> 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/phase-association.md).

# Phase Association

Links individual arrival times picked at different stations to a common origin. It is a critical step in earthquake monitoring.

### **Used ML Approaches:**

* Artificial Neural Networks

### **Used Neural Networks:**

* GRU
* TCN

### Used Learning Procedures:

* Unsupervised Learning
* Supervised Learning

### References:

1. McBrearty, I. W., Gomberg, J., Delorey, A. A., & Johnson, P. A. (2019). Earthquake arrival association with backprojection and graph theory. Bulletin of the Seismological Society of America, 109(6), 2510-2531.
2. McBrearty, I. W., Delorey, A. A., & Johnson, P. A. (2019). Pairwise association of seismic arrivals with convolutional neural networks. Seismological Research Letters, 90(2A), 503-509.
3. Ross, Z. E., Yue, Y., Meier, M. A., Hauksson, E., & Heaton, T. H. (2019). PhaseLink: A deep learning approach to seismic phase association. Journal of Geophysical Research: Solid Earth, 124(1), 856-869.
4. Dickey, J., Borghetti, B., Junek, W., & Martin, R. (2020). Beyond correlation: A path‐invariant measure for seismogram similarity. Seismological Research Letters, 91(1), 356-369.