def test_utils_contaminate_same_seed():
"utils.contaminate uses same noise using same random seed"
size = 1000
data = numpy.linspace(-1000, 1000, size)
noise = 10
for seed in numpy.random.randint(low=0, high=10000, size=20):
d1 = utils.contaminate(data, noise, seed=seed)
d2 = utils.contaminate(data, noise, seed=seed)
assert numpy.all(d1 == d2)
def test_utils_contaminate_diff():
"utils.contaminate uses diff noise"
size = 1235
data = numpy.linspace(-100., 12255., size)
noise = 244.4
for i in range(20):
d1 = utils.contaminate(data, noise)
d2 = utils.contaminate(data, noise)
assert numpy.all(d1 != d2)
def test_utils_contaminate_diff():
"utils.contaminate uses diff noise"
size = 1235
data = numpy.linspace(-100., 12255., size)
noise = 244.4
for i in xrange(20):
d1 = utils.contaminate(data, noise)
d2 = utils.contaminate(data, noise)
assert numpy.all(d1 != d2)
def test_utils_contaminate_same_seed():
"utils.contaminate uses same noise using same random seed"
size = 1000
data = numpy.linspace(-1000, 1000, size)
noise = 10
for seed in numpy.random.randint(low=0, high=10000, size=20):
d1 = utils.contaminate(data, noise, seed=seed)
d2 = utils.contaminate(data, noise, seed=seed)
assert numpy.all(d1 == d2)
def test_utils_contaminate_seed_noseed():
"utils.contaminate uses diff noise after using random seed"
size = 1000
data = numpy.linspace(-1000, 1000, size)
noise = 10
seed = 45212
d1 = utils.contaminate(data, noise, seed=seed)
d2 = utils.contaminate(data, noise, seed=seed)
assert numpy.all(d1 == d2)
d3 = utils.contaminate(data, noise)
assert numpy.all(d1 != d3)
def test_utils_contaminate_seed_noseed():
"utils.contaminate uses diff noise after using random seed"
size = 1000
data = numpy.linspace(-1000, 1000, size)
noise = 10
seed = 45212
d1 = utils.contaminate(data, noise, seed=seed)
d2 = utils.contaminate(data, noise, seed=seed)
assert numpy.all(d1 == d2)
d3 = utils.contaminate(data, noise)
assert numpy.all(d1 != d3)
def test_utils_contaminate():
"utils.contaminate generates noise with 0 mean and right stddev"
size = 10**6
data = numpy.zeros(size)
std = 4.213
for i in range(20):
noise = utils.contaminate(data, std)
assert abs(noise.mean()) < 10**-10, 'mean:%g' % (noise.mean())
assert abs(noise.std() - std) / std < 0.01, 'std:%g' % (noise.std())
def test_utils_contaminate():
"utils.contaminate generates noise with 0 mean and right stddev"
size = 10**6
data = numpy.zeros(size)
std = 4.213
for i in xrange(20):
noise = utils.contaminate(data, std)
assert abs(noise.mean()) < 10**-10, 'mean:%g' % (noise.mean())
assert abs(noise.std() - std)/std < 0.01, 'std:%g' % (noise.std())
def test_utils_contaminate_seed():
"utils.contaminate noise with 0 mean and right stddev using random seed"
size = 10**6
data = numpy.zeros(size)
std = 4400.213
for i in xrange(20):
noise = utils.contaminate(data, std, seed=i)
assert abs(noise.mean()) < 10**-10, 's:%d mean:%g' % (i, noise.mean())
assert abs(noise.std() - std)/std < 0.01, 's:%d std:%g' % (i,
noise.std())
def test_utils_contaminate_seed():
"utils.contaminate noise with 0 mean and right stddev using random seed"
size = 10**6
data = numpy.zeros(size)
std = 4400.213
for i in xrange(20):
noise = utils.contaminate(data, std, seed=i)
assert abs(noise.mean()) < 10**-10, 's:%d mean:%g' % (i, noise.mean())
assert abs(noise.std() - std) / std < 0.01, 's:%d std:%g' % (
i, noise.std())
def update(self):
self.predtts = utils.contaminate(fatiando.seismic.profile.vertical(
self.thickness, self.velocity, self.zp),
self.error,
percent=True)
self.predplot.set_data(self.predtts, self.zp)
if self.tts is not None:
xmin = min(self.predtts.min(), self.tts.min())
xmax = max(self.predtts.max(), self.tts.max())
else:
xmin = self.predtts.min()
xmax = self.predtts.max()
if xmin != xmax:
self.dcanvas.set_xlim(xmin, xmax)
def update(self):
self.predtts = utils.contaminate(
fatiando.seismic.profile.vertical(self.thickness, self.velocity,
self.zp),
self.error, percent=True)
self.predplot.set_data(self.predtts, self.zp)
if self.tts is not None:
xmin = min(self.predtts.min(), self.tts.min())
xmax = max(self.predtts.max(), self.tts.max())
else:
xmin = self.predtts.min()
xmax = self.predtts.max()
if xmin != xmax:
self.dcanvas.set_xlim(xmin, xmax)
def update(self):
if self.polygons:
polys = []
for p, d in zip(self.polygons, self.densities):
polys.append(Polygon(1000. * numpy.array(p), {'density': d}))
self.predgz = utils.contaminate(
talwani.gz(self.xp, self.zp, polys), self.error)
else:
self.predgz = numpy.zeros_like(self.xp)
self.predplot.set_data(self.xp * 0.001, self.predgz)
if self.gz is not None:
ymin = min(self.predgz.min(), self.gz.min())
ymax = max(self.predgz.max(), self.gz.max())
else:
ymin = self.predgz.min()
ymax = self.predgz.max()
if ymin != ymax:
self.dcanvas.set_ylim(ymin, ymax)
self.draw()
def update(self):
if self.polygons:
polys = []
for p, d in zip(self.polygons, self.densities):
polys.append(Polygon(1000.*numpy.array(p), {'density':d}))
self.predgz = utils.contaminate(talwani.gz(self.xp, self.zp, polys),
self.error)
else:
self.predgz = numpy.zeros_like(self.xp)
self.predplot.set_data(self.xp*0.001, self.predgz)
if self.gz is not None:
ymin = min(self.predgz.min(), self.gz.min())
ymax = max(self.predgz.max(), self.gz.max())
else:
ymin = self.predgz.min()
ymax = self.predgz.max()
if ymin != ymax:
self.dcanvas.set_ylim(ymin, ymax)
self.draw()
from matplotlib import pyplot
import numpy
from fatiando.heat import climatesignal
from fatiando.inversion.gradient import levmarq
from fatiando import vis, utils
params = __import__('exercicio3e4_entrada')
zp = numpy.arange(0, 100, 1)
temp, error = utils.contaminate(climatesignal.linear(params.amplitude,
params.idade, zp),
params.ruido,
percent=True,
return_stddev=True)
solver = levmarq(initial=params.inicial)
p, residuals = climatesignal.invert_linear(temp, zp, solver)
est_amp, est_age = p
pyplot.figure(figsize=(12, 5))
pyplot.subplot(1, 2, 1)
pyplot.title("Sinal climatico")
pyplot.plot(temp, zp, 'ok', label='Observado')
pyplot.plot(temp - residuals, zp, '--r', linewidth=3, label='Predito')
pyplot.legend(loc='lower right', numpoints=1)
pyplot.xlabel("Temperatura (C)")
pyplot.ylabel("Z")
pyplot.ylim(100, 0)
