mpl.ylim(-0.05, 0.05)
mpl.ylabel('Amplitude')
mpl.subplot(3, 1, 2)
mpl.title('z seismogram')
zseismogram, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-0.05, 0.05)
mpl.ylabel('Amplitude')
ax = mpl.subplot(3, 1, 3)
mpl.title('time: 0.0 s')
wavefield = mpl.imshow(background,
extent=area,
cmap=mpl.cm.gray_r,
vmin=-0.00001,
vmax=0.00001)
mpl.points(stations, '^b', size=8)
mpl.text(500000, 20000, 'Crust')
mpl.text(500000, 60000, 'Mantle')
fig.text(0.7, 0.31, 'Seismometer')
mpl.xlim(area[:2])
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.m2km()
times = np.linspace(0, dt * maxit, maxit)
# This function updates the plot every few timesteps
def animate(i):
t, p, s, xcomp, zcomp = simulation.next()
mpl.title('time: %0.1f s' % (times[t]))
xseismogram, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10**(-3), 10**(-3))
mpl.subplot(2, 2, 4)
mpl.title('z component')
zseismogram, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10**(-3), 10**(-3))
mpl.subplot(1, 2, 1)
# Start with everything zero and grab the plot so that it can be updated later
wavefield = mpl.imshow(np.zeros(shape),
extent=area,
vmin=-10**-6,
vmax=10**-6,
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)
# This function updates the plot every few timesteps
def animate(i):
# Simulation will yield panels corresponding to P and S waves because
# xz2ps=True
t, p, s, xcomp, zcomp = simulation.next()
mpl.title('time: %0.1f s' % (times[t]))
wavefield.set_array((p + s)[::-1])
xseismogram.set_data(times[:t + 1], xcomp[0][:t + 1])
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')
zseismogram, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10 ** (-3), 10 ** (-3))
mpl.subplot(1, 2, 1)
# Start with everything zero and grab the plot so that it can be updated later
wavefield = mpl.imshow(np.zeros(shape), extent=area, vmin=-10 ** -6,
vmax=10 ** -6, 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)
# This function updates the plot every few timesteps
def animate(i):
"""
Simulation will yield panels corresponding to P and S waves because
xz2ps=True
"""
t, p, s, xcomp, zcomp = simulation.next()
mpl.title('time: %0.1f s' % (times[t]))
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')
mpl.xlim(area[:2])
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.subplot(1, 2, 1)
seismogram_plt, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10 ** (-4), 10 ** (-4))
mpl.xlabel('time (s)')
mpl.ylabel('Amplitude')
times = np.linspace(0, duration, maxit)
# Update the plot everytime the simulation yields
def animate(i):
from fatiando.seismic.epic2d import Homogeneous
# Make a velocity model to calculate traveltimes
area = (0, 10, 0, 10)
vp, vs = 2, 1
model = [Square(area, props={'vp':vp, 'vs':vs})]
# Pick the locations of the receivers
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the receivers")
rec_points = mpl.pick_points(area, mpl.gca(), marker='^', color='r')
# and the source
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the source")
mpl.points(rec_points, '^r')
src = mpl.pick_points(area, mpl.gca(), marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
# Calculate the P and S wave traveltimes
srcs, recs = utils.connect_points(src, rec_points)
ptime = ttime2d.straight(model, 'vp', srcs, recs)
stime = ttime2d.straight(model, 'vs', srcs, recs)
# Calculate the residual time (S - P) with added noise
traveltime, error = utils.contaminate(stime - ptime, 0.05, percent=True,
return_stddev=True)
