Ground Motion Characterization

Estimates of distinct attributes of the ground motion caused by a seismic source like the maximum of the ground shaking level (e,g. Peak Ground Acceleration (PGA), Peak Ground Displacement (PGD), or Pseudo Spectral Accelerations (PSA)), seismic intensity, or the travel time of a seismic wave between two points in a medium.

Used ML Approaches:

  • Neuro Fuzzy

  • Random Forest

  • Artificial Neural Networks

Used Neural Networks:

  • FC

  • CNN

  • RNN

  • Transformer

  • LSTM

  • PINN

Used Learning Procedures:

  • Supervised Learning

  • Unsupervised Learning

  • Active Learning

References:

  1. Thomas, S., Pillai, G. N., Pal, K., & Jagtap, P. (2016). Prediction of ground motion parameters using randomized ANFIS (RANFIS). Applied Soft Computing, 40, 624-634.

  2. Derras, B., Bard, P. Y., & Cotton, F. (2016). Site-condition proxies, ground motion variability, and data-driven GMPEs: Insights from the NGA-West2 and RESORCE data sets. Earthquake spectra, 32(4), 2027-2056.

  3. Ameur, M., Derras, B., & Zendagui, D. (2018). Ground motion prediction model using adaptive neuro-fuzzy inference systems: an example based on the NGA-West 2 data. Pure and Applied Geophysics, 175(3), 1019-1034.

  4. Trugman, D. T., & Shearer, P. M. (2018). Strong correlation between stress drop and peak ground acceleration for recent M 1–4 earthquakes in the San Francisco Bay area. Bulletin of the Seismological Society of America, 108(2), 929-945.

  5. Kubo, H., Kunugi, T., Suzuki, W., Suzuki, S., & Aoi, S. (2020). Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation. Scientific reports, 10(1), 1-12.

  6. Kerh, T., & Ting, S. B. (2005). Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. Engineering Applications of Artificial Intelligence, 18(7), 857-866.

  7. Güllü, H., & Erçelebi, E. (2007). A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey. Engineering Geology, 93(3-4), 65-81.

  8. Ahmad, I., El Naggar, M. H., & Khan, A. N. (2008). Neural network based attenuation of strong motion peaks in Europe. Journal of earthquake Engineering, 12(5), 663-680.

  9. Günaydın, K., & Günaydın, A. (2008). Peak ground acceleration prediction by artificial neural networks for northwestern Turkey. Mathematical Problems in Engineering, 2008.

  10. Kuyuk, H. S., & Motosaka, M. (2009). Real-time ground motion forecasting using front-site waveform data based on artificial neural network. Journal of Disaster Research, 4(4), 261.

  11. Derras, B., Bard, P. Y., Cotton, F., & Bekkouche, A. (2012). Adapting the neural network approach to PGA prediction: An example based on the KiK‐net data. Bulletin of the Seismological Society of America, 102(4), 1446-1461.

  12. Derras, B., Bard, P. Y., & Cotton, F. (2014). Towards fully data driven ground-motion prediction models for Europe. Bulletin of Earthquake Engineering, 12(1), 495-516.

  13. Dhanya, J., & Raghukanth, S. T. G. (2018). Ground motion prediction model using artificial neural network. Pure and Applied Geophysics, 175(3), 1035-1064.

  14. Khosravikia, F., Clayton, P., & Nagy, Z. (2019). Artificial neural network‐based framework for developing ground‐motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas. Seismological Research Letters, 90(2A), 604-613.

  15. Wiszniowski, J. (2019). Estimation of a ground motion model for induced events by Fahlman's Cascade Correlation Neural Network. Computers & Geosciences, 131, 23-31.

  16. Derakhshani, A., & Foruzan, A. H. (2019). Predicting the principal strong ground motion parameters: A deep learning approach. Applied Soft Computing, 80, 192-201.

  17. Withers, K. B., Moschetti, M. P., & Thompson, E. M. (2020). A machine learning approach to developing ground motion models from simulated ground motions. Geophysical Research Letters, 47(6), e2019GL086690.

  18. Wang, Z., Zentner, I., & Zio, E. (2020). Accounting for Uncertainties of Magnitude‐and Site‐Related Parameters on Neural Network‐Computed Ground‐Motion Prediction EquationsAccounting for Uncertainties of Magnitude‐and Site‐Related Parameters on Neural Network‐Computed Ground‐Motion Prediction Equations. Bulletin of the Seismological Society of America, 110(2), 629-646.

  19. Ji, D., Li, C., Zhai, C., Dong, Y., Katsanos, E. I., & Wang, W. (2021). Prediction of Ground‐Motion Parameters for the NGA‐West2 Database Using Refined Second‐Order Deep Neural Networks. Bulletin of the Seismological Society of America.

  20. Hsu, T. Y., Wu, R. T., Liang, C. W., Kuo, C. H., & Lin, C. M. (2020). Peak ground acceleration estimation using P-wave parameters and horizontal-to-vertical spectral ratios. Terr. Atmos. Ocean. Sci, 31, 1-8.

  21. Hsu, T. Y., & Huang, C. W. (2021). Onsite Early Prediction of PGA Using CNN With Multi-Scale and Multi-Domain P-Waves as Input. Frontiers in Earth Science, 9, 247.

  22. Jozinović, D., Lomax, A., Štajduhar, I., & Michelini, A. (2020). Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. Geophysical Journal International, 222(2), 1379-1389.

  23. Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). The transformer earthquake alerting model: a new versatile approach to earthquake early warning. Geophysical Journal International, 225(1), 646-656.

  24. Otake, R., Kurima, J., Goto, H., & Sawada, S. (2020). Deep Learning Model for Spatial Interpolation of Real‐Time Seismic Intensity. Seismological Society of America, 91(6), 3433-3443.

  25. Smith, J. D., Azizzadenesheli, K., & Ross, Z. E. (2020). Eikonet: Solving the eikonal equation with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing.

  26. bin Waheed, U., Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2021). PINNeik: Eikonal solution using physics-informed neural networks. Computers & Geosciences, 104833.

  27. Khoshnevis, N., & Taborda, R. (2019). Application of pool‐based active learning in physics‐based earthquake ground‐motion simulation. Seismological Research Letters, 90(2A), 614-622.

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