> For the complete documentation index, see [llms.txt](https://smousavi05.gitbook.io/mlseismology/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://smousavi05.gitbook.io/mlseismology/seismological-tasks/location-estimation.md).

# Earthquake Location Estimation

Determining the location of the focal point of earthquake at subsurface. 

### **Used ML Approaches:**

* Graph Clustering
* Hierarchical Clustering
* Artificial Neural Networks

### **Used Neural Networks:**

* FC
* CNN
* TCN
* Transformer
* PINN

### Used Learning Procedures:

* Unsupervised Learning
* Supervised Learning 
* Transfer Learning
* Reinforcement Learning

### References:

1. Riahi, N., & Gerstoft, P. (2017). Using graph clustering to locate sources within a dense sensor array. Signal processing, 132, 110-120.
2. Trugman, D. T., & Shearer, P. M. (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88(2A), 379-391.
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. 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.
5. 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.
6. 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.
7. Mousavi, S. M., & Beroza, G. C. (2019). Bayesian-deep-learning estimation of earthquake location from single-station observations. arXiv preprint arXiv:1912.01144.
8. 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.
9. Kriegerowski, M., Petersen, G. M., Vasyura‐Bathke, H., & Ohrnberger, M. (2019). A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms. Seismological Research Letters, 90(2A), 510-516.
10. Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method
11. van den Ende, M., & Ampuero, J. P. (2020). Automated seismic source characterisation using deep graph neural networks.
12. Shen, H., & Shen, Y. (2021). Array‐Based Convolutional Neural Networks for Automatic Detection and 4D Localization of Earthquakes in Hawai ‘i. Seismological Research Letters.
13. 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.
14. 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.
15. Smith, J. D., Ross, Z. E., Azizzadenesheli, K., & Muir, J. B. (2021). HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks. arXiv.
16. Wu, Y., Wei, J., Pan, J., & Chen, P. (2019). Research on Microseismic Source Locations Based on Deep Reinforcement Learning. IEEE Access, 7, 39962-39973.