solver = Homogeneous(traveltime, recs, vp, vs)
# Pick the initial estimate and fit
mpl.figure()
mpl.axis('scaled')
rc, axis=0, padlen=len(twt)-2) # convolve rc*wavelet
mpl.ylabel('twt (s)')
mpl.subplot2grid((3, 4), (1, 0), colspan=4) # plot zero-offset traces
traces = utils.matrix2stream(samples.transpose(), header={'delta': dt})
mpl.seismic_image(traces, cmap=mpl.pyplot.cm.jet, aspect='auto', ranges=[0, nx])
mpl.seismic_wiggle(traces, ranges=[0, nx], normalize=True)
mpl.ylabel('twt (s)')
mpl.title("Zero-offset section amplitude",
fontsize=13, family='sans-serif', weight='bold')
ax = mpl.subplot2grid((3, 4), (2, 0), colspan=4) # plot vmodel
mpl.imshow(vmodel, extent=[0, nx, nz*ds, 0],
cmap=mpl.pyplot.cm.bwr, aspect='auto', origin='upper')
ax.autoscale(False)
mpl.ylabel('depth (m)')
stations = [[i, 0] for i in xrange(0, nx, disc)]
mpl.points(stations, '^k', size=7) # zero-offset station points
mpl.text(250, 120, '2900 m/s') # model velocities
mpl.text(450, 315, '2500 m/s')
mpl.text(250, 550, '3500 m/s')
# thickness by lambda/4 2nd axis
ax2 = ax.twiny()
ax2.set_frame_on(True)
ax2.patch.set_visible(False)
ax2.xaxis.set_ticks_position('bottom')
ax2.xaxis.set_label_position('bottom')
ax2.spines['bottom'].set_position(('outward', 20))
def thick_function_thicknessbyres(x):
l4 = 3000/(f*4) # average velocity resolution lambda 4
# pinchout thickness expression
thick = 0
if x > 120:
simulation.add_point_source((125, 75), -1 * wavefd.Gauss(1., fc))
duration = 2.6
maxit = int(duration / simulation.dt)
maxt = duration
# This part makes an animation using matplotlibs animation API
background = (velocity - 6000) * 10**-3
fig = mpl.figure(figsize=(8, 6))
mpl.subplots_adjust(right=0.98, left=0.11, hspace=0.5, top=0.93)
mpl.subplot2grid((4, 3), (0, 0), colspan=3, rowspan=3)
wavefield = mpl.imshow(np.zeros_like(velocity),
extent=area,
cmap=mpl.cm.gray_r,
vmin=-0.05,
vmax=0.05)
mpl.points([75 * ds, 125 * ds], '^b', size=8) # seismometer position
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.m2km()
mpl.subplot2grid((4, 3), (3, 0), colspan=3)
seismogram1, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-0.05, 0.05)
mpl.ylabel('Amplitude')
mpl.xlabel('Time (s)')
times = np.linspace(0, maxt, maxit)
# This function updates the plot every few timesteps
simulation.run(maxit)
seismogram = simulation[:, 125, 75] # (time, z and x) shape
print "Estimated regularization parameter: %g" % (tomo.regul_param_)
# Calculate and print the standard deviation of the residuals
# Should be close to the data error if the inversion was able to fit the data
residuals = tomo.residuals()
print "Assumed error: %g" % (error)
print "Standard deviation of residuals: %g" % (np.std(residuals))
mpl.figure(figsize=(14, 5))
mpl.subplot(1, 2, 1)
mpl.axis('scaled')
mpl.title('Vp model')
mpl.squaremesh(model, prop='vp', cmap=mpl.cm.seismic)
cb = mpl.colorbar()
cb.set_label('Velocity')
mpl.points(src_loc, '*y', label="Sources")
mpl.points(rec_loc, '^r', label="Receivers")
mpl.legend(loc='lower left', shadow=True, numpoints=1, prop={'size': 10})
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.axis('scaled')
mpl.title('Tomography result')
mpl.squaremesh(mesh, prop='vp', vmin=4000, vmax=10000,
cmap=mpl.cm.seismic)
cb = mpl.colorbar()
cb.set_label('Velocity')
mpl.m2km()
mpl.figure()
mpl.grid()
mpl.title('Residuals (data with %.4f s error)' % (error))
mpl.hist(residuals, color='gray', bins=10)
mpl.subplot(3, 1, 1)
mpl.title("Seismogram 1")
