Metadata-Version: 2.1
Name: ELEP
Version: 0.0.2
Summary: Ensemble Learning for Earthquake Processing
Author-email: Congcong Yuan <cyuan@g.harvard.edu>
Maintainer-email: Congcong Yuan <cyuan@g.harvard.edu>, Yiyu Ni <niyiyu@uw.edu>
License: GPLv3
Project-URL: GitHub, https://github.com/congcy/ELEP
Project-URL: Documentation, https://ssec-python-project-template.readthedocs.io/en/latest
Project-URL: Issues, https://github.com/othneildrew/Best-README-Template/issues
Keywords: seismology,machine learning,ensemble learning,seismic signal processing
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: development
Provides-Extra: tests
License-File: LICENSE

# ELEP

<!-- PROJECT LOGO & STATUS -->
<br />
<div align="center">
  <a href="https://github.com/congcy/ELEP">
    <img src="docs/images/ELEP_logo.png" alt="Logo" width="100" height="80">
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  <h3 align="center">ELEP</h3>
  <p align="center">
    A ensemble-learning based toolkit for seismologists to make the best pick on earthquake phases by combining multiple predictions into the one. 
    <br />
    <br />
    <a href="https://github.com/congcy/ELEP/blob/main/LICENSE" alt="Liscence">
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## Prerequisites & installations

* create a new environment using [Anaconda](https://www.anaconda.com/) 
  ```python
  >>> conda create --name myenv
  ```
* install [Seisbench](https://github.com/seisbench/seisbench)
  ```python
  >>> pip install seisbench
  ```
* install other packages
  ```python
  >>> pip install jupyter mpi4py
  ```
* install our toolkit (under development)
  ```python
  >>> pip install ELEP
  ```

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## Features

![workflow](/docs/images/ELEP_framework.png)

This toolkit contains the following features:

1. It provides broadband and multiband prediction workflows.
2. It provides three ensemble estimation or combination approaches.
3. It provides GPU-supported batch predictions on avilable datasets.
4. It supports parallel predictions for real-time earthquake monitoring.
5. It possess a good generalization capability.

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## Demo
Please see the notebooks under the folder `tutorials` for more details.
<p align="right">(<a href="https://github.com/congcy/ELEP">back to top</a>)</p>

## Citation 
Yuan, C., Ni, Y., Lin, Y., Denolle, M., Ensemble Learning for Earthquake Detection and Phase Picking, 2023, in preparation.
<p align="right">(<a href="https://github.com/congcy/ELEP">back to top</a>)</p>

## Contact
Congcong Yuan - cyuan@g.harvard.edu
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