def plot_horizons(ax, horizons, slip=(0,0)):
fault = data.world_xyz(data.fault)
sec = Section()
ax.set_ylim([-8, -1.5])
for hor in horizons:
xyz = data.world_xyz(hor)[::100]
xyz = inclined_shear(fault, xyz, slip, data.alpha)
sec.plot(xyz, ax)
sec.plot(fault, ax, color='red')
plt.setp(ax.get_xticklabels(), visible=False)
def plot_horizons(ax, horizons, slip=(0, 0)):
fault = data.world_xyz(data.fault)
sec = Section()
ax.set_ylim([-8, -1.5])
for hor in horizons:
xyz = data.world_xyz(hor)[::100]
xyz = inclined_shear(fault, xyz, slip, data.alpha)
sec.plot(xyz, ax)
sec.plot(fault, ax, color='red')
plt.setp(ax.get_xticklabels(), visible=False)
def calculate_heave(slip, hor):
"""Calculates the average heave resulting from moving the given horizon
(`hor`) by `slip` along the main fault."""
orig_xyz = data.world_xyz(hor)[::50]
fault = data.world_xyz(data.fault)
func = homogeneous_simple_shear.inclined_shear
moved_xyz = func(fault, orig_xyz, slip, data.alpha, remove_invalid=False)
diff = moved_xyz - orig_xyz
mask = np.isfinite(diff)
mask = mask[:,0] & mask[:,1]
return diff[mask,:].mean(axis=0)
def setup_plot(self):
tri = triangles(self.fault)
fault = self.view_triangles(data.world_xyz(self.fault), tri)
# fault = self.view_xyz_surface(data.world_xyz(self.fault))
if self.origxyz is not None:
self.view_xyz_surface(self.origxyz)
self.scene.mlab.orientation_axes()
self.scene.mlab.outline(fault)
return self.view_xyz_surface(self.horxyz)
def setup_plot(self):
tri = triangles(self.fault)
fault = self.view_triangles(data.world_xyz(self.fault), tri)
# fault = self.view_xyz_surface(data.world_xyz(self.fault))
if self.origxyz is not None:
self.view_xyz_surface(self.origxyz)
self.scene.mlab.orientation_axes()
self.scene.mlab.outline(fault)
return self.view_xyz_surface(self.horxyz)
def restore_horizons(func=invert_slip):
"""
Restore each of the uplifted horizons individually.
"func" just allows overriding of the specific inversion (e.g. see
"invert_slip_fixed_azimuth.py") without code duplication.
"""
# Note that we start each horizon at zero offset and restore independently
guess = (0, 0)
variances, planar_variances = [], []
slips, heaves = [], []
for hor in data.horizons[::-1]:
hor_xyz = data.world_xyz(hor)
# Downsample the horizon for faster runtime
# (No need to include millions of points along the horizon's surface)
hor_xyz = hor_xyz[::50]
# Invert for the slip along the fault needed to restore the horizon
# to horizontal.
slip, metric = func(data.fault_xyz,
hor_xyz,
alpha=data.alpha,
guess=guess,
overlap_thresh=1,
return_metric=True)
heave = utilities.calculate_heave(slip, hor)
variances.append(metric)
planar_var = planar_variance(hor_xyz)
planar_variances.append(planar_var)
slips.append(slip)
heaves.append(heave)
# Note: We're plotting "metric / planar_var" to allow a direct
# comparison of the quality of the fit between different horizons.
print 'Restoring', hor.name
print ' Roughness (lower is better):', metric / planar_var
return slips, heaves, variances, planar_variances
def restore_horizons(func=invert_slip):
"""
Restore each of the uplifted horizons individually.
"func" just allows overriding of the specific inversion (e.g. see
"invert_slip_fixed_azimuth.py") without code duplication.
"""
# Note that we start each horizon at zero offset and restore independently
guess = (0,0)
variances, planar_variances = [], []
slips, heaves = [], []
for hor in data.horizons[::-1]:
hor_xyz = data.world_xyz(hor)
# Downsample the horizon for faster runtime
# (No need to include millions of points along the horizon's surface)
hor_xyz = hor_xyz[::50]
# Invert for the slip along the fault needed to restore the horizon
# to horizontal.
slip, metric = func(data.fault_xyz, hor_xyz, alpha=data.alpha,
guess=guess, overlap_thresh=1, return_metric=True)
heave = utilities.calculate_heave(slip, hor)
variances.append(metric)
planar_var = planar_variance(hor_xyz)
planar_variances.append(planar_var)
slips.append(slip)
heaves.append(heave)
# Note: We're plotting "metric / planar_var" to allow a direct
# comparison of the quality of the fit between different horizons.
print 'Restoring', hor.name
print ' Roughness (lower is better):', metric / planar_var
return slips, heaves, variances, planar_variances
def forced_direction_inversion(fault, xyz, alpha, azimuth, **kwargs):
azimuth = np.radians(90 - azimuth)
dx, dy = np.cos(azimuth), np.sin(azimuth)
direc = [[dx, dy], [dx, dy]]
return invert_slip(fault, xyz, alpha, direc=direc, **kwargs)
def visualize(slip, faultxyz, horxyz):
fault = geoprobe.swfault('/data/nankai/data/swFaults/jdk_oos_splay_large_area_depth.swf')
azimuth = np.degrees(np.arctan2(*slip[::-1]))
azimuth = 90 - azimuth
mag = np.linalg.norm(slip) / 1000
f = FaultModel(fault, horxyz, origxyz=horxyz, ve=3, azimuth=azimuth,
slip=mag, alpha=data.alpha, calc_fault=faultxyz)
f.configure_traits()
fault = data.world_xyz(data.fault)
for i, hor in enumerate(data.horizons[::-1]):
print hor.name
xyz = data.to_world(data.to_xyz(hor))[::50]
slip, metric = invert_slip(fault, xyz, alpha=data.alpha, guess=(0,0),
overlap_thresh=1, return_metric=True)
# slip, metric = forced_direction_inversion(fault, xyz, data.alpha, data.fault_strike+90,
# guess=guess, return_metric=True)
visualize(slip, fault, xyz)
def dip(hor):
x, y, z = data.world_xyz(hor).T
strike, dip = geoprobe.utilities.points2strikeDip(x, y, -z)
return strike, dip
def dip(hor):
x, y, z = data.world_xyz(hor).T
strike, dip = geoprobe.utilities.points2strikeDip(x, y, -z)
return strike, dip
