Earthquake Magnitude Estimation

The size of an earthquake at its source is measured from the amplitude (or sometimes the duration) of the motion recorded on seismograms, and is expressed in terms of magnitude. Magnitude is a logarithmic measure. At the same distance from the earthquake, the amplitude of the seismic waves from which the magnitude is determined are 10 times as large during a magnitude 5 earthquake as during a magnitude 4 earthquake. The total amount of energy released by an average earthquake, depending on magnitude type, increases by a factor of approximately 32 for each unit increase in magnitude.

Used ML Approaches:

• Support Vector Machine

• Artificial Neural Networks

• Bayesian Network

Used Neural Networks:

• FC

• CNN

• RNN

• Transformer

• LSTM

Used Learning Procedures:

• Supervised Learning

• Transfer Learning

References:

1. Ochoa, L. H., Niño, L. F., & Vargas, C. A. (2018). Fast magnitude determination using a single seismological station record implementing machine learning techniques. Geodesy and Geodynamics, 9(1), 34-41.

2. Reddy, R., & Nair, R. R. (2013). The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan. Journal of Earth System Science, 122(5), 1423-1434.

3. Böse, M., Wenzel, F., & Erdik, M. (2008). PreSEIS: A neural network-based approach to earthquake early warning for finite faults. Bulletin of the Seismological Society of America, 98(1), 366-382.

4. Böse, M., Heaton, T., & Hauksson, E. (2012). Rapid estimation of earthquake source and ground‐motion parameters for earthquake early warning using data from a single three‐component broadband or strong‐motion sensor. Bulletin of the Seismological Society of America, 102(2), 738-750.

5. Zazzaro, G., Pisano, F. M., & Romano, G. (2012). Bayesian networks for earthquake magnitude classification in a early warning system. World Acad. Sci. Eng. Technol, 6, 4-27.

6. Käufl, P., Valentine, A. P., O'Toole, T. B., & Trampert, J. (2014). A framework for fast probabilistic centroid-moment-tensor determination—inversion of regional static displacement measurements. Geophysical Journal International, 196(3), 1676-1693.

7. Käufl, P., Valentine, A. P., & Trampert, J. (2016). Probabilistic point source inversion of strong‐motion data in 3‐D media using pattern recognition: A case study for the 2008 Mw 5.4 Chino Hills earthquake. Geophysical Research Letters, 43(16), 8492-8498.

8. Lomax, A., Michelini, A., & Jozinović, D. (2019). An investigation of rapid earthquake characterization using single‐station waveforms and a convolutional neural network. Seismological Research Letters, 90(2A), 517-529.

9. Saad, O. M., Hafez, A. G., & Soliman, M. S. (2020). Deep learning approach for earthquake parameters classification in earthquake early warning system. IEEE Geoscience and Remote Sensing Letters.

10. van den Ende, M., & Ampuero, J. P. (2020). Automated seismic source characterisation using deep graph neural networks.

11. Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophysical Journal International, 226(2), 1086-1104.

12. Wibowo, A., Pratama, C., Sahara, D. P., Heliani, L. S., Rasyid, S., Akbar, Z., ... & Sudrajat, A. (2021). Earthquake Early Warning System Using Ncheck and Hard-Shared Orthogonal Multitarget Regression on Deep Learning. IEEE Geoscience and Remote Sensing Letters.

13. Mousavi, S. M., & Beroza, G. C. (2020). A machine‐learning approach for earthquake magnitude estimation. Geophysical Research Letters, 47(1), e2019GL085976.

14. Ristea, N. C., & Radoi, A. (2021). Complex Neural Networks for Estimating Epicentral Distance, Depth, and Magnitude of Seismic Waves. IEEE Geoscience and Remote Sensing Letters.

15. Zhu, J., Li, S., Song, J., & Wang, Y. (2021). Magnitude estimation for earthquake early warning using a deep convolutional neural network. Frontiers in Earth Science, 9, 341.

16. Lin, J. T., Melgar, D., Thomas, A., & Searcy, J. (2021). Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning.

17. Kuang, W., Yuan, C., & Zhang, J. (2021). Network‐Based Earthquake Magnitude Determination via Deep Learning. Seismological Research Letters.

18. Wang, Y., Wang, Z., Cao, Z., & Lan, J. (2019). Deep learning for magnitude prediction in earthquake early warning. arXiv preprint arXiv:1912.05531.