# 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))
mpl.subplot(2, 2, 4)
mpl.title('z component')
# 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)
mpl.points(stations, '.k')
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.m2km()
times = np.linspace(0, maxit*dt, maxit)
# the only way for getting the feedback response of the seimograms
global seismograms
mu = wavefd.lame_mu(svel, density)
lamb = wavefd.lame_lamb(pvel, svel, density)
# Make a wave source from a mexican hat wavelet for the x and z directions
sources = [
[wavefd.MexHatSource(350000, 20000, area, shape, 100000, 0.5, delay=2)],
[wavefd.MexHatSource(350000, 20000, 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, pvel.max())
duration = 100
maxit = int(duration/dt)
stations = [[550000, 0]]
snapshots = int(1./dt)
simulation = wavefd.elastic_psv(lamb, mu, density, area, dt, maxit, sources,
stations, snapshots, padding=70, taper=0.005, xz2ps=True)
# This part makes an animation using matplotlibs animation API
background = 10**-5*((pvel - pvel.min())/pvel.max())
fig = mpl.figure(figsize=(10, 8))
mpl.subplots_adjust(right=0.98, left=0.11, hspace=0.3, top=0.93)
mpl.subplot(2, 1, 1)
mpl.title('x seismogram')
xseismogram, = mpl.plot([],[],'-k')
mpl.xlim(0, duration)
mpl.ylim(-0.05, 0.05)
mpl.ylabel('Amplitude')
ax = mpl.subplot(2, 1, 2)
mpl.title('time: 0.0 s')
wavefield = mpl.imshow(density, extent=area, cmap=mpl.cm.gray_r,