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
# true solution of the prism
tfpole = prism.tf(x, y, z, model, -90, 0)
tfreduced = sphere.tf(x, y, z, layer, -90, 0)
mpl.figure()
mpl.suptitle('L-curve')
mpl.title("Estimated regularization parameter: %g" % (solver.regul_param_))
solver.plot_lcurve()
mpl.grid()
mpl.figure(figsize=(14, 4))
mpl.subplot(1, 3, 1)
mpl.axis('scaled')
mpl.title('Layer (A/m)')
mpl.pcolor(layer.y, layer.x, layer.props['magnetization'], layer.shape)
mpl.colorbar()
mpl.m2km()
mpl.subplot(1, 3, 2)
mpl.axis('scaled')
mpl.title('Fit (nT)')
levels = mpl.contour(y, x, tf, shape, 15, color='r')
"""
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()
# 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()
# Pick the centers of the expanding windows
# The number of final solutions will be the number of points picked
mpl.figure()
mpl.suptitle('Pick the centers of the expanding windows')
mpl.axis('scaled')
mpl.contourf(yp, xp, tensor[-1], shape, 50)
mpl.colorbar()
centers = mpl.pick_points(area, mpl.gca(), xy2ne=True)
cms = []
for center in centers:
# Use the first eigenvector to estimate the center of mass
cm, sigma = gravmag.tensor.center_of_mass(xp, yp, zp, eigenvecs[0],
windows=100, wcenter=center)
cms.append(cm)
print "Sigma = %g" % (sigma)
# Plot the prism and the estimated center of mass
# It won't work well because we're using only a single window
myv.figure()
import sys
import numpy
from fatiando import gridder, utils
from fatiando.mesher import Square
from fatiando.vis import mpl
from fatiando.seismic import ttime2d
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
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
data = [
polyprism.gxx(xp, yp, zp, model),
polyprism.gxy(xp, yp, zp, model),
polyprism.gxz(xp, yp, zp, model),
polyprism.gyy(xp, yp, zp, model),
polyprism.gyz(xp, yp, zp, model),
polyprism.gzz(xp, yp, zp, model)
]
# and plot it
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Gravity tensor produced by prism model (Eotvos)")
for i in xrange(len(data)):
mpl.subplot(3, 2, i + 1)
mpl.title(titles[i])
mpl.contourf(yp, xp, data[i], shape, 20)
mpl.colorbar()
for p in model:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
mpl.m2km()
mpl.show()
# Show the prisms
myv.figure()
myv.polyprisms(model, 'density')
myv.axes(myv.outline(bounds), ranges=[i * 0.001 for i in bounds])
myv.wall_north(bounds)
# 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()
# Pick the centers of the expanding windows
# The number of final solutions will be the number of points picked
mpl.figure()
mpl.suptitle('Pick the centers of the expanding windows')
mpl.axis('scaled')
mpl.contourf(yp, xp, tensor[-1], shape, 50)
mpl.colorbar()
centers = mpl.pick_points(area, mpl.gca(), xy2ne=True)
cms = []
for center in centers:
# Use the first eigenvector to estimate the center of mass
cm, sigma = gravmag.tensor.center_of_mass(xp,
yp,
zp,
eigenvecs[0],
windows=100,
wcenter=center)
cms.append(cm)
print "Sigma = %g" % (sigma)
xp, yp, zp = gridder.regular((-5000, 5000, -5000, 5000), shape, z=-150)
fields = [prism.potential(xp, yp, zp, model),
prism.gx(xp, yp, zp, model),
prism.gy(xp, yp, zp, model),
prism.gz(xp, yp, zp, model),
prism.gxx(xp, yp, zp, model),
prism.gxy(xp, yp, zp, model),
prism.gxz(xp, yp, zp, model),
prism.gyy(xp, yp, zp, model),
prism.gyz(xp, yp, zp, model),
prism.gzz(xp, yp, zp, model)]
titles = ['potential', 'gx', 'gy', 'gz',
'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure(figsize=(8, 9))
mpl.subplots_adjust(left=0.03, right=0.95, bottom=0.05, top=0.92, hspace=0.3)
mpl.suptitle("Potential fields produced by a 3 prism model")
for i, field in enumerate(fields):
mpl.subplot(4, 3, i + 3)
mpl.axis('scaled')
mpl.title(titles[i])
