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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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+
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+ # PhaseNet-TF Alaska: Advanced Seismic Arrival Time Detection
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+
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+ ## Model Description
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+
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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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+
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+ ## Available Versions
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+
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+ This repository contains two versions of the PhaseNet-TF Alaska model:
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+
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+ ### 🔄 Iteration 1
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+ - **Model File**: `pytorch_model_iter1.bin`
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+ - **Config**: `config_iter1.json`
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+ - **Documentation**: [README_iter1.md](README_iter1.md)
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+
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+ ### 🔄 Iteration 2
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+ - **Model File**: `pytorch_model_iter2.bin`
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+ - **Config**: `config_iter2.json`
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+ - **Documentation**: [README_iter2.md](README_iter2.md)
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+
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+ ## Quick Start
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+
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+ ### Load Iteration 1
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+ ```python
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+ import torch
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+ checkpoint = torch.load("pytorch_model_iter1.bin", map_location="cpu")
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+ ```
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+
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+ ### Load Iteration 2
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+ ```python
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+ import torch
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+ checkpoint = torch.load("pytorch_model_iter2.bin", map_location="cpu")
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+ ```
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+
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+ ## Model Architecture
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+
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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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+
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+ ## Citation
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+
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+ If you use this model in your research, please cite:
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+
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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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+
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+ ## License
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+
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+ This model is licensed under the MIT License.