DeepGPR 0.0.12

PyTorch and CUDA for GPR FWI

DeepGPR

DeepGPR provides a wave propagation module for PyTorch, designed for applications such as Ground Penetrating Radar (GPR) imaging and inversion. Its core concepts are derived from Deepwave. You can use it to perform both forward modeling and backpropagation—thereby enabling the simulation of wave propagation to generate synthetic data—as well as for Full Waveform Inversion (FWI). Furthermore, you can integrate this wave propagation functionality into a larger operational pipeline—incorporating various wavelets, loss functions, and other components—to achieve end-to-end forward and reverse propagation, powered by automatic differentiation and our high-performance operators.

Features

Supports 2D and 3D forward modeling of Maxwell's equations—via the Finite-Difference Time-Domain (FDTD) method—for both single and multiple excitation scenarios.

Gradients of the output receiver data can be computed with respect to model parameters (relative permittivity, conductivity), the initial wavefield, and source amplitudes.

Utilizes CPML, allowing the width of the PML layer to be configured independently for each boundary.

All operations are executed on the GPU.

Supports techniques such as checkpointing, DDP, and the utilization of CPU memory to minimize GPU memory consumption, thereby enabling the execution of large-scale models.

System Requirements

• OS: Linux and Windows
• Environment: Python 3.8+, CUDA Toolkit
• Libraries: torch (with CUDA support), numpy, scipy, matplotlib
• Hardware: NVIDIA GPU with sufficient VRAM for 3D computational grids.

Start

Before use, you must ensure that you have an NVIDIA graphics card and have installed a CUDA-enabled version of PyTorch.

DeepGPR can then be installed using

  pip install DeepGPR

A Small Forward Modeling Test

import torch
import DeepGPR
import matplotlib.pyplot as plt

# Set up the parameters and models
device=torch.device("cuda")
dx=0.02
dt=3e-11
nt=2000
er = torch.ones(100, 100,1) * 2  
er[50:,:]=5
se = torch.zeros_like(er)  
er.requires_grad_()
source_location=torch.tensor([[[10,10,0]]],device=device,dtype=torch.int)
receiver_location=torch.tensor([[[10,90,0]]],device=device,dtype=torch.int)
freq=2e8
peak_time = 1 / freq
source_amplitudes = torch.zeros((1,nt,1),device=device)
source_amplitudes[0,:,0]=DeepGPR.ricker(freq, nt, dt, peak_time).to(device)


#forward modeling
r = DeepGPR.compute(
    device=device, dx=dx, dt=dt, 
    source_amplitudes=source_amplitudes,
    source_location=source_location, 
    receiver_location=receiver_location, 
    er=er, se=se
)

(r[-1]**2).sum().backward()

_, ax = plt.subplots(1, 2, figsize=(10, 3))
ax[0].plot(r[-1].detach().flatten().cpu().numpy())
ax[0].set_title("Receiver data")
ax[1].imshow(er.grad.detach())
ax[1].set_title("Gradient")
plt.show()

There are more examples in the ./examples.

The following figures present representative 2D and 3D full-waveform inversion (FWI) examples. For each case, the true model, initial model, and inverted result are shown to evaluate the reconstruction performance of the proposed method.

2D FWI Result

The 2D example illustrates the inversion performance on a two-dimensional subsurface model. The comparison between the true model, initial model, and inverted result shows that the proposed method can effectively recover the main structural features from the initial model.

True Model	Initial Model	Inverted Result

3D FWI Result

The 3D example demonstrates the applicability of the proposed method to three-dimensional full-waveform inversion. For visualization, the figures below show the central slice of the 3D model, including the true model, the initial model, and the inverted result. The comparison indicates that the proposed method can reconstruct the dominant subsurface structures in the 3D case and improve the model consistency relative to the initial model.

Model Type	Central Slice of 3D Model
True Model
Initial Model
Inverted Result

compute Interface Documentation

compute is a core function for 3D/2D Finite-Difference Time-Domain (FDTD) forward modeling, primarily designed for Ground Penetrating Radar (GPR) and electromagnetic wave propagation. It fully supports backpropagation (e.g., for Full Waveform Inversion, FWI) utilizing PyTorch's autograd engine.

📝 Function Signature

def compute(device, dx=None, dt=None, 
            source_amplitudes=None,
            source_location=None, 
            receiver_location=None, 
            er=None, se=None, mr=None, 
            E=None, H=None, PML=None,
            pmlthick=10, source_direction=2, reciever_direction=2,
            model_gradient_sampling_interval=1,
            use_async_offload=False):

📥 Input Parameters

1. Basic Physics & Grid Parameters

Parameter	Data Type	Description
device	torch.device / str	PyTorch computation device, e.g., 'cuda:0'. Determines where the computation takes place.
dx	float	Spatial grid step size (assuming an isotropic grid, i.e., $dx = dy = dz$). Typically in meters (m).
dt	float	Time step size. Note: Must strictly satisfy the CFL (Courant-Friedrichs-Lewy) stability condition, or an exception will be raised. Typically in seconds (s).