seismogram1, = mpl.plot([], [], "-k")
mpl.xlim(0, duration)
mpl.ylim(-0.1, 0.1)
mpl.ylabel("Amplitude")
mpl.subplot(3, 1, 2)
mpl.title("Seismogram 2")
seismogram2, = mpl.plot([], [], "-k")
mpl.xlim(0, duration)
mpl.ylim(-0.1, 0.1)
mpl.ylabel("Amplitude")
ax = mpl.subplot(3, 1, 3)
mpl.title("time: 0.0 s")
wavefield = mpl.imshow(background, extent=area, cmap=mpl.cm.gray_r, vmin=-0.005, vmax=0.005)
mpl.points(stations, "^b", size=8)
mpl.text(750000, 20000, "Crust")
mpl.text(740000, 100000, "Mantle")
fig.text(0.82, 0.33, "Seismometer 2")
fig.text(0.16, 0.33, "Seismometer 1")
mpl.ylim(area[2:][::-1])
mpl.xlabel("x (km)")
mpl.ylabel("z (km)")
mpl.m2km()
times = np.linspace(0, dt * maxit, maxit)
# This function updates the plot every few timesteps
def animate(i):
t, u, seismogram = simulation.next()
mpl.title("time: %0.1f s" % (times[t]))
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
# This function updates the plot every few timesteps
def animate(i):
# Simulation will yield panels corresponding to P and S waves because
# xz2ps=True
t, ux, uz, xcomp, zcomp = simulation.next()
mpl.title('time: %0.3f s' % (times[t]))
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')
mpl.xlim(area[:2])
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.subplot(1, 2, 1)
seismogram_plt, = mpl.plot([], [], '-k')
mpl.xlim(0, duration)
mpl.ylim(-10**(-4), 10**(-4))
mpl.xlabel('time (s)')
mpl.ylabel('Amplitude')
times = np.linspace(0, duration, maxit)
# Update the plot everytime the simulation yields
def animate(i):
mpl.figure()
titles = ['Gravity anomaly', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([gz, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
mpl.show()
# Run the euler deconvolution on moving windows to produce a set of solutions
euler = Classic(xp, yp, zp, gz, xderiv, yderiv, zderiv, 2)
solver = MovingWindow(euler, windows=(10, 10), size=(2000, 2000)).fit()
mpl.figure()
mpl.axis('scaled')
mpl.title('Moving window centers')
mpl.contourf(yp, xp, gz, shape, 50)
mpl.points(solver.window_centers)
mpl.show()
myv.figure()
myv.points(solver.estimate_, size=100.)
myv.prisms(model, opacity=0.5)
axes = myv.axes(myv.outline(bounds), ranges=[b * 0.001 for b in bounds])
myv.wall_bottom(bounds)
myv.wall_north(bounds)
myv.title('Euler solutions')
myv.show()
from fatiando.seismic.epic2d import Homogeneous
# Make a velocity model to calculate traveltimes
area = (0, 10, 0, 10)
vp, vs = 2, 1
model = [Square(area, props={'vp': vp, 'vs': vs})]
# Pick the locations of the receivers
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the receivers")
rec_points = mpl.pick_points(area, mpl.gca(), marker='^', color='r')
# and the source
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the source")
mpl.points(rec_points, '^r')
src = mpl.pick_points(area, mpl.gca(), marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
# Calculate the P and S wave traveltimes
srcs, recs = utils.connect_points(src, rec_points)
ptime = ttime2d.straight(model, 'vp', srcs, recs)
stime = ttime2d.straight(model, 'vs', srcs, recs)
# Calculate the residual time (S - P) with added noise
traveltime, error = utils.contaminate(stime - ptime,
0.05,
percent=True,
return_stddev=True)
solver = Homogeneous(traveltime, recs, vp, vs)
# Pick the initial estimate and fit
maxit = int(duration/dt)
stations = [[2200+i*dx, 0, 0] for i in range(220)] # x, z coordinate of the seismometers
snapshot = 1 # 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