levels = mpl.contourf(yp*0.001, xp*0.001, field, shape, 15)
cb = mpl.colorbar()
mpl.contour(yp*0.001, xp*0.001, field, shape, levels, clabel=False, linewidth=0.1)
mpl.show()
myv.figure()
myv.prisms(model, prop='density')
axes = myv.axes(myv.outline())
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
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_)
# Now I can forward model gz using my layer to produce an integrated map in a
# much denser region
shape = (50, 50)
x, y, z = gridder.regular(area, shape, z=0)
gz_layer = sphere.gz(x, y, z, layer)
gz_true = prism.gz(x, y, z, model)
mpl.figure()
mpl.suptitle('L-curve')
mpl.title("Estimated regularization parameter: %g" % (solver.regul_param_))
solver.plot_lcurve()
mpl.grid()
# Plot the layer and the fit
mpl.figure(figsize=(14, 4))
mpl.suptitle('Observed data (black) | Predicted by layer (red)')
mpl.subplot(1, 3, 1)
mpl.axis('scaled')
mpl.title('Layer (kg.m^-3)')
mpl.pcolor(layer.y, layer.x, layer.props['density'], layer.shape)
mpl.colorbar().set_label(r'Density $kg.m^{-3}$')
mpl.m2km()
mpl.subplot(1, 3, 2)
mpl.axis('scaled')
shape = (100, 100)
xp, yp, zp = gridder.regular(dataarea, shape, z=-500)
data = [
polyprism.gxx(xp, yp, zp, model),
polyprism.gxy(xp, yp, zp, model),
polyprism.gxz(xp, yp, zp, model),
polyprism.gyy(xp, yp, zp, model),
polyprism.gyz(xp, yp, zp, model),
polyprism.gzz(xp, yp, zp, model)]
# Calculate the 3 invariants
invariants = tensor.invariants(data)
data = data + invariants
# and plot it
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Tensor and invariants produced by prism model (Eotvos)")
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz', 'I1', 'I2', 'I']
for i in xrange(len(data)):
mpl.subplot(3, 3, i + 1)
mpl.title(titles[i])
levels = 20
if i == 8:
levels = numpy.linspace(0, 1, levels)
mpl.contourf(yp, xp, data[i], shape, levels)
mpl.colorbar()
for p in model:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(dataarea)
mpl.m2km()
mpl.show()
xp, yp, zp = gridder.regular(dataarea, shape, z=-500)
tensor = [
gravmag.polyprism.gxx(xp, yp, zp, prisms),
gravmag.polyprism.gxy(xp, yp, zp, prisms),
gravmag.polyprism.gxz(xp, yp, zp, prisms),
gravmag.polyprism.gyy(xp, yp, zp, prisms),
gravmag.polyprism.gyz(xp, yp, zp, prisms),
gravmag.polyprism.gzz(xp, yp, zp, prisms)
]
# Calculate the 3 invariants
invariants = gravmag.tensor.invariants(tensor)
data = tensor + invariants
# and plot it
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Tensor and invariants produced by prism model (Eotvos)")
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz', 'I1', 'I2', 'I']
for i in xrange(len(data)):
mpl.subplot(3, 3, i + 1)
mpl.title(titles[i])
levels = 20
if i == 8:
levels = numpy.linspace(0, 1, levels)
mpl.contourf(yp, xp, data[i], shape, levels)
mpl.colorbar()
for p in prisms:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(dataarea)
mpl.m2km()
mpl.show()
gravmag.prism.gx(xp, yp, zp, prisms),
gravmag.prism.gy(xp, yp, zp, prisms),
gravmag.prism.gz(xp, yp, zp, prisms),
gravmag.prism.gxx(xp, yp, zp, prisms),
gravmag.prism.gxy(xp, yp, zp, prisms),
gravmag.prism.gxz(xp, yp, zp, prisms),
gravmag.prism.gyy(xp, yp, zp, prisms),
gravmag.prism.gyz(xp, yp, zp, prisms),
gravmag.prism.gzz(xp, yp, zp, prisms)
]
titles = [
'potential', 'gx', 'gy', 'gz', 'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz'
]
mpl.figure(figsize=(8, 9))
mpl.subplots_adjust(left=0.03, right=0.95, bottom=0.05, top=0.92, hspace=0.3)
mpl.suptitle("Potential fields produced by a 3 prism model")
for i, field in enumerate(fields):
mpl.subplot(4, 3, i + 3)
mpl.axis('scaled')
mpl.title(titles[i])
levels = mpl.contourf(yp * 0.001, xp * 0.001, field, shape, 15)
cb = mpl.colorbar()
mpl.contour(yp * 0.001,
xp * 0.001,
field,
