import matplotlib.pyplot as plt
from geoist.pfm import prism, pftrans, giutils
from geoist.inversion.geometry import Prism
from geoist import gridder
# Create some synthetic magnetic data with a total magnetization that is
# different from the geomagnetic field (so there is remanent magnetization or
# some demagnetizing effect)
inc, dec = -60, 23 # Geomagnetic field direction
sinc, sdec = -30, -20 # Source magnetization direction
mag = giutils.ang2vec(1, sinc, sdec)
model = [Prism(-1500, 1500, -500, 500, 0, 2000, {'magnetization': mag})]
area = (-7e3, 7e3, -7e3, 7e3)
shape = (100, 100)
x, y, z = gridder.regular(area, shape, z=-300)
data = prism.tf(x, y, z, model, inc, dec)
# Reduce to the pole
data_at_pole = pftrans.reduce_to_pole(x, y, data, shape, inc, dec, sinc, sdec)
# Make some plots
plt.figure(figsize=(8, 6))
ax = plt.subplot(1, 2, 1)
ax.set_title('Original data')
ax.set_aspect('equal')
tmp = ax.tricontourf(y / 1000, x / 1000, data, 30, cmap='RdBu_r')
plt.colorbar(tmp, pad=0.1, aspect=30, orientation='horizontal').set_label('nT')
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax.set_xlim(area[2] / 1000, area[3] / 1000)
prisms.append(p)
print('kernel')
inc, dec = 30, -4
kernelgz = prism.gz_kernel(xp, yp, zp, prisms)
for i, layer in enumerate(mesh.layers()):
for j, p in enumerate(layer):
x1 = mesh.get_layer(i)[j].x1
x2 = mesh.get_layer(i)[j].x2
y1 = mesh.get_layer(i)[j].y1
y2 = mesh.get_layer(i)[j].y2
z1 = mesh.get_layer(i)[j].z1
z2 = mesh.get_layer(i)[j].z2
den = mesh.get_layer(i)[j].props
model=[geometry.Prism(x1, x2, y1, y2, z1, z2,
{'magnetization': giutils.ang2vec(1, inc, dec)})]
field = prism.tf(xp, yp, zp, model, inc, dec)
kernel.append(field)
kk=np.transpose(kernel) #kernel matrix for inversion, 500 cells * 400 points
field_mag=np.mat(kk)*np.transpose(np.mat(mag1.ravel()))
field_gra=np.mat(kernelgz)*np.transpose(np.mat(rho1.ravel()))
field_mag1 = giutils.contaminate(np.array(field_mag).ravel(), 0.05, percent = True)
field_gra1 = giutils.contaminate(np.array(field_gra).ravel(), 0.05, percent = True)
#保存正演异常
with open(graoutfile, 'w') as f:
f.write('! model 2 gravity anomlay (mGal)\n')
f.write('{}\n'.format(len(field_gra)))
for i in range(len(field_gra)):
f.write('{} {} {} {}\n'.format(yp[i],xp[i],zp[i],np.array(field_gra[i]).ravel()[0]))
"""
import matplotlib.pyplot as plt
from geoist.pfm import prism, pftrans, giutils
from geoist.inversion.geometry import Prism
from geoist import gridder
# Create some synthetic magnetic data. We'll assume the data is already reduced
# to the pole.
inc, dec = 90, 0
mag = giutils.ang2vec(1, inc, dec)
model = [Prism(-1500, 1500, -500, 500, 0, 2000, {'magnetization': mag})]
area = (-7e3, 7e3, -7e3, 7e3)
shape = (100, 100)
x, y, z = gridder.regular(area, shape, z=-300)
data_at_pole = prism.tf(x, y, z, model, inc, dec)
# Calculate the tilt
tilt = pftrans.tilt(x, y, data_at_pole, shape)
# Make some plots
plt.figure(figsize=(8, 6))
ax = plt.subplot(1, 2, 1)
ax.set_title('Original data at the pole')
ax.set_aspect('equal')
tmp = ax.tricontourf(y / 1000, x / 1000, data_at_pole, 30, cmap='RdBu_r')
plt.colorbar(tmp, pad=0.1, aspect=30, orientation='horizontal').set_label('nT')
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax.set_xlim(area[2] / 1000, area[3] / 1000)
from geoist.inversion import geometry
from geoist.pfm import prism, pftrans, giutils
from geoist.vis import giplt
# Direction of the Geomagnetic field
inc, dec = -60, 0
# Make a model with only induced magnetization
model = [
geometry.Prism(-100, 100, -100, 100, 0, 2000,
{'magnetization': giutils.ang2vec(10, inc, dec)})
]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
tf = giutils.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 = pftrans.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=giutils.ang2vec(10, 90, 0))
fig, axes = plt.subplots(1, 3, figsize=(14, 4))
for ax in axes:
ax.set_aspect('equal')
plt.sca(axes[0])
plt.title("Original total field anomaly")
giplt.contourf(y, x, tf, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(pad=0).set_label('nT')
giplt.m2km()
plt.sca(axes[1])
"""
GravMag: Calculate the analytic signal of a total field anomaly using FFT
"""
import matplotlib.pyplot as plt
from geoist import gridder
from geoist.inversion import geometry
from geoist.pfm import prism, pftrans, giutils
from geoist.vis import giplt
model = [geometry.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 = giutils.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 = pftrans.tga(x, y, giutils.nt2si(tf), shape)
plt.figure()
plt.subplot(1, 2, 1)
plt.title("Original total field anomaly")
plt.axis('scaled')
giplt.contourf(y, x, tf, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(orientation='horizontal').set_label('nT')
giplt.m2km()
plt.subplot(1, 2, 2)
plt.title("Total Gradient Amplitude")
plt.axis('scaled')