unet_cea

Using Unet to train model based on wenchuan aftershocks and automatically detect p,s phases

This is the unet code used in the paper: 赵明，陈石，房立华，David A Yuen. 2019. 基于U形全卷积神经网络的震相识别与到时拾取方法研究. 地球物理学报，待刊.

And,it is developed from ConvNetQuake : Perol., T, M. Gharbi and M. Denolle. Convolutional Neural Network for Earthquake detection and location. preprint arXiv:1702.02073, 2017.

The u-net structure

The example of labeled sample:

Some detect results

Some train snapshots

Installation

Install dependencies: conda env create -f unet_cea python27.yaml

Train(just a small data to show the code really work ^_^)

python ./bin/unet_train.py --tfrecords_dir data/train/ --checkpoint_dir model

validate

python ./bin/unet_eval.py --tfrecords_dir data/test --checkpoint_path output/unet_capital/ --batch_size 1000 --output_dir output/unet --events

Tensorboard for real-time monitor

tensorboard --logdir model

Trained model

An trained model on Chinese Metropolitian Network(178 stations,266350 samples),thanks to Hebei Earthquake Administration for providing the catalogs and high accuracy manual picks

The directory unet_capital

Use new test data(not used in the train and validate process) to check the preformance of the model:

python ./bin/unet_eval_from_tfrecords.py --tfrecords_dir detection --checkpoint_path ./unet_capital/unet.ckpt-585000 --batch_size 8 --output_dir output

(more to come)