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
traveltime, error = utils.contaminate(stime - ptime, 0.05, percent=True,
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()
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)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment and the result
mpl.figure()
mpl.title("True: color | Predicted: contour")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
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()
myv.points(cms, size=200.)
myv.prisms(prisms, prop='density', opacity=0.5)
axes = myv.axes(myv.outline(extent=[-5000, 5000, -5000, 5000, 0, 5000]))
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
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,
mesh,
compactness=0.05,
threshold=0.0005)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment and the result
mpl.figure()
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
traveltime, error = utils.contaminate(stime - ptime,
