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This documentation is not for the latest stable Salvus version.

This tutorial is presented as Python code running inside a Jupyter Notebook, the recommended way to use Salvus. To run it yourself you can copy/type each individual cell or directly download the full notebook, including all required files.

Model Parameterizations in Salvus

In this notebook we will explore all the different options to parameterize the material properties, which are currently supported in Salvus. As always, the first step is to import the requisite Python modules.
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%matplotlib inline
import os
from functools import partial
from pathlib import Path

import h5py
import matplotlib.pyplot as plt
import numpy as np

from salvus.mesh import mesh_block
import salvus.mesh.unstructured_mesh as um
import salvus.toolbox.toolbox as st
from salvus.flow import api
from salvus.flow import simple_config as sc

SALVUS_FLOW_SITE_NAME = os.environ.get("SITE_NAME", "local")
First we set up a simple function to return simple meshes for testing in both
2- and 3-D dimensions. The size of the mesh is small as these examples will
only run for a trivial duration, and are just meant to be used to explore the
possible parameterizations.
def get_basic_mesh(dim: int, epd: int = 20) -> um.UnstructuredMesh:
    """Get a simple mesh to outline allowed parameter types.

    Parameters
    ----------
    dim : int
        Dimension of the mesh.
    epd : int, optional
        Elements per dimension, by default 10

    Returns
    -------
    um.UnstructuredMesh
        An unstructured mesh free of any parameters.

    """
    x = 2.0
    if dim == 2:

        mesh = mesh_block.generators.cartesian.rectangle_2d(
            nelem_x=epd, nelem_y=epd, max_x=x, max_y=x
        ).get_unstructured_mesh()

    elif dim == 3:

        mesh = mesh_block.generators.cartesian.cube_3d(
            nelem_x=epd, nelem_y=epd, nelem_z=epd, max_x=x, max_y=x, max_z=x
        ).get_unstructured_mesh()

    mesh.find_side_sets()
    mesh.change_tensor_order(4)
    return mesh
Second, we will define some basic parameter values to use (all SI units).
vs = 500.0
vp = 1000.0
rho = 1000.0
Now we will define a set of sources which can be used in all of our testing
environments. We'll use a Ricker wavelet in all cases, but we'll change the
spatial type of our source depending on the dimension of the problem and the
physics.
stf = sc.stf.Ricker(center_frequency=2e3)

src_scalar_2d = sc.source.cartesian.ScalarPoint2D(
    f=1, x=1, y=1, source_time_function=stf
)
src_scalar_3d = sc.source.cartesian.ScalarPoint3D(
    f=1, x=1, y=1, z=1, source_time_function=stf
)
src_vector_2d = sc.source.cartesian.VectorPoint2D(
    fx=1, fy=1, x=1, y=1, source_time_function=stf
)
src_vector_3d = sc.source.cartesian.VectorPoint3D(
    fx=1, fy=1, fz=1, x=1, y=1, z=1, source_time_function=stf
)
An finally we'll partially fill in Salvus Flow's api.run function. We'll
be running the function many times with mostly the same setup, so this is a nice
space saving method.
run_salvus = partial(
    api.run, ranks=2, get_all=True, site_name=SALVUS_FLOW_SITE_NAME
)
Now, onto the examples themselves.


2D domains


We'll start with all of the 2-D parameterizations. Again, to save space,
we'll prepare our simulation.Waveform object with values which will
be re-used.
w = sc.simulation.Waveform()

w.domain.dimension = 2
w.output.volume_data.format = "hdf5"
w.output.volume_data.filename = "output.h5"
w.output.volume_data.sampling_interval_in_time_steps = 100
Acoustic


Acoustic meshes can be parameterized either using p-velocity and density, or
a linear parameterization using M0 and M1. Acoustic elements must have the
fluid flag set to 1.


Velocity (sound speed) and density
# Generate the mesh.
m = get_basic_mesh(2)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("VP", par_template * vp)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.ones(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["phi"]
w.physics.wave_equation.point_source = [src_scalar_2d]

# Run the solver.
output_folder = Path("acoustic_rhovp")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder)

# Visualize the results.
f, ax = plt.subplots(1, 1)
ax.set_aspect("equal")
t, da0 = st.visualize_wavefield_2d(output_file, "phi")
ax.tricontourf(t, da0[-1, :])
Job `job_2204302001384311_8b9c92bee4` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32

* Downloaded 528.9 KB of results to `acoustic_rhovp`.
* Total run time: 2.14 seconds.
* Pure simulation time: 1.02 seconds.