ax = pyplot.subplot(1, 2, 2)
ax2 = pyplot.twinx()
pyplot.title("Idade e amplitude")
width = 0.3
ax.bar([1 - width], [params.idade], width, color='b', label="Verdadeiro")
(more complex model + noisy data)
"""
from fatiando import gridder, mesher, utils
from fatiando.gravmag import prism, imaging
from fatiando.vis import mpl, myv
# Make some synthetic gravity data from a simple prism model
model = [
mesher.Prism(-4000, 0, -4000, -2000, 2000, 5000, {'density': 1200}),
mesher.Prism(-1000, 1000, -1000, 1000, 1000, 7000, {'density': -800}),
mesher.Prism(2000, 4000, 3000, 4000, 0, 2000, {'density': 600})
]
# Calculate on a scatter of points to show that migration doesn't need gridded
# data
xp, yp, zp = gridder.scatter((-6000, 6000, -6000, 6000), 1000, z=-10)
gz = utils.contaminate(prism.gz(xp, yp, zp, model), 0.1)
# Plot the data
shape = (50, 50)
mpl.figure()
mpl.axis('scaled')
mpl.contourf(yp, xp, gz, shape, 30, interp=True)
mpl.colorbar()
mpl.plot(yp, xp, '.k')
mpl.xlabel('East (km)')
mpl.ylabel('North (km)')
mpl.m2km()
mpl.show()
mesh = imaging.migrate(xp, yp, zp, gz, 0, 10000, (30, 30, 30), power=0.8)
bounds = [0, 5000, 0, 5000, 0, 1000]
model = [Prism3D(600, 1200, 200, 4200, 400, 900, {'density':1000}),
Prism3D(1500, 4500, 2500, 3000, 300, 800, {'density':-1000}),
Prism3D(3000, 4000, 1000, 2000, 200, 800, {'density':700}),
Prism3D(2700, 3200, 3700, 4200, 0, 900, {'density':900})]
with open('model.pickle', 'w') as f:
pickle.dump(model, f)
shape = (26, 26)
area = bounds[0:4]
noise = 5
x, y, z = gridder.regular(area, shape, z=-150)
tensor = (potential.prism.gxx(x, y, z, model),
potential.prism.gxy(x, y, z, model),
potential.prism.gxz(x, y, z, model),
potential.prism.gyy(x, y, z, model),
potential.prism.gyz(x, y, z, model),
potential.prism.gzz(x, y, z, model))
tensor_noisy = [utils.contaminate(d, noise) for d in tensor]
data = [x, y, z]
data.extend(tensor_noisy)
with open('data.txt', 'w') as f:
f.write("# Noise corrupted tensor components:\n")
f.write("# noise = %g Eotvos\n" % (noise))
f.write("# x y z gxx gxy gxz gyy gyz gzz\n")
numpy.savetxt(f, numpy.array(data).T)
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
# Direction of the Geomagnetic field
inc, dec = -60, 0
# Make a model with only induced magnetization
model = [
mesher.Prism(-100, 100, -100, 100, 0, 2000,
{'magnetization': utils.ang2vec(10, inc, dec)})
]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
tf = utils.contaminate(prism.tf(x, y, z, model, inc, dec), 1, seed=0)
# Reduce to the pole using FFT. Since there is only induced magnetization, the
# magnetization direction (sinc and sdec) is the same as the geomagnetic field
pole = transform.reduce_to_pole(x, y, tf, shape, inc, dec, sinc=inc, sdec=dec)
# Calculate the true value at the pole for comparison
true = prism.tf(x, y, z, model, 90, 0, pmag=utils.ang2vec(10, 90, 0))
fig, axes = mpl.subplots(1, 3, figsize=(14, 4))
for ax in axes:
ax.set_aspect('equal')
mpl.sca(axes[0])
mpl.title("Original total field anomaly")
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(pad=0).set_label('nT')
mpl.m2km()
mpl.sca(axes[1])
from fatiando import gravmag as gm
from fatiando.mesher import Prism, PrismMesh, vremove
from fatiando.vis import mpl, myv
log = logger.get()
log.info(logger.header())
# Create a synthetic model
model = [Prism(250, 750, 250, 750, 200, 700, {'density':1000})]
# and generate synthetic data from it
shape = (25, 25)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
noise = 0.1 # 0.1 mGal noise
gz = utils.contaminate(gm.prism.gz(xp, yp, zp, model), noise)
# plot the data
mpl.figure()
mpl.title("Synthetic gravity anomaly (mGal)")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
mpl.m2km()
mpl.show()
# Inversion setup
# Create a mesh
mesh = PrismMesh(bounds, (25, 25, 25))
# Wrap the data so that harvester can use it
from matplotlib import pyplot
import numpy
from fatiando.seismic import profile
from fatiando import vis, utils, ui
area = (0, 10000, 0, 600)
vmin, vmax, zmin, zmax = area
figure = pyplot.figure()
pyplot.xlabel("Velocidade (m/s)")
pyplot.ylabel("Profundidade (m)")
thickness, velocity = ui.picker.draw_layers(area, figure.gca())
zp = numpy.arange(zmin + 1, zmax, 1)
tts, error = utils.contaminate(profile.vertical(thickness, velocity, zp), 0.0,
percent=True, return_stddev=True)
pyplot.figure(figsize=(12,5))
pyplot.subplot(1, 2, 1)
pyplot.grid()
pyplot.title("Perfilagem sismica vertical")
pyplot.plot(tts, zp, '.k')
pyplot.xlabel("Tempo de chegada (s)")
pyplot.ylabel("Profundidade (m)")
pyplot.ylim(sum(thickness), 0)
pyplot.subplot(1, 2, 2)
pyplot.grid()
pyplot.title("Perfil de velocidades")
vis.map.layers(thickness, velocity, '--b', linewidth=2)
pyplot.ylim(zmax, zmin)
pyplot.xlim(vmin, vmax)
pyplot.xlabel("Velocidade (m/s)")
"""
GravMag: Generate noise-corrupted gravity gradient tensor data
"""
from fatiando import mesher, gridder, gravmag, utils
from fatiando.vis import mpl
prisms = [mesher.Prism(-1000,1000,-1000,1000,0,2000,{'density':1000})]
shape = (100,100)
xp, yp, zp = gridder.regular((-5000, 5000, -5000, 5000), shape, z=-200)
components = [gravmag.prism.gxx, gravmag.prism.gxy, gravmag.prism.gxz,
gravmag.prism.gyy, gravmag.prism.gyz, gravmag.prism.gzz]
print "Calculate the tensor components and contaminate with 5 Eotvos noise"
ftg = [utils.contaminate(comp(xp, yp, zp, prisms), 5.0) for comp in components]
print "Plotting..."
mpl.figure(figsize=(14,6))
mpl.suptitle("Contaminated FTG data")
names = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
for i, data in enumerate(ftg):
mpl.subplot(2,3,i+1)
mpl.title(names[i])
mpl.axis('scaled')
levels = mpl.contourf(xp*0.001, yp*0.001, data, (100,100), 12)
mpl.colorbar()
mpl.contour(xp*0.001, yp*0.001, data, shape, levels, clabel=False)
mpl.show()
GravMag: Simple gravity inversion for the relief of a 2D trapezoidal basin
"""
import numpy
from fatiando import logger, utils, mesher, gravmag, inversion
from fatiando.vis import mpl
log = logger.get()
log.info(logger.header())
log.info(__doc__)
log.info("Generating synthetic data")
verts = [(10000, 1.), (90000, 1.), (90000, 7000), (10000, 3330)]
model = mesher.Polygon(verts, {'density':-100})
xp = numpy.arange(0., 100000., 1000.)
