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Finite difference 2D/3D acoustic wave propagator.

Project description

PyPI version

Simwave

Simwave is a Python package to simulate the propagation of the constant or variable density acoustic wave in an isotropic 2D/3D medium using the finite difference method. Finite difference kernels of aribtrary spatial order (up to 16th order) are written in C for performance and compiled at run time. These kernels are called via a user-friendly Python interface for easy integration with several scientific and engineering libraries to, for example perform full-waveform inversion.

Installation:

For installation, simwave needs only scipy, numpy, and segyio. See requirements.txt. If you wish to plot, then matplotlib is additionally required. simwave compiles finite difference stencils at run time in C for performance and thus requires a working C compiler.

git clone https://github.com/HPCSys-Lab/simwave.git

cd simwave

pip3 install -e .

Contributing

All contributions are welcome.

To contribute to the software:

  1. Fork the repository.
  2. Clone the forked repository, add your contributions and push the changes to your fork.
  3. Create a Pull request

Before creating the pull request, make sure that the tests pass by running

pytest

Some things that will increase the chance that your pull request is accepted:

  • Write tests.
  • Add Python docstrings that follow the Sphinx.
  • Write good commit and pull request messages.

Problems?

If something isn't working as it should or you'd like to recommend a new addition/feature to the software, please let us know by starting an issue through the issues tab. I'll try to get to it as soon as possible.

Examples

Simulation with simwave is simple and can be accomplished in a dozen or so lines of Python! Jupyter notebooks with tutorials can be found here here.

Here we show how to simulate the constant density acoustic wave equation on a simple two layer velocity model.

from simwave import (
    SpaceModel, TimeModel, RickerWavelet, Solver, Compiler,
    Receiver, Source, plot_wavefield, plot_shotrecord, plot_velocity_model
)
import numpy as np


# set compiler options
# available language options: c (sequential) or  cpu_openmp (parallel CPU)
compiler = Compiler(
    cc='gcc',
    language='cpu_openmp',
    cflags='-O3 -fPIC -Wall -std=c99 -shared'
)

# Velocity model
vel = np.zeros(shape=(512, 512), dtype=np.float32)
vel[:] = 1500.0
vel[100:] = 2000.0

# create the space model
space_model = SpaceModel(
    bounding_box=(0, 5120, 0, 5120),
    grid_spacing=(10, 10),
    velocity_model=vel,
    space_order=4,
    dtype=np.float32
)

# config boundary conditions
# (none,  null_dirichlet or null_neumann)
space_model.config_boundary(
    damping_length=0,
    boundary_condition=(
        "null_neumann", "null_dirichlet",
        "none", "null_dirichlet"
    ),
    damping_polynomial_degree=3,
    damping_alpha=0.001
)

# create the time model
time_model = TimeModel(
    space_model=space_model,
    tf=1.0
)

# create the set of sources
source = Source(
    space_model,
    coordinates=[(2560, 2560)],
    window_radius=1
)

# crete the set of receivers
receiver = Receiver(
    space_model=space_model,
    coordinates=[(2560, i) for i in range(0, 5112, 10)],
    window_radius=1
)

# create a ricker wavelet with 10hz of peak frequency
ricker = RickerWavelet(10.0, time_model)

# create the solver
solver = Solver(
    space_model=space_model,
    time_model=time_model,
    sources=source,
    receivers=receiver,
    wavelet=ricker,
    saving_stride=0,
    compiler=compiler
)

# run the forward
u_full, recv = solver.forward()

print("u_full shape:", u_full.shape)
plot_velocity_model(space_model.velocity_model)
plot_wavefield(u_full[-1])
plot_shotrecord(recv)

Performance

  • TO DO

Project details


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