<matplotlib.tri.tricontour.TriContourSet at 0x7f678ccccad0>
# Re-parameterize as m0 and m1.
m0, m1 = (1 / rho) / vp**2, 1 / rho

# Generate the mesh.
m = get_basic_mesh(2)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("M0", par_template * m0)
m.attach_field("M1", par_template * m1)
m.attach_field("fluid", np.ones(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["phi"]
w.physics.wave_equation.point_source = [src_scalar_2d]

# Run the solver.
output_folder = Path("acoustic_linear")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder)

# Visualize the results.
f, ax = plt.subplots(1, 1)
ax.set_aspect("equal")
t, da1 = st.visualize_wavefield_2d(output_file, "phi")
ax.tricontourf(t, da1[-1, :])

# All parameterizations should have produced the same output.
np.testing.assert_allclose(da0, da1, atol=1e-3)
Job `job_2204302001649015_068cad9018` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32

* Downloaded 528.8 KB of results to `acoustic_linear`.
* Total run time: 1.69 seconds.
* Pure simulation time: 1.35 seconds.



Isotropic elastic models can be parameterized using either:


    

  • 

    velocities {VP, VS, RHO}
    

    • 

  • 

    Lame's first parameter, shear modulus, and density {LAMBDA, MU, RHO}
    

    • 

  • 

    Bulk modulus, shear modulus, and density {KAPPA, MU, RHO}
    

    • 



Velocities and density
# Generate the mesh.
m = get_basic_mesh(2)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("VP", par_template * vp)
m.attach_field("VS", par_template * vs)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.zeros(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["displacement"]
w.physics.wave_equation.point_source = [src_vector_2d]

# Run the solver.
output_folder = Path("elastic_vpvsrho")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder)

# Visualize the results.
f, ax = plt.subplots(1, 1)
ax.set_aspect("equal")
t, de0 = st.visualize_wavefield_2d(output_file, "displacement")
ax.tricontourf(t, de0[-1, :])
Job `job_2204302001592267_c3d55c3735` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32

* Downloaded 679.9 KB of results to `elastic_vpvsrho`.
* Total run time: 1.34 seconds.
* Pure simulation time: 1.03 seconds.

<matplotlib.tri.tricontour.TriContourSet at 0x7f678ae12ed0>
# Generate the mesh.
m = get_basic_mesh(2)

# Attach parameter to the nodes of each element.
mu = rho * vs**2
lam = rho * vp**2 - 2 * mu
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("LAMBDA", par_template * lam)
m.attach_field("MU", par_template * mu)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.zeros(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["displacement"]
w.physics.wave_equation.point_source = [src_vector_2d]

# Run the solver.
output_folder = Path("elastic_lambdamurho")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder)

# Visualize the results.
f, ax = plt.subplots(1, 1)
ax.set_aspect("equal")
t, de1 = st.visualize_wavefield_2d(output_file, "displacement")
ax.tricontourf(t, de1[-1, :])
Job `job_2204302001186179_6080328977` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32

* Downloaded 679.9 KB of results to `elastic_lambdamurho`.
* Total run time: 1.64 seconds.
* Pure simulation time: 1.27 seconds.

<matplotlib.tri.tricontour.TriContourSet at 0x7f678ccd9310>
# Generate the mesh.
m = get_basic_mesh(2)

# Attach parameter to the nodes of each element.
mu = rho * vs**2
kap = rho * (vp**2 - 4 / 3 * vs**2)
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("KAPPA", par_template * kap)
m.attach_field("MU", par_template * mu)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.zeros(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["displacement"]
w.physics.wave_equation.point_source = [src_vector_2d]

# Run the solver.
output_folder = Path("elastic_kappamurho")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder)

# Visualize the results.
f, ax = plt.subplots(1, 1)
ax.set_aspect("equal")
t, de2 = st.visualize_wavefield_2d(output_file, "displacement")
ax.tricontourf(t, de2[-1, :])

# All parameterizations should have produced the same output.
np.testing.assert_allclose(de0, de1, atol=1e-7)
np.testing.assert_allclose(de1, de2, atol=1e-7)
Job `job_2204302001189713_e0f15969ad` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32

* Downloaded 679.9 KB of results to `elastic_kappamurho`.
* Total run time: 2.15 seconds.
* Pure simulation time: 1.71 seconds.