zp = numpy.zeros_like(xp)
gz = utils.contaminate(gravmag.talwani.gz(xp, zp, [model]), 0.5)
log.info("Preparing for the inversion")
solver = inversion.gradient.levmarq(initial=(9000, 500))
estimate, residuals = gravmag.basin2d.trapezoidal(xp, zp, gz, verts[0:2], -100,
solver)
mpl.figure()
mpl.subplot(2, 1, 1)
mpl.title("Gravity anomaly")
mpl.plot(xp, gz, 'ok', label='Observed')
mpl.plot(xp, gz - residuals, '-r', linewidth=2, label='Predicted')
mpl.legend(loc='lower left', numpoints=1)
mpl.ylabel("mGal")
mpl.xlim(0, 100000)
mpl.subplot(2, 1, 2)
"""
Geothermal: Forward and inverse modeling of a linear change in temperature
measured in a well
"""
import numpy
from fatiando import utils
from fatiando.geothermal.climsig import linear, SingleChange
from fatiando.vis import mpl
# Generating synthetic data
amp = 5.43
age = 78.2
# along a well at these depths
zp = numpy.arange(0, 100, 1)
temp, error = utils.contaminate(linear(amp, age, zp),
0.02,
percent=True,
return_stddev=True)
# Preparing for the inversion
data = SingleChange(temp, zp, mode='linear').config('levmarq', initial=[1, 1])
amp_, age_ = data.fit().estimate_
print "Linear change in temperature"
print " true: amp=%.3f age=%.3f" % (amp, age)
print " estimated: amp=%.3f age=%.3f" % (amp_, age_)
mpl.figure(figsize=(4, 5))
mpl.title("Residual well temperature")
mpl.plot(temp, zp, 'ok', label='Observed')
mpl.plot(data.predicted(), zp, '--r', linewidth=3, label='Predicted')
mpl.legend(loc='lower right', numpoints=1)
"""
GravMag: Simple gravity inversion for the relief of a 2D triangular basin
"""
import numpy
from fatiando import utils, mesher, gravmag, inversion
from fatiando.vis import mpl
verts = [(10000, 1.), (90000, 1.), (80000, 5000)]
model = mesher.Polygon(verts, {'density': -100})
xp = numpy.arange(0., 100000., 1000.)
zp = numpy.zeros_like(xp)
gz = utils.contaminate(gravmag.talwani.gz(xp, zp, [model]), 1)
solver = inversion.gradient.levmarq(initial=(10000, 1000))
estimate, residuals = gravmag.basin2d.triangular(xp, zp, gz, verts[0:2], -100,
solver)
mpl.figure()
mpl.subplot(2, 1, 1)
mpl.title("Gravity anomaly")
mpl.plot(xp, gz, 'ok', label='Observed')
mpl.plot(xp, gz - residuals, '-r', linewidth=2, label='Predicted')
mpl.legend(loc='lower left')
mpl.ylabel("mGal")
mpl.xlim(0, 100000)
mpl.subplot(2, 1, 2)
mpl.polygon(estimate,
'o-r',
linewidth=2,
fill='r',
alpha=0.3,
Seismic: Invert vertical seismic profile (VSP) traveltimes using smoothness
regularization and unknown layer thicknesses
"""
import numpy
from fatiando import utils
from fatiando.seismic.profile import layered_straight_ray, LayeredStraight
from fatiando.inversion.regularization import Smoothness1D
from fatiando.vis import mpl
# Make a layered model
thickness = [10, 20, 10, 30, 40, 60]
velocity = [2000, 1000, 5000, 1000, 3000, 6000]
zp = numpy.arange(1, sum(thickness), 1, dtype='f')
# Produce some noise-corrupted synthetic data
tts, error = utils.contaminate(
layered_straight_ray(thickness, velocity, zp),
0.02, percent=True, return_stddev=True)
# Assume that the thicknesses are unknown. In this case, use a mesh of many
# thin layers and invert for each slowness
thick = 10.
mesh = [thick]*int(sum(thickness)/thick)
solver = (LayeredStraight(tts, zp, mesh) +
5*Smoothness1D(len(mesh))).fit()
velocity_ = solver.estimate_
mpl.figure(figsize=(12,5))
mpl.subplot(1, 2, 1)
mpl.grid()
mpl.title("Vertical seismic profile")
mpl.plot(tts, zp, 'ok', label='Observed')
mpl.plot(solver.predicted(), zp, '-r', linewidth=3, label='Predicted')
from matplotlib import pyplot
import numpy
from fatiando.seismic import profile
from fatiando import vis, utils, ui
import cPickle as pickle
area = (0, 10000, 0, 600)
vmin, vmax, zmin, zmax = area
figure = pyplot.figure()
pyplot.xlabel("Velocidade (m/s)")
pyplot.ylabel("Profundidade (m)")
thickness, velocity = ui.picker.draw_layers(area, figure.gca())
zp = numpy.arange(zmin + 1, zmax, 1)
tts, error = utils.contaminate(profile.vertical(thickness, velocity, zp),
0.01,
percent=True,
return_stddev=True)
with open('exercicio5.pickle', 'w') as f:
data = {'thickness': thickness, 'velocity': velocity, 'tts': tts, 'zp': zp}
pickle.dump(data, f)
pyplot.figure(figsize=(12, 5))
pyplot.subplot(1, 2, 1)
pyplot.grid()
pyplot.title("Perfilagem sismica vertical")
pyplot.plot(tts, zp, '.k')
pyplot.xlabel("Tempo de chegada (s)")
pyplot.ylabel("Profundidade (m)")
pyplot.ylim(sum(thickness), 0)
pyplot.subplot(1, 2, 2)
pylab.subplots_adjust(wspace=0.4, hspace=0.3)
for i, field in enumerate(['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']):
data = synthetic.from_prisms(prisms,
x1=0,
x2=5000,
y1=0,
y2=5000,
nx=50,
ny=50,
height=150,
field=field)
data['value'], error = utils.contaminate(data['value'],
stddev=error,
percent=False,
return_stddev=True)
data['error'] = error * numpy.ones(len(data['value']))
io.dump('%s_data.txt' % (field), data)
pylab.subplot(2, 3, i + 1)
pylab.axis('scaled')
pylab.title(field)
vis.contourf(data, 10)
vis.contourf(data, 10)
cb = pylab.colorbar(shrink=0.9)
cb.set_label(r'$E\"otv\"os$', fontsize=14)
pylab.xlabel('X [m]')
pylab.ylabel('Y [m]')
"""
GravMag: Calculate the analytic signal of a total field anomaly using FFT
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
model = [mesher.Prism(-100, 100, -100, 100, 0, 2000, {'magnetization': 10})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
inc, dec = -30, 0
tf = utils.contaminate(prism.tf(x, y, z, model, inc, dec), 0.001,
percent=True)
# Need to convert gz to SI units so that the result is also in SI
total_grad_amp = transform.tga(x, y, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Total Gradient Amplitude")
mpl.axis('scaled')
mpl.contourf(y, x, total_grad_amp, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT/m')
from fatiando.vis import mpl, myv
# Generate some synthetic total field anomaly data
bounds = [0, 10000, 0, 10000, 0, 5000]
props = {'density': 500}
props2 = {'density': 1000}
model = [
mesher.Prism(4000, 6000, 4000, 6000, 500, 2500, props),
mesher.Prism(2000, 2500, 2000, 2500, 500, 1000, props2),
mesher.Prism(7500, 8000, 5500, 6500, 500, 1000, props2),
mesher.Prism(1500, 2000, 4000, 5000, 500, 1000, props2)
]
area = bounds[:4]
shape = (50, 50)
x, y, z = gridder.regular(area, shape, z=-1)
gz = utils.contaminate(gravmag.prism.gz(x, y, z, model), 0.1)
mesh = mesher.PrismMesh(bounds, (20, 40, 40))
