2D Elastic FWI on Overthrust with Torch

Source file:

• examples/FWI/2d/elastic/torch/fwi_overthrust.py

What This Example Does

This example runs 2D elastic full-waveform inversion on an Overthrust slice with one script that supports two propagator backends:

• eager: pure PyTorch propagation through PropTorch(..., backend="eager")
• cuda: compiled CUDA propagation through PropTorch(..., backend="cuda")

The script:

1. loads the 2D Overthrust true and smooth vp models
2. derives vs from vp / 1.732 and uses constant rho
3. generates observed elastic shot gathers from the true model
4. inverts vp and vs from the smooth starting model

Main Components

The solver is built from:

• equation: Elastic(...)
• propagator: PropTorch(...)
• wave: a Ricker wavelet injected into sxx and szz
• sources: regularly sampled surface sources
• receivers: regularly sampled surface receivers
• models: vp, vs, and rho

Prepare the Overthrust Model Files

This 2D example reads an extracted Overthrust slice:

• examples/models/overthrust/true_2d.npy
• examples/models/overthrust/smooth_2d.npy

Generate the 3D volume, smooth it, and extract the 2D slice before running the example:

python3 examples/models/overthrust/download_3d_overthrust.py --extract
python3 examples/models/overthrust/convert_3d_overthrust_vites_to_npy.py
python3 examples/models/overthrust/make_smooth_model.py \
  --input examples/models/overthrust/true_3d.npy \
  --output examples/models/overthrust/smooth_3d.npy \
  --radii 6,6,6 \
  --passes 3
python3 examples/models/overthrust/extract_2d_slice.py \
  --input examples/models/overthrust/true_3d.npy \
  --output examples/models/overthrust/true_2d.npy \
  --axis y
python3 examples/models/overthrust/extract_2d_slice.py \
  --input examples/models/overthrust/smooth_3d.npy \
  --output examples/models/overthrust/smooth_2d.npy \
  --axis y

Backend Selection

Run the example with:

PyTorchCUDA

python3 examples/FWI/2d/elastic/torch/fwi_overthrust.py --backend eager

python3 examples/FWI/2d/elastic/torch/fwi_overthrust.py --backend cuda

Key Configuration

Shared configuration comes from examples/_shared/configure_overthrust.py and includes:

• nt=1500, dt=0.002
• dh=25.0
• spatial_order=4
• abcn=10
• src_step=4, rec_step=2
• source_encoding=True
• batchsize=8

The script uses:

• source_type=["sxx", "szz"]
• receiver_type=["vx", "vz"]
• separate learning rates for vp, vs, and rho

Geometry

The final array shapes are:

• sources: (nshots, 2)
• receivers: (nshots, nreceivers, 2)

Outputs

The script saves:

• ricker.png
• observed_data.png
• loss.png
• epoch_XXXX.png

Each backend writes into its own output directory:

PyTorchCUDA

elastic_fwi_overthrust_torch

elastic_fwi_overthrust_cuda

Example Figures

The following figures come from a completed CUDA run of the 2D elastic Overthrust example.

observed_data.png: observed vx and vz shot gathers generated from the true model.

loss.png: the inversion loss curve across optimization steps.

epoch_0100.png: the saved progress panel at the final shown epoch, including the true models, current inverted models, and current gradients.

Notes

• the Overthrust elastic example now defaults to source encoding
• cuda mode requires a CUDA-capable PyTorch environment and compiled bindings