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Chai, C., Maceira, M., Santos-Villalobos, H.J., Venkatakrishnan, S.V., Schoenball, M., Zhu, W., et al. (2020) Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking. Geophysical Research Letters, 47, e2020GL088651.
https://doi.org/10.1029/2020gl088651

has been cited by the following article:

• TITLE: Improving Earthquake Signal Detection Model Based on Transfer Learning

  AUTHORS: Yuling Liu, Xinxin Yin

  KEYWORDS: Deep Learning, Transfer Learning, Seismic Detection, Gansu Region

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.16 No.9, September 21, 2026

  ABSTRACT: Seismic wave detection is of great significance for seismic monitoring, early warning and disaster reduction. To improve the accuracy and practicability of seismic wave detection in Gansu, this study adopts a transfer learning method. On the basis of the pre-trained PhaseNet model, local Gansu seismic data are used for fine-tuning to boost the model’s generalization ability in this region. To verify the validity of the proposed method, we conduct comparative experiments with multiple seismic detection models on the waveform dataset collected from the Gansu Seismic Network. The results show that the transfer learning optimized model PhaseNet-TL surpasses the original model and other algorithms in detection precision and stability. Notably, its error indicator is reduced by about seven times. This proves that transfer learning can drastically strengthen the model’s adaptability to local seismic characteristics. This study provides a paradigm for the regional application of models in seismic monitoring, and also expands the ideas for the application of transfer learning in seismology.