seeds = gravmag.harvester.sow([[5000, 5000, 1000, props]], mesh)
# Run the inversion without using weights
data = [gravmag.harvester.Gz(x, y, z, gz)]
estimate, predicted = gravmag.harvester.harvest(data,
seeds,
mesh,
compactness=1.5,
threshold=0.001)
mesh.addprop('density', estimate['density'])
bodies = mesher.vremove(0, 'density', mesh)
mpl.figure()
mpl.axis('scaled')
mpl.title('No weights: Observed (color) vs Predicted (black)')
from fatiando import mesher, utils, seismic, vis, inversion
area = (0, 100000, 0, 100000)
shape = (100, 100)
model = mesher.SquareMesh(area, shape)
# Fetch the image from the online docs
urllib.urlretrieve(
'http://fatiando.readthedocs.org/en/latest/_static/logo.png', 'logo.png')
model.img2prop('logo.png', 4000, 10000, 'vp')
# Make some travel time data and add noise
src_loc = utils.random_points(area, 200)
rec_loc = utils.circular_points(area, 80, random=True)
srcs, recs = utils.connect_points(src_loc, rec_loc)
ttimes = seismic.ttime2d.straight(model, 'vp', srcs, recs, par=True)
ttimes, error = utils.contaminate(ttimes, 0.01, percent=True,
return_stddev=True)
# Make the mesh
mesh = mesher.SquareMesh(area, shape)
# Since the matrices are big, use the Steepest Descent solver to avoid dealing
# with Hessian matrices. It needs a starting guess, so start with 1000
inversion.gradient.use_sparse()
solver = inversion.gradient.steepest(1000*numpy.ones(mesh.size))
# and run the inversion
estimate, residuals = seismic.srtomo.run(ttimes, srcs, recs, mesh, sparse=True,
solver=solver, smooth=0.01)
# Convert the slowness estimate to velocities and add it the mesh
mesh.addprop('vp', seismic.srtomo.slowness2vel(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)
"""
Geothermal: Forward and inverse modeling of an abrupt change in temperature
measured in a well
"""
import numpy
from fatiando import utils
from fatiando.geothermal import climsig
from fatiando.vis import mpl
# Generating synthetic data
amp = 3
age = 54
zp = numpy.arange(0, 100, 1)
temp, error = utils.contaminate(climsig.abrupt(amp, age, zp),
0.02,
percent=True,
return_stddev=True)
# Preparing for the inversion
p, residuals = climsig.iabrupt(temp, zp)
est_amp, est_age = p
mpl.figure(figsize=(12, 5))
mpl.subplot(1, 2, 1)
mpl.title("Climate signal (abrupt)")
mpl.plot(temp, zp, 'ok', label='Observed')
mpl.plot(temp - residuals, zp, '--r', linewidth=3, label='Predicted')
mpl.legend(loc='lower right', numpoints=1)
mpl.xlabel("Temperature (C)")
mpl.ylabel("Z")
mpl.ylim(100, 0)
"""
GravMag: Center of mass estimation using the first eigenvector of the gravity
gradient tensor (2 sources with expanding windows)
"""
from fatiando import mesher, gridder, utils, gravmag
from fatiando.vis import mpl, myv
# Generate some synthetic data
prisms = [mesher.Prism(-2500,-500,-1000,1000,500,2500,{'density':1000}),
mesher.Prism(500,2500,-1000,1000,500,2500,{'density':1000})]
shape = (100, 100)
area = (-5000, 5000, -5000, 5000)
xp, yp, zp = gridder.regular(area, shape, z=-150)
noise = 2
tensor = [utils.contaminate(gravmag.prism.gxx(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gxy(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gxz(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gyy(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gyz(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gzz(xp, yp, zp, prisms), noise)]
# Get the eigenvectors from the tensor data
eigenvals, eigenvecs = gravmag.tensor.eigen(tensor)
# Plot the data
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
for i, title in enumerate(titles):
mpl.subplot(3, 2, i + 1)
mpl.title(title)
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tensor[i], shape, 10)
mpl.contour(yp, xp, tensor[i], shape, levels)
rec_points = vis.mpl.pick_points(area, ax, marker='^', color='r')
vis.mpl.figure()
ax = vis.mpl.subplot(1, 1, 1)
vis.mpl.axis('scaled')
vis.mpl.suptitle("Choose the location of the source")
vis.mpl.points(rec_points, '^r')
src = vis.mpl.pick_points(area, ax, marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
srcs, recs = utils.connect_points(src, rec_points)
ptime = seismic.ttime2d.straight(model, 'vp', srcs, recs)
stime = seismic.ttime2d.straight(model, 'vs', srcs, recs)
ttresiduals, error = utils.contaminate(stime - ptime, 0.10, percent=True,
return_stddev=True)
solver = inversion.gradient.levmarq(initial=(0, 0), maxit=1000, tol=10**(-3))
result = seismic.epic2d.homogeneous(ttresiduals, recs, vp, vs, solver)
estimate, residuals = result
predicted = ttresiduals - residuals
shape = (100, 100)
xs, ys = gridder.regular(area, shape)
goals = seismic.epic2d.mapgoal(xs, ys, ttresiduals, recs, vp, vs)
vis.mpl.figure(figsize=(10,4))
vis.mpl.subplot(1, 2, 1)
vis.mpl.title('Epicenter + %d recording stations' % (len(recs)))
vis.mpl.axis('scaled')
vis.mpl.contourf(xs, ys, goals, shape, 50)
"""
GravMag: Use the polynomial equivalent layer to upward continue gravity data
"""
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import PELGravity, PELSmoothness
from fatiando import gridder, utils, mesher
from fatiando.vis import mpl
# Make synthetic data
props = {'density':1000}
model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
shape = (50, 50)
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
gz = utils.contaminate(prism.gz(x, y, z, model), 0.1)
# Setup the layer
layer = mesher.PointGrid([-5000, 5000, -5000, 5000], 200, (100, 100))
# Estimate the density using the PEL (it is faster and more memory efficient
# than the traditional equivalent layer).
windows = (20, 20)
degree = 1
solver = (PELGravity(x, y, z, gz, layer, windows, degree) +
10**-21*PELSmoothness(layer, windows, degree)).fit()
layer.addprop('density', solver.estimate_)
residuals = solver.residuals()
print "Residuals:"
print "mean:", residuals.mean()
print "stddev:", residuals.std()
# Plot the layer and the fit
mpl.figure(figsize=(14, 4))
mpl.subplot(1, 3, 1)
"""
Seismic: Invert vertical seismic profile (VSP) traveltimes using smoothness
regularization
"""
import numpy
from fatiando import utils, seismic, vis
thickness = [10, 20, 10, 30, 40, 60]
velocity = [2000, 1000, 5000, 1000, 2500, 6000]
zp = numpy.arange(1., sum(thickness), 1., dtype='f')
tts, error = utils.contaminate(
seismic.profile.vertical(thickness, velocity, zp),
0.02, percent=True, return_stddev=True)
thick = 10.