shape,
levels,
clabel=False,
linewidth=0.1)
mpl.show()
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_)
# Now I can forward model gz using my layer to produce an integrated map in a
# much denser region
shape = (50, 50)
x, y, z = gridder.regular(area, shape, z=0)
gz_layer = sphere.gz(x, y, z, layer)
gz_true = prism.gz(x, y, z, model)
mpl.figure()
mpl.suptitle('L-curve')
mpl.title("Estimated regularization parameter: %g" % (solver.regul_param_))
solver.plot_lcurve()
mpl.grid()
# Plot the layer and the fit
mpl.figure(figsize=(14, 4))
mpl.suptitle('Observed data (black) | Predicted by layer (red)')
mpl.subplot(1, 3, 1)
mpl.axis('scaled')
mpl.title('Layer (kg.m^-3)')
mpl.pcolor(layer.y, layer.x, layer.props['density'], layer.shape)
mpl.colorbar().set_label(r'Density $kg.m^{-3}$')
mpl.m2km()
mpl.subplot(1, 3, 2)
mpl.axis('scaled')
# Pad with the odd reflection and a cosine taper (default)
g, _ = gridder.pad_array(gz, padtype='OddReflectionTaper')
pads.append(g.flatten())
# Get coordinate vectors
N = gridder.pad_coords(xy, gz.shape, nps)
shapepad = g.shape
# Generate new meshgrid and plot results
yp = N[1]
xp = N[0]
titles = ['Original', 'Zero', 'Mean', 'Edge', 'Linear Taper', 'Reflection',
'Odd Reflection', 'Odd Reflection/Taper']
mpl.figure(figsize=(17, 9))
mpl.suptitle('Padding algorithms for a 2D array')
for ii, p in enumerate(pads):
mpl.subplot(2, 4, ii+2)
mpl.axis('scaled')
mpl.title(titles[ii+1])
levels = mpl.contourf(yp*0.001, xp*0.001, p, shapepad, 15)
cb = mpl.colorbar()
mpl.contour(yp*0.001, xp*0.001, p, shapepad, levels, clabel=False,
linewidth=0.1)
mpl.subplot(2, 4, 1)
mpl.axis('scaled')
mpl.title(titles[0])
levels = mpl.contourf(y*0.001, x*0.001, gz, shape, 15)
cb = mpl.colorbar()
mpl.contour(y*0.001, x*0.001, gz, shape, levels, clabel=False, linewidth=0.1)
mpl.show()
(2951, 3951, 301, {'density':800}),
(2951, 3951, 701, {'density':800})],
mesh)
# Run the inversion and collect the results
estimate, predicted = gm.harvester.harvest(data, seeds, mesh,
compactness=1, threshold=0.0001)
# Insert the estimated density values into the mesh
mesh.addprop('density', estimate['density'])
# and get only the prisms corresponding to our estimate
density_model = vremove(0, 'density', mesh)
# Plot the results
tensor = (gxy, gzz)
titles = ('gxy', 'gzz')
mpl.figure()
mpl.suptitle("True: color | Inversion: contour")
for i in xrange(len(tensor)):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
levels = mpl.contourf(y*0.001, x*0.001, tensor[i], shape, 12)
mpl.colorbar()
mpl.contour(y*0.001, x*0.001, predicted[i], shape, levels, color='k')
for i in xrange(len(tensor)):
mpl.subplot(2, 2, i + 3)
residuals = tensor[i] - predicted[i]
mpl.title('residuals stddev = %.2f' % (residuals.std()))
mpl.hist(residuals, bins=10)
mpl.xlabel('Residual (Eotvos)')
mpl.show()
myv.figure()
'density': 1000
}], [500, 550, 510, {
'density': 1000
}]], mesh)
# Run the inversioin
estimate, predicted = gm.harvester.harvest(data,
seeds,
mesh,
compactness=0.5,
threshold=0.001)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment
mpl.figure()
mpl.suptitle("True: color | Inversion: contour")
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, 12)
mpl.colorbar()
mpl.contour(yp, xp, predicted[i], shape, levels, color='k')
mpl.xlabel('y (km)')
mpl.ylabel('x (km)')
mpl.m2km()
mpl.figure()
mpl.suptitle("Residuals")
for i in xrange(len(tensor)):
residuals = tensor[i] - predicted[i]
mpl.subplot(2, 3, i + 1)
0, 1000, {'density':500})]
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
tensor = [
gravmag.polyprism.gxx(xp, yp, zp, prisms),
gravmag.polyprism.gxy(xp, yp, zp, prisms),
gravmag.polyprism.gxz(xp, yp, zp, prisms),
gravmag.polyprism.gyy(xp, yp, zp, prisms),
gravmag.polyprism.gyz(xp, yp, zp, prisms),
