Mousavi, S. M., Zhu, W., Sheng, Y., & Beroza, G. C. (2019). CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection. Scientific reports, 9(1), 1-14.
pip install -r requirements.txt
@article{mousavi2019cred,
title={CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection},
author={Mousavi, S Mostafa and Zhu, Weiqiang and Sheng, Yixiao and Beroza, Gregory C},
journal={Scientific reports},
volume={9},
number={1},
pages={1--14},
year={2019},
publisher={Nature Publishing Group}
}
Link 1: (https://www.nature.com/articles/s41598-019-45748-1)
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations.