mesh = [thick]*int(sum(thickness)/thick)
smooth = 50.
estimates = []
for i in xrange(30):
p, r = seismic.profile.ivertical(utils.contaminate(tts, error), zp, mesh,
smooth=smooth)
estimates.append(1./p)
estimate = utils.vecmean(estimates)
predicted = seismic.profile.vertical(mesh, estimate, zp)
vis.mpl.figure(figsize=(12,5))
vis.mpl.subplot(1, 2, 1)
vis.mpl.grid()
vis.mpl.title("Vertical seismic profile")
vis.mpl.plot(tts, zp, 'ok', label='Observed')
vis.mpl.plot(predicted, zp, '-r', linewidth=3, label='Predicted')
import matplotlib.pyplot as plt
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import EQLGravity
from fatiando.inversion import Damping
from fatiando import gridder, utils, mesher
# First thing to do is make some synthetic data to test the method. We'll use a
# single prism to keep it simple
props = {'density': 500}
model = [mesher.Prism(-5000, 5000, -200, 200, 100, 4000, props)]
# The synthetic data will be generated on a random scatter of points
area = [-8000, 8000, -5000, 5000]
x, y, z = gridder.scatter(area, 300, z=0, seed=42)
# Generate some noisy data from our model
gz = utils.contaminate(prism.gz(x, y, z, model), 0.2, seed=0)
# Now for the equivalent layer. We must setup a layer of point masses where
# we'll estimate a density distribution that fits our synthetic data
layer = mesher.PointGrid(area, 500, (20, 20))
# Estimate the density using enough damping so that won't try to fit the error
eql = EQLGravity(x, y, z, gz, layer) + 1e-22*Damping(layer.size)
eql.fit()
# Now we add the estimated densities to our layer
layer.addprop('density', eql.estimate_)
# and print some statistics of how well the estimated layer fits the data
residuals = eql[0].residuals()
print("Residuals:")
print(" mean:", residuals.mean(), 'mGal')
print(" stddev:", residuals.std(), 'mGal')
"""
GravMag: Use the polynomial equivalent layer to upward continue gravity data
"""
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import PELGravity, PELSmoothness
from fatiando import gridder, utils, mesher
from fatiando.vis import mpl
# Make synthetic data
props = {'density': 1000}
model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
shape = (50, 50)
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
gz = utils.contaminate(prism.gz(x, y, z, model), 0.1, seed=0)
# Setup the layer
layer = mesher.PointGrid([-5000, 5000, -5000, 5000], 200, (100, 100))
# Estimate the density using the PEL (it is faster and more memory efficient
# than the traditional equivalent layer).
windows = (20, 20)
degree = 1
misfit = PELGravity(x, y, z, gz, layer, windows, degree)
# Apply a smoothness constraint to the borders of the equivalent layer windows
# to avoid gaps in the physical property distribution
solver = misfit + 1e-18 * PELSmoothness(layer, windows, degree)
solver.fit()
# Add the estimated density distribution to the layer object for plotting and
# forward modeling
layer.addprop('density', solver.estimate_)
residuals = solver[0].residuals()
print("Residuals:")
print("mean:", residuals.mean())
Geothermal: Forward and inverse modeling of a linear change in temperature
measured in a well
"""
import numpy
from fatiando import utils
from fatiando.geothermal.climsig import linear, SingleChange
from fatiando.vis import mpl
# Generating synthetic data
amp = 5.43
age = 78.2
# along a well at these depths
zp = numpy.arange(0, 100, 1)
temp, error = utils.contaminate(linear(amp, age, zp), 0.02,
percent=True, return_stddev=True)
# Preparing for the inversion
data = SingleChange(temp, zp, mode='linear').config('levmarq', initial=[1, 1])
amp_, age_ = data.fit().estimate_
print "Linear change in temperature"
print " true: amp=%.3f age=%.3f" % (amp, age)
print " estimated: amp=%.3f age=%.3f" % (amp_, age_)
mpl.figure(figsize=(4, 5))
mpl.title("Residual well temperature")
mpl.plot(temp, zp, 'ok', label='Observed')
mpl.plot(data.predicted(), zp, '--r', linewidth=3, label='Predicted')
mpl.legend(loc='lower right', numpoints=1)
mpl.xlabel("Temperature (C)")
from fatiando.seismic.profile import layered_straight_ray, LayeredStraight
from fatiando.inversion.regularization import Damping
from fatiando.vis import mpl
# The limits in velocity and depths, respectively
area = (0, 10000, 0, 100)
vmin, vmax, zmin, zmax = area
# Use the interactive functions of mpl to draw a layered model
figure = mpl.figure()
mpl.xlabel("Velocity (m/s)")
mpl.ylabel("Depth (m)")
thickness, velocity = mpl.draw_layers(area, figure.gca())
# Make some synthetic noise-corrupted travel-time data
zp = numpy.arange(zmin + 0.5, zmax, 0.5)
tts, error = utils.contaminate(layered_straight_ray(thickness, velocity, zp),
0.02,
percent=True,
return_stddev=True)
# Make the solver and run the inversion using damping regularization
# (assumes known thicknesses of the layers)
solver = (LayeredStraight(tts, zp, thickness) +
0.1 * Damping(len(thickness))).fit()
velocity_ = solver.estimate_
# Plot the results
mpl.figure(figsize=(12, 5))
mpl.subplot(1, 2, 1)
mpl.grid()
mpl.title("Vertical seismic profile")
mpl.plot(tts, zp, 'ok', label='Observed')
mpl.plot(solver[0].predicted(), zp, '-r', linewidth=3, label='Predicted')
mpl.legend(loc='upper right', numpoints=1)
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = mesher.SquareMesh(area, shape)
# Fetch the image from the online docs
urllib.urlretrieve("http://fatiando.readthedocs.org/en/latest/_static/logo.png", "logo.png")
model.img2prop("logo.png", 4000, 10000, "vp")
# Make some travel time data and add noise
log.info("Generating synthetic travel-time data")
src_loc = utils.random_points(area, 80)
rec_loc = utils.circular_points(area, 30, random=True)
srcs, recs = utils.connect_points(src_loc, rec_loc)
start = time.time()
tts = seismic.ttime2d.straight(model, "vp", srcs, recs, par=True)
log.info(" time: %s" % (utils.sec2hms(time.time() - start)))
tts, error = utils.contaminate(tts, 0.01, percent=True, return_stddev=True)
# Make the mesh
mesh = mesher.SquareMesh(area, shape)
# and run the inversion
estimate, residuals = seismic.srtomo.run(tts, srcs, recs, mesh, damping=10 ** 9)
# Convert the slowness estimate to velocities and add it the mesh
mesh.addprop("vp", seismic.srtomo.slowness2vel(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
log.info("Assumed error: %g" % (error))
log.info("Standard deviation of residuals: %g" % (numpy.std(residuals)))
vis.mpl.figure(figsize=(14, 5))
vis.mpl.subplot(1, 2, 1)
