shape = (200, 200)
area = [0, 60000, 0, 60000]
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
pvel = 6600
svel = 3700
mu = wavefd.lame_mu(svel, density)
lamb = wavefd.lame_lamb(pvel, svel, density)
# Make a wave source from a mexican hat wavelet that vibrates in the x and z
# directions equaly
sources = [[wavefd.MexHatSource(30000, 40000, area, shape, 10000, 1, delay=1)],
[wavefd.MexHatSource(30000, 40000, area, shape, 10000, 1, delay=1)]]
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, pvel)
duration = 20
maxit = int(duration / dt)
stations = [[55000, 0]] # x, z coordinate of the seismometer
snapshot = int(0.5 / dt) # Plot a snapshot of the simulation every 0.5 seconds
simulation = wavefd.elastic_psv(lamb,
mu,
density,
area,
dt,
maxit,
sources,
stations,
snapshot,
padding=50,
taper=0.01,
pvel[int(600/dz)+int(120/dz):int(600/dz)+int(240/dz), :] = 3540 # basalt flow 2
svel = 1600*np.ones(shape) # botucatu
svel[0:int(600/dz), :] = 1166 # bauru
svel[int(600/dz):int(600/dz)+int(120/dz), :] = 2611 # basalt flow 1
svel[int(600/dz)+int(120/dz):int(600/dz)+int(240/dz), :] = 1900 # basalt flow 2
mu = wavefd.lame_mu(svel, density)
lamb = wavefd.lame_lamb(pvel, svel, density)
# Make a wave source from a gauss derivative that vibrates in the z direction only
# (removes the shear component amplitude equals zero, simulating a default seismic acquisition)
sources = [[wavefd.GaussSource(2000, 0, area, shape, 0.0, 5.)], # x source or shear source
[wavefd.GaussSource(2000, 0, area, shape, 100.0, 25.)]] # z source or z compressional source
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, np.max(pvel))
duration = 3.0
maxit = int(duration/dt)
stations = [[2200+i*dx, 0] for i in range(220)] # x, z coordinate of the seismometers
snapshot = int(0.004/dt) # Plot a snapshot of the simulation every 4 miliseconds
print "dt for simulation is ", dt
print "max iteration for simulation is ", maxit
print "duration for simulation is ", duration
simulation = wavefd.elastic_psv(lamb, mu, density, area, dt, maxit, sources,
stations, snapshot, padding=50, taper=0.01)
# This part makes an animation using matplotlibs animation API
fig = mpl.figure(figsize=(12, 5))
# Start with everything zero and grab the plot so that it can be updated later
mpl.imshow(pvel[::-1], extent=area, alpha=0.25)
wavefield = mpl.imshow(np.zeros(shape), extent=area, vmin=-10**-7, vmax=10**-7, alpha=0.75, cmap=mpl.cm.gray_r)
shape = (200, 200)
area = [0, 60000, 0, 60000]
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
pvel = 6600
svel = 3700
mu = wavefd.lame_mu(svel, density)
lamb = wavefd.lame_lamb(pvel, svel, density)
# Make a wave source from a mexican hat wavelet that vibrates in the x and z
# directions equaly
sources = [[wavefd.MexHatSource(30000, 40000, area, shape, 10000, 1, delay=1)],
[wavefd.MexHatSource(30000, 40000, area, shape, 10000, 1, delay=1)]]
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, pvel)
duration = 20
maxit = int(duration / dt)
stations = [[55000, 0]] # x, z coordinate of the seismometer
snapshot = int(0.5 / dt) # Plot a snapshot of the simulation every 0.5 seconds
simulation = wavefd.elastic_psv(lamb, mu, density, area, dt, maxit, sources,
stations, snapshot, padding=50, taper=0.01,
xz2ps=True)
# This part makes an animation using matplotlibs animation API
fig = mpl.figure(figsize=(12, 5))
mpl.subplot(2, 2, 2)
mpl.title('x component')
xseismogram, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10 ** (-3), 10 ** (-3))
from fatiando.seismic import wavefd
from fatiando.vis import mpl
# Set the parameters of the finite difference grid
shape = (150, 150)
area = [0, 60000, 0, 60000]
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
velocity = 3700
mu = wavefd.lame_mu(velocity, density)
# Make a wave source from a mexican hat wavelet
sources = [wavefd.MexHatSource(30000, 15000, area, shape, 100, 1, delay=2)]
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, velocity)
duration = 20
maxit = int(duration / dt)
stations = [[50000, 0]] # x, z coordinate of the seismometer
snapshot = int(0.5 / dt) # Plot a snapshot of the simulation every 0.5 seconds
simulation = wavefd.elastic_sh(mu, density, area, dt, maxit, sources, stations,
snapshot, padding=50, taper=0.01)
# This part makes an animation using matplotlibs animation API
fig = mpl.figure(figsize=(14, 5))
ax = mpl.subplot(1, 2, 2)
mpl.title('Wavefield')
# Start with everything zero and grab the plot so that it can be updated later
wavefield_plt = mpl.imshow(np.zeros(shape), extent=area, vmin=-10 ** (-5),
vmax=10 ** (-5), cmap=mpl.cm.gray_r)
mpl.points(stations, '^b')
# Set the parameters of the finite difference grid
shape = (200, 1000)
