Pinky is a simple deep learning toolbox for earthquake localization and detection, for an example study have a look at our application of a CNN with 3 convolutional layers to a West Bohemia earthquake swarm.
To get started, have a look at our small synthetic example generator in the 'example' subdirectory.
- pyrocko
- tensorflow v1.14
- scikit-optimize (optional for hyperparameter optimization)
You can use pip to install dependencies:
pip install -r requirements.txt
Note that this will install tensorflow without GPU support. Checkout the tensorflow documentation to install with GPU support
You need to provide pinky with a configuration file.
pinky --config <config_filename>
A good starting point to see if data is properly loaded an preprocessed is to have look at a couple of examples.
pinky --config <config_filename> --show-data 9
This will generate a figure with 9 panels show the first 9 preprocessed data labels and features from your dataset.
To start training:
pinky --config <config_filename> --train
You can dump your examples to TFRecordDatasets to accelerate io operations:
pinky --config <config_filename> --write <new_config_filename>
and use the newly created config file to run --train
Invoke pinky with --debug to enable keep track of weight matrices in
tensorboard.
Marius Kriegerowski, Gesa M. Petersen, Hannes Vasyura‐Bathke, Matthias Ohrnberger; A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms. Seismological Research Letters ; 90 (2A): 510–516. doi: https://doi.org/10.1785/0220180320