CATS is a signal processing technique and framework for detecting and denoising sparse signals in the time-frequency domain. Particularly, very useful for processing earthquakes. This work is still in progress, and the package is under active development. Soon, here will be links to our papers/preprints.
- Versatile. Any sparse signals in the time-frequency domain can be localized by CATS.
- Flexible. Fast detection with STFT or more accurate denoising with CWT.
- Fast and accurate. Here will be links to our papers showing this.
- Comprehensive quality control.
- Autotunable parameters with direct physical interpretation.
- Easy visualization of all intermediate workflow steps.
- Collected cluster statistics allow for fine-grained QC and classification of signals.
To install the package:
- Short way:
pip install git+https://github.com/sgrubas/cats.git - Other way:
- Clone repository:
git clone https://github.com/sgrubas/cats.git - Open the
catsdirectory:cd cats - Install: 1)
pip install .or 2)pip install -e .(editable mode)
- Clone repository:
- To update:
pip install -U git+https://github.com/sgrubas/cats.git
The package was tested on Python 3.9. See other dependencies in requirements.txt.
- Detection of seismic events
- Autotuning CATS detector with Optuna
- Denoising seismic events
- Autotuning CATS denoising with Optuna
If you find CATS useful for your research, please cite this repository (soon there will be links to our papers):
@article{grubas2023cats,
title = {Cluster Analysis of Trimmed Spectrograms (CATS)},
author = {Serafim Grubas and Mirko van der Baan},
journal = {GitHub},
url = {https://github.com/sgrubas/cats},
year = {2024},
doi = {10.5281/zenodo.13830301},
}
- Serafim Grubas (serafimgrubas@gmail.com, grubas@ualberta.ca)
- Mirko van der Baan