2. Medium Model Parameters

This section defines the electromagnetic properties of the simulation space. For 2D simulations, set nz=1.

Parameter	Data Type	Shape	Description
er	Tensor (float)	(nx, ny, nz) or (nx, ny)	Relative permittivity ($\epsilon_r$). Values must be $\ge 1$.
se	Tensor (float)	(nx, ny, nz) or (nx, ny)	Electrical conductivity ($\sigma$). Values must be non-negative.
mr	Tensor (float)	(nx, ny, nz) or (nx, ny)	Relative permeability ($\mu_r$). Optional; defaults to 1 for the entire space. Shape must match er.

Dimension Key: nx, ny, and nz represent the number of grid cells along the X, Y, and Z axes, respectively.

3. Source & Receiver Setup

This section defines the geometric observation system (coordinates) and the excitation waveforms.

Parameter	Data Type	Shape	Description
source_amplitudes	Tensor (float)	(num_waveforms, nt)	Source excitation waveforms. nt is the total number of time steps. - If num_waveforms == 1: All sources share this single waveform. - If num_waveforms == nsr: Each source uses its corresponding waveform.
source_location	Tensor (int)	(nstep, nsr, 3)	Grid coordinate indices of the sources. The last dimension corresponds to [x_idx, y_idx, z_idx].
receiver_location	Tensor (int)	(nstep, nrx, 3)	Grid coordinate indices of the receivers. The last dimension corresponds to [x_idx, y_idx, z_idx].
source_direction	int	Scalar	Polarization direction/component of the source excitation. 0 = X, 1 = Y, 2 = Z (e.g., exciting $E_z$).
reciever_direction	int	Scalar	The component direction recorded by the receivers. (Note: typo in variable name is kept as-is to match code). 0 = $E_x$, 1 = $E_y$, 2 = $E_z$.

Core Shape Definitions:

• nstep: Number of shots/batches (independent simulation tasks running in parallel).
• nsr: Number of sources per single simulation.
• nrx: Number of receivers per single simulation.
• nt: Total number of time steps to simulate.

4. Boundary Conditions & Optimization

Parameter	Data Type	Format	Description
pmlthick	int / list / Tensor	Scalar or list of 6	Thickness (in grid layers) of the PML (Perfectly Matched Layer) absorbing boundaries. - Integer p: All six boundaries have thickness p (Z-boundaries are ignored in 2D). - List [x0, xm, y0, ym, z0, zm]: Specific thicknesses for the 6 boundaries.
model_gradient_sampling_interval	int	Scalar	Wavefield sampling interval during forward propagation (Default: 1). A larger integer reduces the VRAM usage for the saved Eall tensor, but may decrease the accuracy of backpropagated gradients.
use_async_offload	bool	Scalar	VRAM optimization flag (Default: False). If True, the internal full wavefield tensor (Eall) is asynchronously offloaded to page-locked host memory (pin_memory CPU RAM). This drastically reduces GPU VRAM consumption at the cost of slightly slower computation times due to PCIe data transfer latency.

5. Field Variable States (Checkpoints / Initial Fields)

For starting a forward simulation from scratch ($t=0$), these three parameters should be passed as None (the system will automatically initialize zero-tensors).

Parameter	Data Type	Shape	Description
E	tuple / None	3 Tensors	Initial state of the electric field components (Ex, Ey, Ez). Each tensor shape is (nstep, nx+1, ny+1, nz+1).
H	tuple / None	3 Tensors	Initial state of the magnetic field components (Hx, Hy, Hz). Shapes identical to E.
PML	tuple / None	24 Tensors	Auxiliary state variables ($\Phi$ fields) for the PML boundary updates.

---

📤 Return Values

The function returns a tuple of 5 elements. These are used to extract synthetic data, initiate the gradient flow for backpropagation, or serve as initial parameters (E, H, PML) for subsequent time-stepped calculations.

return Eall, (Ex, Ey, Ez), (Hx, Hy, Hz), (x0EPhi1...zmHPhi2), receiver_amplitudes

1. Eall: The global electric field history saved for gradient calculation.
   • Shape: (nt_saved, nstep, nx, ny, nz) (where nt_saved depends on nt and model_gradient_sampling_interval).
2. (Ex, Ey, Ez): The 3D electric field state at the final time step.
3. (Hx, Hy, Hz): The 3D magnetic field state at the final time step.
4. (PML_Tuple): A tuple of 24 Tensors recording the final time step state of the PML auxiliary $\Phi$ variables.
5. receiver_amplitudes: The core output. The waveform signals recorded by the receivers over the entire simulation time.
   • Shape: (nstep, nt, nrx)
   • Meaning: [Shot Index, Time Step, Receiver Index]. Note that this output is already sliced to extract only the component specified by reciever_direction.

Cite information

If you find our codes useful, please kindly cite this article. Thanks.

@article{liu2026fast,

title={Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch},

author={Liu, Lei and Song, Chao and He, Liangsheng and Wang, Silin and Feng, Xuan and Liu, Cai}, journal={Computers & Geosciences},

pages={106101},

year={2026},

publisher={Elsevier}

}