metadata
license: mit
tags:
- seismology
- earthquake
- waveform-prediction
- transformer
- pytorch
- pytorch-lightning
library_name: pytorch
pipeline_tag: other
SeismoGPT (Phase 1)
Pre-trained Phase 1 weights for SeismoGPT: a causal RoPE transformer over seismic waveform tokens (Z, N, E), trained with log-cosh loss and multi-horizon auxiliary heads.
This checkpoint is the deterministic model used for paper rollouts and figures in the companion code repository.
Paper: Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures — arXiv:2606.02912v1
Code: github.com/wesmail/SeismoGPT
Files
| File | Description |
|---|---|
epoch=12-step=633750.ckpt |
PyTorch Lightning checkpoint (Phase 1, epoch 12) |
train_phase1_logcosh.yaml |
Training configuration used for Phase 1 |
Requirements
- Python 3.10+
torch,lightning,seisbench, and the SeismoGPT code repository
Download weights
pip install huggingface_hub
huggingface-cli download wesmail/SeismoGPT \
epoch=12-step=633750.ckpt train_phase1_logcosh.yaml \
--local-dir phase1
Or from Python:
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(
repo_id="wesmail/SeismoGPT",
filename="epoch=12-step=633750.ckpt",
local_dir="phase1",
)
Use the same kernel_size (16) and num_tokens (320) as in training; these are stored in the checkpoint hyperparameters. For evaluation (configurations A/B/C), rollouts, and plotting, see the code repository.
Model summary
| Setting | Value |
|---|---|
| Phase | 1 (deterministic, log_cosh) |
| Channels | Z, N, E |
| Token size K | 16 samples |
| Tokens per window | 320 |
d_model |
512 |
| Encoder layers | 8 |
| Prediction horizons (train) | 4 (inference uses horizon 1) |