area = bounds[:4]
axis = mpl.figure().gca()
mpl.axis('scaled')
model = [
mesher.PolygonalPrism(
mpl.draw_polygon(area, axis, xy2ne=True),
# Use only induced magnetization
0, 2000, {'magnetization':2})]
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
tf = polyprism.tf(xp, yp, zp, model, inc, dec)
# and plot it
mpl.figure()
mpl.axis('scaled')
mpl.title("Total field anomalyproduced by prism model (nT)")
mpl.contourf(yp, xp, tf, 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, 'magnetization')
myv.axes(myv.outline(bounds), ranges=[i*0.001 for i in bounds])
myv.wall_north(bounds)
myv.wall_bottom(bounds)
myv.show()
area = bounds[:4]
axis = mpl.figure().gca()
mpl.axis('scaled')
model = [
mesher.PolygonalPrism(
mpl.draw_polygon(area, axis, xy2ne=True),
# Use only induced magnetization
0, 2000, {'magnetization': 2})]
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
tf = polyprism.tf(xp, yp, zp, model, inc, dec)
# and plot it
mpl.figure()
mpl.axis('scaled')
mpl.title("Total field anomalyproduced by prism model (nT)")
mpl.contourf(yp, xp, tf, 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, 'magnetization')
myv.axes(myv.outline(bounds), ranges=[i * 0.001 for i in bounds])
myv.wall_north(bounds)
myv.wall_bottom(bounds)
myv.show()
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)
mpl.polygon(estimate, 'o-r', linewidth=2, fill='r', alpha=0.3,
label='Estimated')
mpl.polygon(model, '--k', linewidth=2, label='True')
mpl.legend(loc='lower left', numpoints=1)
mpl.xlabel("X")
mpl.ylabel("Z")
mpl.set_area((0, 100000, 10000, -500))
mpl.show()
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,
label='Estimated')
mpl.polygon(model, '--k', linewidth=2, label='True')
mpl.legend(loc='lower left', numpoints=1)
mpl.xlabel("X")
mpl.ylabel("Z")
mpl.set_area((0, 100000, 10000, -500))
mpl.show()
import numpy
from fatiando import mesher, gridder
from fatiando.gravmag import polyprism, tensor
from fatiando.vis import mpl
print "Draw the polygons one by one"
area = [-10000, 10000, -10000, 10000]
dataarea = [-5000, 5000, -5000, 5000]
model = []
for depth in [5000, 5000]:
fig = mpl.figure()
mpl.axis('scaled')
mpl.square(dataarea)
for p in model:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
model.append(
mesher.PolygonalPrism(
mpl.draw_polygon(area, fig.gca(), xy2ne=True),
0, depth, {'density':500}))
# Calculate the effect
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
log = logger.get()
log.info(logger.header())
log.info(__doc__)
log.info("Draw the polygons one by one")
area = [-10000, 10000, -10000, 10000]
dataarea = [-5000, 5000, -5000, 5000]
prisms = []
for depth in [5000, 5000]:
fig = mpl.figure()
mpl.axis('scaled')
mpl.square(dataarea)
for p in prisms:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
prisms.append(
mesher.PolygonalPrism(mpl.draw_polygon(area, fig.gca(), xy2ne=True), 0,
depth, {'density': 500}))
# Calculate the effect
shape = (100, 100)
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