gravmag.polyprism.gzz(xp, yp, zp, prisms)]
# and plot it
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Gravity tensor produced by prism model (Eotvos)")
for i in xrange(len(tensor)):
mpl.subplot(3, 2, i + 1)
mpl.title(titles[i])
mpl.contourf(yp, xp, tensor[i], shape, 20)
mpl.colorbar()
for p in prisms:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
mpl.m2km()
mpl.show()
# Show the prisms
myv.figure()
myv.polyprisms(prisms, 'density')
myv.axes(myv.outline(bounds), ranges=[i*0.001 for i in bounds])
myv.wall_north(bounds)
[-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(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 = [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
# Set the right density and depth
locations = [[x, y, 1500, {'density': 1000}] for x, y in zip(seedx, seedy)]
mpl.show()
# Make the seed and set the compactness regularizing parameter mu
seeds = harvester.sow(locations, mesh)
# Run the inversion
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=0.05, threshold=0.0005)
from fatiando.vis import mpl
area = [-5000, 5000, -5000, 5000]
model = [Prism(-3000, 3000, -1000, 1000, 0, 1000, {'density': 1000})]
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
data = [prism.gxx(xp, yp, zp, model),
prism.gxy(xp, yp, zp, model),
prism.gxz(xp, yp, zp, model),
prism.gyy(xp, yp, zp, model),
prism.gyz(xp, yp, zp, model),
prism.gzz(xp, yp, zp, model)]
# Calculate the 3 invariants
invariants = tensor.invariants(data)
data = data + invariants
# and plot it
mpl.figure()
mpl.axis('scaled')
mpl.suptitle("Tensor and invariants (Eotvos)")
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz', 'I1', 'I2', 'I']
for i in xrange(len(data)):
mpl.subplot(3, 3, i + 1)
mpl.title(titles[i])
levels = 20
if i == 8:
levels = numpy.linspace(0, 1, levels)
mpl.contourf(yp, xp, data[i], shape, levels, cmap=mpl.cm.RdBu_r)
mpl.colorbar()
mpl.m2km()
mpl.show()
gm.harvester.Gyy(xp, yp, zp, gyy),
gm.harvester.Gyz(xp, yp, zp, gyz),
gm.harvester.Gzz(xp, yp, zp, gzz)]
# Make the seeds
seeds = gm.harvester.sow([
[500, 400, 210, {'density':1000}],
[500, 550, 510, {'density':1000}]], mesh)
# Run the inversioin
estimate, predicted = gm.harvester.harvest(data, seeds, mesh,
compactness=0.5, threshold=0.001)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment
mpl.figure()
mpl.suptitle("True: color | Inversion: contour")
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, 12)
mpl.colorbar()
mpl.contour(yp, xp, predicted[i], shape, levels, color='k')
mpl.xlabel('y (km)')
mpl.ylabel('x (km)')
mpl.m2km()
mpl.figure()
mpl.suptitle("Residuals")
for i in xrange(len(tensor)):
residuals = tensor[i] - predicted[i]
mpl.subplot(2, 3, i + 1)
[-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
# Set the right density and depth
locations = [[x, y, 1500, {'density': 1000}] for x, y in zip(seedx, seedy)]
mpl.show()
# Make the seed and set the compactness regularizing parameter mu
seeds = gm.harvester.sow(locations, mesh)
# Run the inversion
estimate, predicted = gm.harvester.harvest(data,
seeds,
import sys
import numpy
from fatiando import gridder, utils
from fatiando.mesher import Square
from fatiando.vis import mpl
from fatiando.seismic import ttime2d
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
"""
GravMag: Generate noise-corrupted gravity gradient tensor data
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism
from fatiando.vis import mpl
model = [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 = [prism.gxx, prism.gxy, prism.gxz, prism.gyy, prism.gyz, prism.gzz]
print "Calculate the tensor components and contaminate with 5 Eotvos noise"
ftg = [utils.contaminate(comp(xp, yp, zp, model), 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()