vis.mpl.axis("scaled")
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)}),
mesher.Prism(x1, x2, y1m[35], y2m[35], zo_t[35], zo_b[35],
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)}),
mesher.Prism(x1, x2, y1m[36], y2m[36], zo_t[36], zo_b[36],
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)}),
mesher.Prism(x1, x2, y1m[37], y2m[37], zo_t[37], zo_b[37],
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)}),
mesher.Prism(x1, x2, y1m[38], y2m[38], zo_t[38], zo_b[38],
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)}),
mesher.Prism(x1, x2, y1m[39], y2m[39], zo_t[39], zo_b[39],
{'magnetization': utils.ang2vec(mag_m, inc_s, dec_s)})
]
#total field from Fatiando a Terra
tf, stdv = utils.contaminate(prism.tf(xi, yi, zi, model_mag, inc_o, dec_o),
1,
percent=False,
return_stddev=True)
print stdv
#save for the plot
out = np.array([yi, xi, zi, tf])
out = out.T
np.savetxt('input_mag.dat', out, delimiter=' ', fmt='%1.8f')
out = None
tf_noise_free = prism.tf(xi, yi, zi, model_mag, inc_o, dec_o)
out = np.array([yi, xi, zi, tf_noise_free])
out = out.T
np.savetxt('mag_noise_free.dat', out, delimiter=' ', fmt='%1.8f')
out = None
x1, x2 = 0, 3000
y1, y2 = 0, 3000
z1, z2 = 0, 3000
extent = [x1, x2, y1, y2, -z2, -z1]
# Now calculate all the components of the gradient tensor and contaminate the
# data with gaussian noise
error = 0.2
fields = ['gzz', 'gyz']
data = {}
for i, field in enumerate(fields):
data[field] = synthetic.from_prisms(model, x1=0, x2=3000, y1=0, y2=3000,
nx=50, ny=50, height=150, field=field)
data[field]['value'] = utils.contaminate(data[field]['value'],
stddev=error,
percent=False)
data[field]['error'] = error*numpy.ones(len(data[field]['value']))
# PERFORM THE INVERSION
################################################################################
#~ # Generate a prism mesh
mesh = fatiando.mesh.prism_mesh(x1=x1, x2=x2, y1=y1, y2=y2, z1=z1, z2=z2,
nx=30, ny=30, nz=30)
# Set the seeds and save them for later use
log.info("Setting seeds in mesh:")
seeds = []
seeds.append(gplant.get_seed((1501, 1501, 1501), 1000, mesh))
# Make a mesh for the seeds to plot them
"""
GravMag: Fit an equivalent layer to gravity and gravity gradient data
"""
import numpy as np
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import EQLGravity
from fatiando.inversion.regularization import Smoothness2D, LCurve
from fatiando import gridder, utils, mesher
from fatiando.vis import mpl
# Make synthetic data
props = {'density': 1000}
model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
area = [-5000, 5000, -5000, 5000]
x1, y1, z1 = gridder.scatter(area, 80, z=0, seed=0)
gz = utils.contaminate(prism.gz(x1, y1, z1, model), 0.1, seed=0)
x2, y2, z2 = gridder.regular(area, (10, 50), z=-200)
gzz = utils.contaminate(prism.gzz(x2, y2, z2, model), 5, seed=0)
# Setup the layer
layer = mesher.PointGrid([-6000, 6000, -6000, 6000], 500, (50, 50))
# and the inversion
# Apply a scaling factor to make both portions of the misfit the same order of
# magnitude
scale = np.linalg.norm(gz)**2 / np.linalg.norm(gzz)**2
misfit = (EQLGravity(x1, y1, z1, gz, layer) +
scale * EQLGravity(x2, y2, z2, gzz, layer, field='gzz'))
regul = Smoothness2D(layer.shape)
# Use an L-curve analysis to find the best regularization parameter
solver = LCurve(misfit, regul, [10**i for i in range(-30, -20)]).fit()
layer.addprop('density', solver.estimate_)
vel[5:25, 5:25] = 10000
model.addprop('vp', vel.ravel())
# Make some noisy travel time data using straight-rays
# Set the random seed so that points are the same every time we run this script
seed = 0
src_loc_x, src_loc_y = gridder.scatter(area, 80, seed=seed)
src_loc = np.transpose([src_loc_x, src_loc_y])
rec_loc_x, rec_loc_y = gridder.circular_scatter(area, 30,
random=True, seed=seed)
rec_loc = np.transpose([rec_loc_x, rec_loc_y])
srcs = [src for src in src_loc for _ in rec_loc]
recs = [rec for _ in src_loc for rec in rec_loc]
tts = ttime2d.straight(model, 'vp', srcs, recs)
# Use 2% random noise to corrupt the data
tts = utils.contaminate(tts, 0.02, percent=True, seed=seed)
# Make a mesh for the inversion. The inversion will estimate the velocity in
# each square of the mesh. To make things simpler, we'll use a mesh that is the
# same as our original model.
mesh = SquareMesh(area, shape)
# Create solvers for each type of regularization and fit the synthetic data to
# obtain an estimated velocity model
solver = srtomo.SRTomo(tts, srcs, recs, mesh)
smooth = solver + 1e8*Smoothness2D(mesh.shape)
smooth.fit()
damped = solver + 1e8*Damping(mesh.size)
damped.fit()
ax = vis.mpl.subplot(1, 1, 1)
vis.mpl.axis('scaled')
vis.mpl.suptitle("Choose the location of the source")
vis.mpl.points(rec_points, '^r')
src = vis.mpl.pick_points(area, ax, marker='*', color='y')
if len(src) > 1:
log.error("Don't be greedy! Pick only one point as the source")
sys.exit()
log.info("Generating synthetic travel-time data")
srcs, recs = utils.connect_points(src, rec_points)
ptime = seismic.ttime2d.straight(model, 'vp', srcs, recs)
stime = seismic.ttime2d.straight(model, 'vs', srcs, recs)
error_level = 0.1
ttr_true = stime - ptime
ttr, error = utils.contaminate(ttr_true, error_level, percent=True,
return_stddev=True)
log.info("Choose the initial estimate for the gradient solvers")
vis.mpl.figure()
ax = vis.mpl.subplot(1, 1, 1)
vis.mpl.axis('scaled')
vis.mpl.suptitle("Choose the initial estimate for the gradient solvers")
vis.mpl.points(rec_points, '^r')
vis.mpl.points(src, '*y')
initial = vis.mpl.pick_points(area, ax, marker='*', color='k')
if len(initial) > 1:
log.error("Don't be greedy! Pick only one initial estimate")
sys.exit()
initial = initial[0]
log.info("Will solve the inverse problem using Newton's method")
from fatiando.mesher import PolygonalPrism, PrismMesh, vremove
from fatiando.vis import mpl, myv
# Create a synthetic model
bounds = [-10000, 10000, -10000, 10000, 0, 10000]
vertices = [[-4948.97959184, -6714.64019851], [-2448.97959184, -3141.43920596],
[2448.97959184, 312.65508685], [6938.7755102, 5394.54094293],
[4846.93877551, 6228.28784119], [2653.06122449, 3409.4292804],
[-3520.40816327, -1434.24317618], [-6632.65306122, -6079.4044665]]
model = [PolygonalPrism(vertices, 1000, 4000, {'density': 1000})]
# and generate synthetic data from it
shape = (20, 20)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
noise = 0.1 # 0.1 mGal noise
gz = utils.contaminate(gm.polyprism.gz(xp, yp, zp, model), noise)