area = [0, 800000, 0, 160000]
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
svel = 3500 * np.ones(shape)
mu = wavefd.lame_mu(svel, density)
# Make a wave source from a mexican hat wavelet
sources = [wavefd.MexHatSource(10000, 10000, area, shape, 100000, 0.5, delay=2)]
# Get the iterator. This part only generates an iterator object. The actual
# computations take place at each iteration in the for loop below
dt = wavefd.maxdt(area, shape, svel.max())
duration = 250
maxit = int(duration / dt)
stations = [[100000, 0], [700000, 0]]
snapshots = int(1. / dt)
simulation = wavefd.elastic_sh(mu, density, area, dt, maxit, sources, stations,
snapshots, padding=70, taper=0.005)
# This part makes an animation using matplotlibs animation API
fig = mpl.figure(figsize=(10, 8))
mpl.subplots_adjust(right=0.98, left=0.11, hspace=0.3, top=0.93)
mpl.subplot(3, 1, 1)
mpl.title('Seismogram 1')
seismogram1, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-0.1, 0.1)
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
svel = 3500 * np.ones(shape)
moho = 50
density[moho:] = 2800
svel[moho:] = 4500
mu = wavefd.lame_mu(svel, density)
# Make a wave source from a mexican hat wavelet
sources = [
wavefd.MexHatSource(10000, 10000, area, shape, 100000, 0.5, delay=2)
]
# Get the iterator. This part only generates an iterator object. The actual
# computations take place at each iteration in the for loop below
dt = wavefd.maxdt(area, shape, svel.max())
duration = 250
maxit = int(duration / dt)
stations = [[100000, 0], [700000, 0]]
snapshots = int(1. / dt)
simulation = wavefd.elastic_sh(mu,
density,
area,
dt,
maxit,
sources,
stations,
snapshots,
padding=70,
taper=0.005)
svel[int(600 / dz) + int(120 / dz):int(600 / dz) +
int(240 / dz), :] = 1900 # basalt flow 2
mu = wavefd.lame_mu(svel, density)
lamb = wavefd.lame_lamb(pvel, svel, density)
# Make a wave source from a gauss derivative that vibrates in the z direction only
# (removes the shear component amplitude equals zero, simulating a default seismic acquisition)
sources = [
[wavefd.GaussSource(2000, 0, area, shape, 0.0,
5.)], # x source or shear source
[wavefd.GaussSource(2000, 0, area, shape, 100.0, 25.)]
] # z source or z compressional source
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, np.max(pvel))
duration = 3.0
maxit = int(duration / dt)
stations = [[2200 + i * dx, 0]
for i in range(220)] # x, z coordinate of the seismometers
snapshot = int(0.004 /
dt) # Plot a snapshot of the simulation every 4 miliseconds
print "dt for simulation is ", dt
print "max iteration for simulation is ", maxit
print "duration for simulation is ", duration
simulation = wavefd.elastic_psv(lamb,
mu,
density,
area,
dt,
maxit,
from fatiando.seismic import wavefd
from fatiando.vis import mpl
# Set the parameters of the finite difference grid
shape = (150, 150)
area = [0, 60000, 0, 60000]
# Make a density and S wave velocity model
density = 2400 * np.ones(shape)
velocity = 3700
mu = wavefd.lame_mu(velocity, density)
# Make a wave source from a mexican hat wavelet
sources = [wavefd.MexHatSource(30000, 15000, area, shape, 100, 1, delay=2)]
# Get the iterator for the simulation
dt = wavefd.maxdt(area, shape, velocity)
duration = 20
maxit = int(duration / dt)
stations = [[50000, 0]] # x, z coordinate of the seismometer
snapshot = int(0.5 / dt) # Plot a snapshot of the simulation every 0.5 seconds
simulation = wavefd.elastic_sh(mu,
density,
area,
dt,
maxit,
sources,
stations,
snapshot,
padding=50,
taper=0.01)
"""
import numpy as np
import sys
from fatiando.seismic import wavefd
# Set the parameters of the finite difference grid 3D
shape = (100, 100, 100)
ds = 10. # spacing
area = [0, shape[2]*ds, 0, shape[1]*ds, 0, shape[0]*ds]
# Set the parameters of the finite difference grid
velocity = np.ones(shape)*2500. # m/s
velocity[50:100, 50:100, 50:100] = 1500. # m/s
# avoiding spatial alias, frequency of source should be smaller than this
fc = 0.5*np.min(velocity)/ds # based on plane waves v=l*f
fc -= 0.5*fc
sources = [wavefd.GaussSource((40*ds, 40*ds, 30*ds), area, shape, 10**(-8), fc)]
dt = wavefd.maxdt(area, shape, np.max(velocity))
duration = 0.6
maxit = int(duration/dt)
# x, y, z coordinate of the seismometer
stations = [[45*ds, 45*ds, 65*ds], [65*ds, 65*ds, 30*ds]]
snapshots = 5 # every 1 iterations plots one
simulation = wavefd.scalar3(velocity, area, dt, maxit,
sources, stations, snapshots)
movie = np.zeros(((maxit/snapshots)+2, 100, 100, 100))
i = 0
for t, u, seismogram in simulation:
movie[i] = u
sys.stdout.write("\rprogressing .. %.1f%% time %.3f"%(100.0*float(t)/maxit, (dt*t)))
sys.stdout.flush()
i += 1
