SeisNN 0.0.2.dev1

Deep learning seismic phase picking package

# SeisNN

Seismic event P phase picking project

Main framework: Obspy, Seisan, Tensorflow with Keras, Pytorch

Using U-net to generate pick probability


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Early access

The code is still in the development state, API will change frequently.

Please star us for upcoming updates!

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Prerequisite:

• S-File catalog from [SEISAN](http://seisan.info/)

• SeisComP Data Structure (SDS) database. The directory and file layout of SDS is defined as:

  SDSROOT/YEAR/NET/STA/CHAN.TYPE/NET.STA.LOC.CHAN.TYPE.YEAR.DAY

Installation:

• pip install seisnn

• Clone this repository

• Follow the instructions in the [Docker](docker) folder to create a Docker container.

• SSH into the Docker container you create.

In the [scripts](scripts) folder:

• [generate_dataset.py](scripts/generate_dataset.py) - Make S-Files into training .pkl datasets.

• [evalution.py](seisnn/qc.py) - Calculate precision, recall and F1 score. - Plot error distribution.

• [plot_instance.py](scripts/plot_instance.py) - Plot the wavefile, picks and the probability form the .pkl dataset.

• [scanning.py](scripts/scanning.py) - Scan through all stations available in the given time window, transfer into .pkl dataset.

Training:

You can choose between [scripts/tensorflow](scripts/tensorflow) or [scripts/pytorch](scripts/pytorch)

• [pre_training.py](scripts/tensorflow/pre_training.py) - Pre-train the model using small dataset.

• [training.py](scripts/tensorflow/training.py) - Train the model with the pre-trained weight.

• [prediction.py](scripts/tensorflow/prediction.py) - Predict the probability of the picks and write into the dataset.

Model:

The model is stored in [seisnn/tensorflow](seisnn/tensorflow) or [seisnn/pytorch](seisnn/pytorch)

• Nest_Net - Unet++ code modified from [MrGiovanni](https://github.com/MrGiovanni/Nested-UNet/blob/master/model_logic.py)

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Reference:

[PhaseNet](https://arxiv.org/abs/1803.03211)

Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211.

[U-net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/)

Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

[U-net ++](https://doi.org/10.1007/978-3-030-00889-5_1)

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3-11). Springer, Cham.

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