def main():
fault = data.to_world(data.to_xyz(data.fault))
fault = shared_array(fault)
# Initalize output file...
output = h5py.File('bootstrap.hdf5', 'w')
group = output.create_group('IndependentBootstrap')
group.attrs['alpha'] = data.alpha
pool = multiprocessing.Pool()
for hor in data.horizons:
print hor.name
horxyz = data.to_world(data.to_xyz(hor))
# Use a shared array...
horxyz = shared_array(horxyz)
# Run in parallel...
slip, var = parallel_bootstrap_slip(fault, horxyz, data.alpha,
pool=pool, numruns=200, numsamples=10000)
# Save results...
hor_group = group.create_group(hor.name)
hor_group.create_dataset('slip', data=slip)
hor_group.create_dataset('variance', data=var)
output.close()
def optimize_individual_alpha():
"""Find the best shear angle for each horizon using a grid search."""
fault = data.to_world(data.to_xyz(data.fault))
alphas = range(-80, 85, 5)
for hor in data.horizons:
update(hor.name + ': ')
xyz = data.to_world(data.to_xyz(hor))[::50]
roughness = []
for i, alpha in enumerate(alphas):
update('%i ' % (len(alphas) - i))
slip, metric = invert(fault, xyz, alpha)
roughness.append(metric)
update('\n')
fig, ax = plt.subplots()
ax.plot(alphas, roughness)
ax.set_title(hor.name)
ax.set_ylabel('Misfit (m)')
ax.set_xlabel('Shear angle (degrees)')
fig.savefig('optimize_alpha_individual_%s.pdf'%hor.name)
plt.show()
def optimize_single_alpha():
"""Find the single shear angle that best flattens all horizons using a grid
search."""
fault = data.to_world(data.to_xyz(data.fault))
alphas = range(-80, 85, 5)
roughness = []
for alpha in alphas:
update('Alpha = %i: ' % alpha)
rough = 0
for i, hor in enumerate(data.horizons):
xyz = data.to_world(data.to_xyz(hor))[::50]
update('%i ' % (len(data.horizons) - i))
slip, metric = invert(fault, xyz, alpha)
rough += metric
update('\n')
roughness.append(rough)
fig, ax = plt.subplots()
ax.plot(alphas, roughness)
ax.set_title('Optimizing Shear Angle')
ax.set_ylabel('Summed misfit (m)')
ax.set_xlabel('Shear angle (degrees)')
fig.savefig('optimize_single_alpha.pdf')
plt.show()
def optimize_single_alpha():
"""Find the single shear angle that best flattens all horizons using a grid
search."""
fault = data.to_world(data.to_xyz(data.fault))
alphas = range(-80, 85, 5)
roughness = []
for alpha in alphas:
update('Alpha = %i: ' % alpha)
rough = 0
for i, hor in enumerate(data.horizons):
xyz = data.to_world(data.to_xyz(hor))[::50]
update('%i ' % (len(data.horizons) - i))
slip, metric = invert(fault, xyz, alpha)
rough += metric
update('\n')
roughness.append(rough)
fig, ax = plt.subplots()
ax.plot(alphas, roughness)
ax.set_title('Optimizing Shear Angle')
ax.set_ylabel('Summed misfit (m)')
ax.set_xlabel('Shear angle (degrees)')
fig.savefig('optimize_single_alpha.pdf')
plt.show()
def optimize_individual_alpha():
"""Find the best shear angle for each horizon using a grid search."""
fault = data.to_world(data.to_xyz(data.fault))
alphas = range(-80, 85, 5)
for hor in data.horizons:
update(hor.name + ': ')
xyz = data.to_world(data.to_xyz(hor))[::50]
roughness = []
for i, alpha in enumerate(alphas):
update('%i ' % (len(alphas) - i))
slip, metric = invert(fault, xyz, alpha)
roughness.append(metric)
update('\n')
fig, ax = plt.subplots()
ax.plot(alphas, roughness)
ax.set_title(hor.name)
ax.set_ylabel('Misfit (m)')
ax.set_xlabel('Shear angle (degrees)')
fig.savefig('optimize_alpha_individual_%s.pdf' % hor.name)
plt.show()
def main():
fault = data.to_world(data.to_xyz(data.fault))
fault = shared_array(fault)
# Initalize output file...
output = h5py.File('bootstrap.hdf5', 'w')
group = output.create_group('IndependentBootstrap')
group.attrs['alpha'] = data.alpha
pool = multiprocessing.Pool()
for hor in data.horizons:
print hor.name
horxyz = data.to_world(data.to_xyz(hor))
