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--- |
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language: en |
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tags: |
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- seismic |
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- earthquake |
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- phase-picking |
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- deep-learning |
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- pytorch |
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license: mit |
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datasets: |
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- PS_Alaska |
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metrics: |
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- f1-score |
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- precision |
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- recall |
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--- |
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# PhaseNet-TF Alaska: Advanced Seismic Arrival Time Detection |
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## Model Description |
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PhaseNet-TF is an advanced deep learning model for automatic seismic phase picking (P-wave, S-wave, and PS-wave detection) using spectrogram-based image segmentation approaches. The model leverages DeepLabV3Plus architecture to detect seismic arrivals with high accuracy, especially for weak and noisy signals from ocean-bottom seismometers and weak phases such as slab interface refracted PS and SP waves. This Alaska version is specifically trained on the PS_Alaska dataset for P and S phases. |
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## Available Versions |
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This repository contains two versions of the PhaseNet-TF Alaska model: |
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### 🔄 Iteration 1 |
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- **Model File**: `alaska_iter1.bin` |
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- **Config**: `config_iter1.json` |
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### 🔄 Iteration 2 |
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- **Model File**: `alaska_iter2.bin` |
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- **Config**: `config_iter2.json` |
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## Model Architecture |
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- **Backbone**: DeepLabV3Plus with ResNet34 encoder |
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- **Input**: 3-component seismic waveforms converted to 6-channel spectrograms (real + imaginary) |
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- **Output**: Probability maps for P, S, PS phases and noise |
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- **Sampling Rate**: 40 Hz (dt_s = 0.025s) |
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- **Window Length**: 4800 points (120 seconds) |
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- **Spectrogram Size**: 64 × 4800 (frequency × time) |
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- **Input Channels**: 6 (3 real + 3 imaginary spectrogram channels) |
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- **Output Classes**: 4 (noise, P, S, PS) |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@article{jie2025background, |
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title={Background Seismicity and Aftershocks of the 2020-2021 Large Earthquakes at the Alaska Peninsula Revealed by a Deep-learning-based Catalog}, |
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author={Jie, Yaqi and Wei, Songqiao Shawn and Zhu, Weiqiang and Freymueller, Jeffrey Todd and Elliott, Julie}, |
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journal={Authorea Preprints}, |
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year={2025}, |
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publisher={Authorea} |
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} |
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``` |
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## License |
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This model is licensed under the MIT License. |
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