sweep-nn

PyTorch neural reparameterization and priors for seismic FWI. PyTorch-only. No sweep / no jax dependency.

The core idea

In neural FWI you don't update the velocity grid vp directly. You define a small network Net and a latent z, and update those via the loss:

vp = Net(z)                      # implicit / learned reparameterization
pred = forward_solver(vp, ...)
loss = misfit(pred, obs) + lambda * regularizer(Net.parameters())
loss.backward()
optimizer.step()                 # updates Net's weights and / or z

This package provides reusable Net candidates and helpers around them.

What's in it

Hash-encoded SIREN for FWI

VelocityINR is the production-grade reparam choice for FWI:

import torch
from sweep_nn import VelocityINR

init_vp = torch.from_numpy(np.load("init_vp.npy"))   # (nz, nx)
net = VelocityINR(
    init_vp,
    vp_std=50.0,                # typical delta magnitude in m/s
    bounds=(1450.0, 5500.0),    # render-time clamp
    hash_levels=16, hash_finest_resolution=512,
    hidden_features=64, hidden_layers=3,
)
optim = torch.optim.Adam(net.parameters(), lr=1e-4)

for it in range(n_iters):
    vp = net()                  # forward through hash + SIREN
    pred = solver(wavelet, sources, receivers, models=[vp])
    loss = misfit(pred, obs)
    optim.zero_grad(); loss.backward(); optim.step()

# Multi-scale FWI: swap the base when the grid resolution changes —
# the network's learned parameters carry over.
net.update_base_velocity(init_vp_finer)

The hash encoder (Instant-NGP style) gives the SIREN head a structured multi-resolution coordinate basis, so a small MLP can fit high-frequency velocity detail with O(table_size) parameters instead of O(grid_points). A typical config (L=16, F=2, T=2^15) is ~1M parameters total, regardless of the velocity grid size.

Install

pip install sweep-nn

It also comes with pip install sweepx, through sweep-tasks.

Examples

docs/examples/ has two notebooks that run implicit FWI with VelocityINR on the sweep wave solver and reproduce the synthetic examples of Accelerating High Resolution Implicit Full Waveform Inversion (Geophysics): Overthrust with the pseudo-Hessian preconditioner, and Marmousi with hash encoding.

Quick example

import torch
from sweep_nn.siren import SIREN

shape = (200, 400)                # (nz, nx)
net = SIREN(out_shape=shape, vp_min=1500.0, vp_max=4500.0)
optim = torch.optim.Adam(net.parameters(), lr=1e-4)

for it in range(n_iters):
    vp = net()                    # (nz, nx) tensor in m/s
    pred = solver(wavelet, sources, receivers, models=[vp])
    loss = misfit(pred, obs)
    optim.zero_grad(); loss.backward(); optim.step()

Design notes

• Output is always in physical units. Networks internally produce normalized features; the Reparameterizer base scales them to [vp_min, vp_max] before returning. No re-scaling boilerplate on the caller side.
• No global state. Each Reparameterizer instance owns its latent (or none, if it generates from coordinates directly). Serialize with the usual state_dict() / load_state_dict().

License

MIT.