---
license: cc-by-4.0
pipeline_tag: feature-extraction
tags:
  - seismology
  - site-effects
  - generative-model
  - flow-matching
  - Vs30
  - HVSR
---

# JsT V4 — Just seismic Transformer

An 8-token conditional flow-matching generative model for three-component
P-wave seismograms. Turns one earthquake record into a site-effect measurement.

## Model

- **Architecture**: 8-token condition encoder (source×3, path×4, receiver×1) +
  8-layer flow-matching transformer (512 dim, 8 heads), adaLN-Zero modulation,
  1-D rotary position encoding
- **Params**: 5.3M (encoder) + transformer denoiser
- **Training**: 800 epochs, batch 1024, AdamW, cosine annealing
- **Data**: MLAAPDE v2.1 36-month P-wave cache (56,047 source–station pairs)
- **Inference**: 50-step Heun ODE integration, CFG scale 1.0

## Usage

```python
import torch
from JsT import load_checkpoint_models, AblationConditionEncoder

device = torch.device("cuda")
ce, dn, ckpt = load_checkpoint_models(
    "checkpoint-last.pth", device, use_ema=True,
    sampling_method="heun", steps=50, cfg_scale=1.0,
)
ce = AblationConditionEncoder(ce, [8, 9, 10])  # remove identity tokens
dn.eval(); ce.eval()
```

Full code: [Just-seismic-Transformer](https://github.com/grayguy2002/Just-seismic-Transformer)

## Reference

Preprint forthcoming. When using this checkpoint, please cite the corresponding
paper and the JsT code repository.

## License

This checkpoint is released under **CC BY 4.0**.
The accompanying code is released under **MIT**.