TEAM_Taiwan

This is a deep learning model trying to implement EEW systems in Taiwan, data is contributed from TSMIP.

Model architecture include CNN, Transformer Encoder, Mixture Density Model

Reference: Münchmeyer et al.,2021 (https://academic.oup.com/gji/article/225/1/646/6047414)

data preprocess

read_tsmip.py: functions of read file, picking, label

afile.py: classify events and records to csv file

station_location_dataset.py: merge TSMIP station locations

catalog_records_cleaning.py: data cleaning (broken data, double events etc.)

picking_label.py: main files to picking and label(PGA or PGV)

traces_cutting.py: summarize catalog and waveforms to hdf5 file

Training model

CNN_Transformer_Mixtureoutput_TEAM.py: model architecture

multiple_sta_dataset.py: the class of pytorch dataset

multi_station_training.py: main training file

Prediction

predict_new.py confusion_matrix_multi_station.py plot_predict_map.py intensity_map.py

Calculate precision, recall, F1 score and calculate warthquake warning time

Model performance