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 20th 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.
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
pip3 install -e .
All contributions are welcome.
To contribute to the software:
- Fork the repository.
- Clone the forked repository, add your contributions and push the changes to your fork.
- Create a Pull request
Before creating the pull request, make sure that the tests pass by running
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.
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.
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, saving_stride=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, 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)