We'll generate a new partially-filled waveform simulation object for 3-D.
w = sc.simulation.Waveform()

w.domain.dimension = 3
w.output.volume_data.format = "hdf5"
w.output.volume_data.filename = "output.h5"
w.output.volume_data.sampling_interval_in_time_steps = 100


Acoustic meshes can be parameterized either using p-velocity and density, or
a linear parameterization using M0 and M1. Acoustic elements must have the
fluid flag set to 1.


Velocity (sounds speed) and density
# Generate the mesh.
m = get_basic_mesh(3)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("VP", par_template * vp)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.ones(m.nelem))

# Attach the mesh.
w.set_mesh(m)
w.output.volume_data.fields = ["phi"]
w.physics.wave_equation.point_source = [src_scalar_3d]

# Run the solver.
output_folder = Path("acoustic_rhovp")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder, overwrite=True)

# Read the results.
with h5py.File(output_file, "r") as fh:
    da0 = fh["/volume/phi"][:]
Job `job_2204302001087285_890c75db9f` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32
                                                     

* Downloaded 66.0 MB of results to `acoustic_rhovp`.
* Total run time: 4.98 seconds.
* Pure simulation time: 4.57 seconds.

# Re-parameterize as m0 and m1.
m0, m1 = (1 / rho) / vp**2, 1 / rho

# Generate the mesh.
m = get_basic_mesh(3)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("M0", par_template * m0)
m.attach_field("M1", par_template * m1)
m.attach_field("fluid", np.ones(m.nelem))

# Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["phi"]
w.physics.wave_equation.point_source = [src_scalar_3d]

# Run the solver.
output_folder = Path("acoustic_linear")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder, overwrite=True)

# Visualize the results.
with h5py.File(output_file, "r") as fh:
    da1 = fh["/volume/phi"][:]

# All parameterizations should have produced the same output.
da0_l2 = np.linalg.norm(da0)
da1_l2 = np.linalg.norm(da1)
np.testing.assert_allclose(da0_l2, da1_l2, atol=da0_l2 * 1e-6)
Job `job_2204302001642082_e45d57f6ba` running on `local_cuda` with 2 rank(s).
Site information:
  * Salvus version: 0.12.0
  * Floating point size: 32
                                          

* Downloaded 66.0 MB of results to `acoustic_linear`.
* Total run time: 6.03 seconds.
* Pure simulation time: 4.69 seconds.



Isotropic elastic models can be parameterized using either:


    

  • velocities {VP, VS, RHO}
    • 

  • Lame's first parameter, shear modulus, and density {LAMBDA, MU, RHO}
    • 

  • Bulk modulus, shear modulus, and density {KAPPA, MU, RHO}
    • 



Velocities and density
# Generate the mesh.
m = get_basic_mesh(3)

# Attach parameter to the nodes of each element.
par_template = np.ones_like(m.get_element_nodes()[:, :, 0])
m.attach_field("VP", par_template * vp)
m.attach_field("VS", par_template * vs)
m.attach_field("RHO", par_template * rho)
m.attach_field("fluid", np.zeros(m.nelem))

# # Attach the mesh and set some custom output.
w.set_mesh(m)
w.output.volume_data.fields = ["displacement"]
w.physics.wave_equation.point_source = [src_vector_3d]

# # Run the solver.
output_folder = Path("elastic_vpvsrho")
output_file = output_folder / "output.h5"
run_salvus(input_file=w, output_folder=output_folder, overwrite=True)

# Visualize the results.
with h5py.File(output_file, "r") as fh:
    de0 = fh["/volume/displacement"][:]
Job `job_2204302001186122_9985d6bb1c` running on `local_cuda` with 2 rank(s).
Site in