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README_iter2.md

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+# PhaseNet-TF Alaska - iter2
+
+## Model Description
+
+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. For more details, please refer to the paper and the [PhaseNet-TF](https://github.com/swei-seismo/PhaseNet-TF) repository.
+
+## Model Architecture
+
+- **Backbone**: DeepLabV3Plus with ResNet34 encoder
+- **Input**: 3-component seismic waveforms converted to 6-channel spectrograms (real + imaginary)
+- **Output**: Probability maps for P, S, PS phases and noise
+- **Sampling Rate**: 40 Hz (dt_s = 0.025s)
+- **Window Length**: 4800 points (120 seconds)
+- **Spectrogram Size**: 64 × 4800 (frequency × time)
+- **Input Channels**: 6 (3 real + 3 imaginary spectrogram channels)
+- **Output Classes**: 4 (noise, P, S, PS)
+
+## Load the checkpoint
+checkpoint = torch.load("pytorch_model_iter2.bin", map_location="cpu")
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@article{jie2025background,
+  title={Background Seismicity and Aftershocks of the 2020-2021 Large Earthquakes at the Alaska Peninsula Revealed by a Deep-learning-based Catalog},
+  author={Jie, Yaqi and Wei, Songqiao Shawn and Zhu, Weiqiang and Freymueller, Jeffrey Todd and Elliott, Julie},
+  journal={Authorea Preprints},
+  year={2025},
+  publisher={Authorea}
+}
+```
+
+## License
+
+This model is licensed under the MIT License.