# TODO implement 2D esg version with abs condition
simulation = wavefd.scalar3_esg(pvel, 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[0][::-1], extent=area[:4], alpha=0.25)
wavefield = mpl.imshow(np.zeros(shape[1:]), extent=area[:4], vmin=-10**-7, vmax=10**-7, alpha=0.75, cmap=mpl.cm.gray_r)
mpl.points([ stations[i][:2] for i in range(len(stations))], '.k')
mpl.ylim(area[2:4][::-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
# This function updates the plot every few timesteps
def animate(i):
# t, pressure, seismograms
t, u, zcomp = simulation.next()
mpl.title('time: %0.3f s' % (times[t]))
# wavefield.set_array((p + s)[::-1])
wavefield.set_data(u[0][::-1])
mpl.seismic_wiggle(traces, ranges=[0, nx], normalize=True)
mpl.ylabel('twt (s)')
mpl.title("Zero-offset section amplitude",
fontsize=13,
family='sans-serif',
weight='bold')
ax = mpl.subplot2grid((3, 4), (2, 0), colspan=4) # plot vmodel
mpl.imshow(vmodel,
extent=[0, nx, nz * ds, 0],
cmap=mpl.pyplot.cm.bwr,
aspect='auto',
origin='upper')
ax.autoscale(False)
mpl.ylabel('depth (m)')
stations = [[i, 0] for i in xrange(0, nx, disc)]
mpl.points(stations, '^k', size=7) # zero-offset station points
mpl.text(250, 120, '2900 m/s') # model velocities
mpl.text(450, 315, '2500 m/s')
mpl.text(250, 550, '3500 m/s')
# thickness by lambda/4 2nd axis
ax2 = ax.twiny()
ax2.set_frame_on(True)
ax2.patch.set_visible(False)
ax2.xaxis.set_ticks_position('bottom')
ax2.xaxis.set_label_position('bottom')
ax2.spines['bottom'].set_position(('outward', 20))
def thick_function_thicknessbyres(x):
l4 = 3000 / (f * 4) # average velocity resolution lambda 4
# pinchout thickness expression
sources = [wavefd.GaussSource(125*ds, 75*ds, area, shape, 1., fc)]
dt = wavefd.scalar_maxdt(area, shape, np.max(velocity))
duration = 2.5
maxit = int(duration/dt)
stations = [[75*ds, 125*ds]] # x, z coordinate of the seismometer
snapshots = 3 # every 3 iterations plots one
simulation = wavefd.scalar(velocity, area, dt, maxit, sources, stations, snapshots)
# This part makes an animation using matplotlibs animation API
background = (velocity-4000)*10**-1
fig = mpl.figure(figsize=(8, 6))
mpl.subplots_adjust(right=0.98, left=0.11, hspace=0.5, top=0.93)
mpl.subplot2grid((4, 3), (0,0), colspan=3,rowspan=3)
wavefield = mpl.imshow(np.zeros_like(velocity), extent=area, cmap=mpl.cm.gray_r,
vmin=-1000, vmax=1000)
mpl.points(stations, '^b', size=8)
mpl.ylim(area[2:][::-1])
mpl.xlabel('x (km)')
mpl.ylabel('z (km)')
mpl.m2km()
mpl.subplot2grid((4,3), (3,0), colspan=3)
seismogram1, = mpl.plot([],[],'-k')
mpl.xlim(0, duration)
mpl.ylim(-200, 200)
mpl.ylabel('Amplitude')
times = np.linspace(0, dt*maxit, maxit)
# This function updates the plot every few timesteps
def animate(i):
t, u, seismogram = simulation.next()
seismogram1.set_data(times[:t+1], seismogram[0][:t+1])
wavefield.set_array(background[::-1]+u[::-1])
mpl.figure()
titles = ['Gravity anomaly', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([gz, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
mpl.show()
# Run the euler deconvolution on moving windows to produce a set of solutions
euler = Classic(xp, yp, zp, gz, xderiv, yderiv, zderiv, 2)
solver = MovingWindow(euler, windows=(10, 10), size=(2000, 2000)).fit()
mpl.figure()
mpl.axis('scaled')
mpl.title('Moving window centers')
mpl.contourf(yp, xp, gz, shape, 50)
mpl.points(solver.window_centers)
mpl.show()
myv.figure()
myv.points(solver.estimate_, size=100.)
