MAGNITUDE ESTIMATION OF EARTHQUAKE EARLY WARNING USING MACHINE LEARNING
Main Article Content
Abstract
To help earthquake early warning (EEW) systems make quick decisions, we build a random forest (RF) model for rapid earthquake localization. This system computes the differences in P-wave arrival timings between the first five stations to record an earthquake as a reference station (i.e., the first recording station). The RF model categorises these differential P-wave arrival times and station locations in order to determine the epicentral position. Using a Japanese earthquake catalogue, we train and evaluate the suggested algorithm. The Mean Absolute Error (MAE) of the RF model, which forecasts earthquake sites, is 2.88 km. Importantly, the suggested RF model can learn from little data—10% of the dataset—and a lot fewer recording stations—three—and yet get good results (MAE5 km). The approach provides a potent new tool for quick and precise source-location prediction in EEW since it is accurate, generalizable, and responsive
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Q. Kong, R. M. Allen, L. Schreier, and Y.-W.
Kwon,“Myshake: A smartphone seismic network for
earthquake early warning and beyond,” Science
advances, vol. 2, no. 2, p. e1501055, 2016.
T.-L. Chin, K.-Y. Chen, D.-Y. Chen, and D.-E. Lin,
“Intelligent real-time earthquake detection by recurrent
neural networks,” IEEE Transactions on Geoscience
and Remote Sensing, vol. 58, no. 8, pp. 5440–5449,
T.-L. Chin, C.-Y. Huang, S.-H. Shen, Y.-C. Tsai,
Y. H. Hu, and Y.-M.Wu, “Learn to detect: Improving
the accuracy of earthquake detection,” IEEE
Transactions on Geoscience and Remote Sensing, vol.
, no. 11, pp. 8867–8878, 2019.
O. M. Saad, A. G. Hafez, and M. S. Soliman,
“Deep learning approach for earthquake parameters
classification in earthquake early warning system,”
IEEE Geoscience and Remote Sensing Letters, pp. 1–5,
X. Zhang, J. Zhang, C. Yuan, S. Liu, Z. Chen, and
W. Li, “Locating induced earthquakes with a network
of seismic stations in oklahoma via a deep learning
method,” Scientific reports, vol. 10, no. 1, pp. 1–12,
L. Breiman, “Random forests,” Machine learning,
vol. 45,no. 1, pp. 5–32, 2001.
S. M. Mousavi, W. L. Ellsworth, W. Zhu, L. Y.
Chuang, and G. C. Beroza, “Earthquake transformeran
attentive deep-learning model for simultaneous
earthquake detection
and phase picking,” Nature Communications, vol. 11,
no. 1, pp. 1–12, 2020.
S. M. Mousavi and G. C. Beroza, “A MachineLearning Approach for Earthquake Magnitude
Estimation,” Geophysical Research Letters, vol. 47, no.
, p.e2019GL085976, 2020.