into the DL framework is necessary.
Bibliographical notePublisher Copyright:
© 2023, The Association of Korean Geoscience Societies and Springer.
- Gyeongju earthquake
- artificial intelligence
- deep learning
- earthquake detection
- phase picking
ASJC Scopus subject areas
- General Environmental Science
- General Earth and Planetary Sciences