> 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/first-motion-polarity-determination.md).

# First Motion Polarity Determination

Polarity of first arrivals in seismic waveform is widely used to determine focal mechanisms. It is also used in diffraction‐stack‐based location methods in microseismic monitoring based on surface-array data to improve location accuracy.

### **Used ML Approaches:**

* Artificial Neural Networks

### **Used Neural Networks:**

* Autoencoder
* CNN
* FC

### Used Learning Procedures:

* Unsupervised Learning
* Supervised Learning

### References:

1. Mousavi, S. M., Zhu, W., Ellsworth, W., & Beroza, G. (2019). Unsupervised clustering of seismic signals using deep convolutional autoencoders. IEEE Geoscience and Remote Sensing Letters, 16(11), 1693-1697.
2. Ross, Z. E., Meier, M. A., & Hauksson, E. (2018). P wave arrival picking and first‐motion polarity determination with deep learning. Journal of Geophysical Research: Solid Earth, 123(6), 5120-5129.
3. Hara, S., Fukahata, Y., & Iio, Y. (2019). P-wave first-motion polarity determination of waveform data in western Japan using deep learning. Earth, Planets and Space, 71(1), 1-11.
4. Tian, X., Zhang, W., Zhang, X., Zhang, J., Zhang, Q., Wang, X., & Guo, Q. (2020). Comparison of single‐trace and multiple‐trace polarity determination for surface microseismic data using deep learning. Seismological Research Letters, 91(3), 1794-1803.