# Create a mesh
mesh = PrismMesh(bounds, (25, 50, 50))
# Wrap the data so that harvester can read it
data = [gm.harvester.Gz(xp, yp, zp, gz)]
# Plot the data and pick the location of the seeds
mpl.figure()
mpl.suptitle("Pick the seeds (polygon is the true source)")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.polygon(model[0], xy2ne=True)
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
seedx, seedy = mpl.pick_points(area, mpl.gca(), xy2ne=True).T
from fatiando import utils
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = SquareMesh(area, shape)
vel = 4000 * np.ones(shape)
vel[5:25, 5:25] = 10000
model.addprop('vp', vel.ravel())
# Make some travel time data and add noise
seed = 0 # Set the random seed so that points are the same every time
src_loc = utils.random_points(area, 80, seed=seed)
rec_loc = utils.circular_points(area, 30, random=True, seed=seed)
srcs, recs = utils.connect_points(src_loc, rec_loc)
tts = ttime2d.straight(model, 'vp', srcs, recs)
tts, error = utils.contaminate(tts, 0.02, percent=True, return_stddev=True,
seed=seed)
# Make the mesh
mesh = SquareMesh(area, shape)
# and run the inversion
misfit = srtomo.SRTomo(tts, srcs, recs, mesh)
regularization = Smoothness2D(mesh.shape)
# Will use the l-curve criterion to find the best regularization parameter
tomo = LCurve(misfit, regularization,
[10 ** i for i in np.arange(0, 10, 1)], jobs=8).fit()
mesh.addprop('vp', tomo.estimate_)
# Plot the L-curve annd print the regularization parameter estimated
mpl.figure()
mpl.title('L-curve: triangle marks the best solution')
tomo.plot_lcurve()
print "Estimated regularization parameter: %g" % (tomo.regul_param_)
import numpy
from fatiando import mesher, seismic, utils, gridder, vis, inversion
area = (0, 10, 0, 10)
vp, vs = 2, 1
model = [mesher.Square(area, props={'vp': vp, 'vs': vs})]
src = (8, 7)
stations = 10
srcs, recs = utils.connect_points([src], [(4, 6), (5, 5.9), (6, 6)])
ptime = seismic.ttime2d.straight(model, 'vp', srcs, recs)
stime = seismic.ttime2d.straight(model, 'vs', srcs, recs)
error_level = 0.05
ttr_true = stime - ptime
ttr, error = utils.contaminate(ttr_true,
error_level,
percent=True,
return_stddev=True)
vis.mpl.figure()
ax = vis.mpl.subplot(1, 1, 1)
vis.mpl.axis('scaled')
vis.mpl.suptitle("Choose the initial estimate for the gradient solvers")
vis.mpl.points(recs, '^r')
vis.mpl.points(srcs, '*y')
initial = vis.mpl.pick_points(area, ax, marker='*', color='k')
if len(initial) > 1:
print "Don't be greedy! Pick only one initial estimate"
sys.exit()
initial = initial[0]
ref = {'y': 7}
from fatiando import gravmag as gm
from fatiando.mesher import Prism, PrismMesh, vremove
from fatiando.vis import mpl, myv
# Create a synthetic model
props = {'density':1000}
model = [Prism(400, 600, 300, 500, 200, 400, props),
Prism(400, 600, 400, 600, 400, 600, props),
Prism(400, 600, 500, 700, 600, 800, props)]
# and generate synthetic data from it
shape = (51, 51)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-150)
noise = 0.5
gxx = utils.contaminate(gm.prism.gxx(xp, yp, zp, model), noise)
gxy = utils.contaminate(gm.prism.gxy(xp, yp, zp, model), noise)
gxz = utils.contaminate(gm.prism.gxz(xp, yp, zp, model), noise)
gyy = utils.contaminate(gm.prism.gyy(xp, yp, zp, model), noise)
gyz = utils.contaminate(gm.prism.gyz(xp, yp, zp, model), noise)
gzz = utils.contaminate(gm.prism.gzz(xp, yp, zp, model), noise)
tensor = [gxx, gxy, gxz, gyy, gyz, gzz]
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
# plot the data
mpl.figure()
for i in xrange(len(tensor)):
mpl.subplot(2, 3, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tensor[i], shape, 30)
mpl.colorbar()
import numpy
from fatiando import potential, logger, gridder, utils
from fatiando.mesher.volume import Prism3D
log = logger.tofile('datagen-%s.log' % (sys.argv[1].split('.')[0]))
log.info(logger.header())
modelfile = __import__(sys.argv[1].split('.')[0])
model = modelfile.model
shape = (51, 51)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
noise = 0.5
x, y, z = gridder.regular(area, shape, z=-150)
tensor = (potential.prism.gxx(x, y, z,
model), potential.prism.gxy(x, y, z, model),
potential.prism.gxz(x, y, z,
model), potential.prism.gyy(x, y, z, model),
potential.prism.gyz(x, y, z,
model), potential.prism.gzz(x, y, z, model))
tensor_noisy = [utils.contaminate(d, noise) for d in tensor]
data = [x, y, z]
data.extend(tensor_noisy)
with open(modelfile.datafile, 'w') as f:
f.write("# Noise corrupted tensor components:\n")
f.write("# noise = %g Eotvos\n" % (noise))
f.write("# x y z gxx gxy gxz gyy gyz gzz\n")
numpy.savetxt(f, numpy.array(data).T)
"""
GravMag: Center of mass estimation using the first eigenvector of the gravity
gradient tensor (elongated model)
"""
from fatiando.vis import mpl, myv
from fatiando import mesher, gridder, utils, gravmag
# Generate some synthetic data
prisms = [mesher.Prism(-4000, 4000, -500, 500, 500, 1000, {'density': 1000})]
shape = (100, 100)
xp, yp, zp = gridder.regular((-5000, 5000, -5000, 5000), shape, z=-150)
noise = 2
tensor = [
utils.contaminate(gravmag.prism.gxx(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gxy(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gxz(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gyy(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gyz(xp, yp, zp, prisms), noise),
utils.contaminate(gravmag.prism.gzz(xp, yp, zp, prisms), noise)
]
# Plot the data
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
for i, title in enumerate(titles):
mpl.subplot(3, 2, i + 1)
mpl.title(title)
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tensor[i], shape, 10)
mpl.contour(yp, xp, tensor[i], shape, levels)
mpl.m2km()
mpl.show()
GravMag: Use an equivalent layer to reduce a magnetic total field anomaly to
the pole
"""
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import EQLTotalField
from fatiando.inversion.regularization import Damping, LCurve
from fatiando import gridder, utils, mesher
from fatiando.vis import mpl
# Make synthetic data
inc, dec = -60, 23
props = {'magnetization': 10}
model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
shape = (25, 25)
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
tf = utils.contaminate(prism.tf(x, y, z, model, inc, dec), 5, seed=0)
# Setup the layer
layer = mesher.PointGrid([-7000, 7000, -7000, 7000], 700, (50, 50))