myv.prisms(model, opacity=0.5)
axes = myv.axes(myv.outline(bounds), ranges=[b * 0.001 for b in bounds])
myv.wall_bottom(bounds)
myv.wall_north(bounds)
myv.title('Euler solutions')
myv.show()
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
# This function updates the plot every few timesteps
def animate(i):
# Simulation will yield panels corresponding to P and S waves because
# xz2ps=True
t, ux, uz, xcomp, zcomp = simulation.next()
mpl.title('time: %0.3f s' % (times[t]))
# wavefield.set_array((p + s)[::-1])
from fatiando.seismic import ttime2d, epic2d
# Make a velocity model to calculate traveltimes
area = (0, 10, 0, 10)
vp, vs = 2, 1
model = [Square(area, props={'vp':vp, 'vs':vs})]
# Pick the locations of the receivers
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the receivers")
rec_points = mpl.pick_points(area, mpl.gca(), marker='^', color='r')
# and the source
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the source")
mpl.points(rec_points, '^r')
src = mpl.pick_points(area, mpl.gca(), marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
# Calculate the P and S wave traveltimes
srcs, recs = utils.connect_points(src, rec_points)
ptime = ttime2d.straight(model, 'vp', srcs, recs)
stime = ttime2d.straight(model, 'vs', srcs, recs)
# Calculate the residual time (S - P) with added noise
traveltime, error = utils.contaminate(stime - ptime, 0.05, percent=True,
return_stddev=True)
solver = epic2d.Homogeneous(traveltime, recs, vp, vs)
# Pick the initial estimate
mpl.figure()
mpl.axis('scaled')
print "Estimated regularization parameter: %g" % (tomo.regul_param_)
# Calculate and print the standard deviation of the residuals
# Should be close to the data error if the inversion was able to fit the data
residuals = tomo.residuals()
print "Assumed error: %g" % (error)
print "Standard deviation of residuals: %g" % (np.std(residuals))
mpl.figure(figsize=(14, 5))
mpl.subplot(1, 2, 1)
mpl.axis('scaled')
mpl.title('Vp model')
mpl.squaremesh(model, prop='vp', cmap=mpl.cm.seismic)
cb = mpl.colorbar()
cb.set_label('Velocity')
mpl.points(src_loc, '*y', label="Sources")
mpl.points(rec_loc, '^r', label="Receivers")
mpl.legend(loc='lower left', shadow=True, numpoints=1, prop={'size': 10})
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.axis('scaled')
mpl.title('Tomography result')
mpl.squaremesh(mesh, prop='vp', vmin=4000, vmax=10000, cmap=mpl.cm.seismic)
cb = mpl.colorbar()
cb.set_label('Velocity')
mpl.m2km()
mpl.figure()
mpl.grid()
mpl.title('Residuals (data with %.4f s error)' % (error))
mpl.hist(residuals, color='gray', bins=10)
mpl.xlabel("seconds")
from fatiando.seismic import ttime2d, epic2d
# Make a velocity model to calculate traveltimes
area = (0, 10, 0, 10)
vp, vs = 2, 1
model = [Square(area, props={'vp': vp, 'vs': vs})]
# Pick the locations of the receivers
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the receivers")
rec_points = mpl.pick_points(area, mpl.gca(), marker='^', color='r')
# and the source
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Choose the location of the source")
mpl.points(rec_points, '^r')
src = mpl.pick_points(area, mpl.gca(), marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
# Calculate the P and S wave traveltimes
srcs, recs = utils.connect_points(src, rec_points)
ptime = ttime2d.straight(model, 'vp', srcs, recs)
stime = ttime2d.straight(model, 'vs', srcs, recs)
# Calculate the residual time (S - P) with added noise
traveltime, error = utils.contaminate(stime - ptime, 0.05, percent=True,
return_stddev=True)
solver = epic2d.Homogeneous(traveltime, recs, vp, vs)
# Pick the initial estimate
mpl.figure()
mpl.axis('scaled')
