README.md

metadata

title: Vs From Dispersion Curve
emoji: 🌍
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 5.24.0
app_file: app.py
pinned: false
license: mit
short_description: 1-D shear-wave velocity from a Rayleigh phase-velocity curve

Shear-wave velocity from a phase-velocity dispersion curve

Inverts a fundamental-mode Rayleigh phase-velocity dispersion curve (the SASW/MASW observable) for a 70-layer 1-D Vs profile in a single forward pass — no initial model, no iterative inversion.

The model is a physically-coded transformer encoder–decoder (2.44 M parameters): each dispersion pick is a token carrying its physical period, and each output depth is a query token carrying its physical depth, reading the period tokens by masked cross-attention. Band-limited and gappy curves are handled natively, with no interpolation.

Trained on OpenSWI-shallow (22 M curve/profile pairs). Test accuracy: 0.151 km/s full-profile RMSE / 6.0 % MAPE / R² 0.949 on 50 k held-out samples; 38 m/s MAE versus the tomographic reference on 5,297 real Long Beach curves.

Usage

• Pick a built-in example, upload a CSV (period or frequency + phase velocity), or paste values in the sidebar.
• Site scale factor k: the elastodynamic problem is scale-invariant, so a high-frequency engineering curve (e.g. MASW at a 30 m site) is inverted by multiplying its periods by k into the model's 0.2–10 s training band and dividing the output depths by k. Velocities are never rescaled — the ≈ 0.3–4.5 km/s support applies at every scale.
• Gray bands in the profile plot mark depths outside the range the input band physically constrains (wavelength heuristic ≈ ⅓·λ_min to ½·λ_max); treat the profile there as extrapolation.
• The predicted profile can be downloaded as CSV.

Scope

Trained on 0.2–10 s periods, 0–2.76 km depth, Vs ≈ 0.3–4.5 km/s. Curves outside this envelope — e.g. soft-soil sites with Vs < 0.3 km/s — are out of distribution.

Files

• app.py — Gradio interface (Gradio SDK; also compatible with ZeroGPU hardware, though the model runs on CPU — it needs no GPU)
• app_streamlit.py — equivalent Streamlit interface, kept for local use (streamlit run app_streamlit.py)
• model.py — standalone model definition (depth-query encoder–decoder)
• checkpoints/best_model.pth — trained weights (v4 finalist)
• assets/ — period/depth grids and example curves from the test split