# Estimate the magnetization intensity
# Need to apply regularization so that won't try to fit the error as well
misfit = EQLTotalField(x, y, z, tf, inc, dec, layer)
regul = Damping(layer.size)
# Use an L-curve analysis to find the best regularization parameter
solver = LCurve(misfit, regul, [10 ** i for i in range(-30, -15)]).fit()
residuals = solver.residuals()
layer.addprop('magnetization', solver.estimate_)
print "Residuals:"
print "mean:", residuals.mean()
print "stddev:", residuals.std()
# Now I can forward model the layer at the south pole and check against the
from fatiando import mesher, gridder
from fatiando.utils import ang2vec, vec2ang, contaminate
from fatiando.gravmag import sphere
from fatiando.vis import mpl
from fatiando.gravmag.magdir import DipoleMagDir
from fatiando.constants import CM
# Make noise-corrupted synthetic data
inc, dec = -10.0, -15.0 # inclination and declination of the Geomagnetic Field
model = [mesher.Sphere(3000, 3000, 1000, 1000,
{'magnetization': ang2vec(6.0, -20.0, -10.0)}),
mesher.Sphere(7000, 7000, 1000, 1000,
{'magnetization': ang2vec(10.0, 3.0, -67.0)})]
area = (0, 10000, 0, 10000)
x, y, z = gridder.scatter(area, 1000, z=-150, seed=0)
tf = contaminate(sphere.tf(x, y, z, model, inc, dec), 5.0, seed=0)
# Give the centers of the dipoles
centers = [[3000, 3000, 1000], [7000, 7000, 1000]]
# Estimate the magnetization vectors
solver = DipoleMagDir(x, y, z, tf, inc, dec, centers).fit()
# Print the estimated and true dipole monents, inclinations and declinations
print 'Estimated magnetization (intensity, inclination, declination)'
for e in solver.estimate_:
print e
# Plot the fit and the normalized histogram of the residuals
mpl.figure(figsize=(14, 5))
mpl.subplot(1, 2, 1)
vis.mpl.figure()
ax = vis.mpl.subplot(1, 1, 1)
vis.mpl.axis('scaled')
vis.mpl.suptitle("Choose the location of the source")
vis.mpl.points(rec_points, '^r')
src = vis.mpl.pick_points(area, ax, marker='*', color='y')
if len(src) > 1:
print "Don't be greedy! Pick only one point as the source"
sys.exit()
srcs, recs = utils.connect_points(src, rec_points)
ptime = seismic.ttime2d.straight(model, 'vp', srcs, recs)
stime = seismic.ttime2d.straight(model, 'vs', srcs, recs)
ttresiduals, error = utils.contaminate(stime - ptime,
0.10,
percent=True,
return_stddev=True)
solver = inversion.gradient.levmarq(initial=(0, 0), maxit=1000, tol=10**(-3))
result = seismic.epic2d.homogeneous(ttresiduals, recs, vp, vs, solver)
estimate, residuals = result
predicted = ttresiduals - residuals
shape = (100, 100)
xs, ys = gridder.regular(area, shape)
goals = seismic.epic2d.mapgoal(xs, ys, ttresiduals, recs, vp, vs)
vis.mpl.figure(figsize=(10, 4))
vis.mpl.subplot(1, 2, 1)
vis.mpl.title('Epicenter + %d recording stations' % (len(recs)))
vis.mpl.axis('scaled')
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')
mpl.suptitle("Choose the initial estimate")
mpl.points(rec_points, '^r')
mpl.points(src, '*y')
initial = mpl.pick_points(area, mpl.gca(), marker='*', color='b')
if len(initial) > 1:
print "Don't be greedy! Pick only one point"
sys.exit()
estimate = solver.config('levmarq', initial=initial[0]).fit().estimate_
mpl.figure(figsize=(10,4))
mpl.subplot(1, 2, 1)
from fatiando.vis import mpl, myv
# Create a synthetic model
props = {'density': 1000}
model = [
Prism(400, 600, 300, 500, 200, 400, props),
Prism(400, 600, 400, 600, 400, 600, props),
Prism(400, 600, 500, 700, 600, 800, props)
]
# and generate synthetic data from it
shape = (51, 51)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-150)
noise = 0.5
gxx = utils.contaminate(gm.prism.gxx(xp, yp, zp, model), noise)
gxy = utils.contaminate(gm.prism.gxy(xp, yp, zp, model), noise)
gxz = utils.contaminate(gm.prism.gxz(xp, yp, zp, model), noise)
gyy = utils.contaminate(gm.prism.gyy(xp, yp, zp, model), noise)
gyz = utils.contaminate(gm.prism.gyz(xp, yp, zp, model), noise)
gzz = utils.contaminate(gm.prism.gzz(xp, yp, zp, model), noise)
tensor = [gxx, gxy, gxz, gyy, gyz, gzz]
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
# plot the data
mpl.figure()
for i in xrange(len(tensor)):
mpl.subplot(2, 3, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tensor[i], shape, 30)
mpl.colorbar()
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = SquareMesh(area, shape)
vel = 4000 * np.ones(shape)
vel[5:25, 5:25] = 10000
model.addprop('vp', vel.ravel())
# Make some travel time data and add noise
seed = 0 # Set the random seed so that points are the same every time
src_loc = utils.random_points(area, 80, seed=seed)
rec_loc = utils.circular_points(area, 30, random=True, seed=seed)
srcs, recs = utils.connect_points(src_loc, rec_loc)
tts = ttime2d.straight(model, 'vp', srcs, recs)
tts, error = utils.contaminate(tts,
0.02,
percent=True,
return_stddev=True,
seed=seed)
# Make the mesh
mesh = SquareMesh(area, shape)
# and run the inversion
misfit = srtomo.SRTomo(tts, srcs, recs, mesh)
regularization = Smoothness2D(mesh.shape)
# Will use the l-curve criterion to find the best regularization parameter
tomo = LCurve(misfit,
regularization, [10**i for i in np.arange(0, 10, 1)],
jobs=8).fit()
mesh.addprop('vp', tomo.estimate_)
# Plot the L-curve annd print the regularization parameter estimated
mpl.figure()
myv.wall_bottom(bounds)
myv.wall_north(bounds)
myv.show()
# Generate the data grid
shape = (25, 25)
area = bounds[0:4]
x, y = gridder.regular(area, shape)
# Generate synthetic topography
height = (300*utils.gaussian2d(x, y, 1000, 3000, x0=500, y0=1000, angle=-60)
+ 1000*utils.gaussian2d(x, y, 500, 2000, x0=3000, y0=3000))
# Calculate the data
noise = 1
noisegz = 0.1
z = -height - 150
data = [x, y, z, height,
utils.contaminate(gravmag.prism.gz(x, y, z, model), noisegz),
utils.contaminate(gravmag.prism.gxx(x, y, z, model), noise),
utils.contaminate(gravmag.prism.gxy(x, y, z, model), noise),
utils.contaminate(gravmag.prism.gxz(x, y, z, model), noise),
utils.contaminate(gravmag.prism.gyy(x, y, z, model), noise),
utils.contaminate(gravmag.prism.gyz(x, y, z, model), noise),
utils.contaminate(gravmag.prism.gzz(x, y, z, model), noise)]
with open('data.txt', 'w') as f:
f.write(logger.header(comment='#'))
f.write("# Noise corrupted gz and tensor components:\n")
f.write("# noise = %g Eotvos\n" % (noise))
f.write("# noise = %g mGal\n" % (noisegz))
f.write("# coordinates are in meters\n")
f.write("# gz in mGal and tensor in Eotvos\n")
f.write("# x y z height gz gxx gxy gxz gyy gyz gzz\n")
numpy.savetxt(f, numpy.transpose(data))