mpl.figure()
mpl.title('L-curve: triangle marks the best solution')
tomo.plot_lcurve()
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.figure()
mpl.title('L-curve: triangle marks the best solution')
tomo.plot_lcurve()
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()
# non-linear. So we need to configure fit to use the Levemberg-Marquardt
# algorithm, a gradient descent method, that requires an initial estimate
tomo.config('levmarq', initial=0.0005*numpy.ones(mesh.size)).fit()
residuals = tomo.residuals()
mesh.addprop('vp', tomo.estimate_)
# Calculate and print the standard deviation of the residuals
# it should be close to the data error if the inversion was able to fit the data
print "Assumed error: %f" % (error)
print "Standard deviation of residuals: %f" % (numpy.std(residuals))
mpl.figure(figsize=(14, 5))
mpl.subplot(1, 2, 1)
mpl.axis('scaled')
mpl.title('Vp synthetic model of the Earth')
mpl.squaremesh(model, prop='vp', vmin=vmin, vmax=vmax,
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 (sharp)')
mpl.squaremesh(mesh, prop='vp', vmin=vmin, vmax=vmax,
cmap=mpl.cm.seismic)
cb = mpl.colorbar()
cb.set_label('Velocity')
mpl.m2km()
mpl.figure()
"""
Meshing: Generate a SquareMesh and get the physical properties from an image
"""
import urllib
from fatiando import logger, mesher
from fatiando.vis import mpl
log = logger.get()
log.info(logger.header())
log.info(__doc__)
# Make a square mesh
mesh = mesher.SquareMesh((0, 5000, 0, 5000), (150, 150))
# Fetch the image from the online docs
urllib.urlretrieve(
'http://fatiando.readthedocs.org/en/latest/_static/logo.png', 'logo.png')
# Load the image as the P wave velocity (vp) property of the mesh
mesh.img2prop('logo.png', 5000, 10000, 'vp')
mpl.figure()
mpl.title('P wave velocity model of the Earth')
mpl.squaremesh(mesh, prop='vp')
cb = mpl.colorbar()
cb.set_label("Vp (km/s)")
mpl.show()
"""
Meshing: Generate a SquareMesh and get the physical properties from an image
"""
import urllib
from fatiando import mesher
from fatiando.vis import mpl
# Make a square mesh
mesh = mesher.SquareMesh((0, 5000, 0, 5000), (150, 150))
# Fetch the image from the online docs
urllib.urlretrieve(
'http://fatiando.readthedocs.org/en/latest/_static/logo.png', 'logo.png')
# Load the image as the P wave velocity (vp) property of the mesh
mesh.img2prop('logo.png', 5000, 10000, 'vp')
mpl.figure()
mpl.title('P wave velocity model of the Earth')
mpl.squaremesh(mesh, prop='vp')
cb = mpl.colorbar()
cb.set_label("Vp (km/s)")
mpl.show()