# Use a shared array...
horxyz = shared_array(horxyz)
# Run in parallel...
slip, var = parallel_bootstrap_slip(fault,
horxyz,
data.alpha,
pool=pool,
numruns=200,
numsamples=10000)
# Save results...
hor_group = group.create_group(hor.name)
hor_group.create_dataset('slip', data=slip)
hor_group.create_dataset('variance', data=var)
output.close()
def main():
fault = geoprobe.swfault('/data/nankai/data/swFaults/jdk_oos_splay_large_area_depth.swf')
faultxyz = data.to_world(data.to_xyz(data.fault))
horxyz = data.to_world(data.to_xyz(data.horizons[0]))[::100]
slip = invert_slip(faultxyz, horxyz, alpha=data.alpha)
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()
def __init__(self, fault, horxyz, origxyz=None, ve=2, calc_fault=None,
**kwargs):
self.ve = ve
self.origxyz = origxyz
self.fault = fault
if calc_fault is None:
self.faultxyz = data.to_world(data.to_xyz(fault))
else:
self.faultxyz = calc_fault
self.horxyz = horxyz
HasTraits.__init__(self, **kwargs)
def main():
fault = geoprobe.swfault(
'/data/nankai/data/swFaults/jdk_oos_splay_large_area_depth.swf')
faultxyz = data.to_world(data.to_xyz(data.fault))
horxyz = data.to_world(data.to_xyz(data.horizons[0]))[::100]
slip = invert_slip(faultxyz, horxyz, alpha=data.alpha)
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()
def __init__(self,
fault,
horxyz,
origxyz=None,
ve=2,
calc_fault=None,
**kwargs):
self.ve = ve
self.origxyz = origxyz
self.fault = fault
if calc_fault is None:
self.faultxyz = data.to_world(data.to_xyz(fault))
else:
self.faultxyz = calc_fault
self.horxyz = horxyz
HasTraits.__init__(self, **kwargs)
Group(
'_', 'azimuth', 'slip', 'alpha',
),
resizable=True,
)
def triangles(fault):
if isinstance(fault, basestring):
fault = geoprobe.swfault(fault)
# Iterate through triangles in internal coords and select those inside
# outline the non-convex outline of the fault...
xyz = fault._internal_xyz
rotated_tri = LinearNDInterpolator(xyz[:,:2], xyz[:,-1])
rotated_xyz = fault._internal_xyz
rotated_outline = Polygon(fault._rotated_outline)
def inside_outline(tri):
return rotated_outline.contains(Polygon(rotated_xyz[tri]))
triangles = rotated_tri.tri.vertices
return np.array([tri for tri in triangles if inside_outline(tri)])
if __name__ == '__main__':
fault = geoprobe.swfault('/data/nankai/data/swFaults/jdk_oos_splay_large_area_depth.swf')
hor = data.horizons[0]
horxyz = data.to_world(data.to_xyz(hor))[::100]
plot = FaultModel(fault, horxyz)
plot.configure_traits()
import numpy as np
import matplotlib.pyplot as plt
from fault_kinematics.homogeneous_simple_shear import inclined_shear
from fault_kinematics.homogeneous_simple_shear import invert_slip
import data
import utilities
fault = data.to_xyz(data.fault)
fault = data.to_world(fault)
models = [[0, 0]]
i = 0
for hor in data.horizons[::-1]:
print hor.name
# Downsample horizon for faster solution...
xyz = data.to_xyz(hor)[::50,:]
xyz = data.to_world(xyz)
# Move this horizon to the last horizon's best fit offset.
# Following the path of all previous horizons...
for model in models:
dx, dy = model
xyz = inclined_shear(fault, xyz, (dx,dy), data.alpha)
model = invert_slip(fault, xyz, data.alpha, guess=(0,0))
models.append(model)
i += 1
from fault_kinematics.homogeneous_simple_shear import invert_slip
import utilities
import data
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 planar_variance(xyz):
vecs, vals = geoprobe.utilities.principal_axes(*xyz.T, return_eigvals=True)
return vals[-1]
fault = data.to_world(data.to_xyz(data.fault))
slips = [(0,0)]
guess = (0,0)
heaves = [(0,0,0)]
planar_variances = []
variances = []
for i, hor in enumerate(data.horizons[::-1]):
print hor.name
xyz = data.to_xyz(hor)[::50]
xyz = data.to_world(xyz)
slip, metric = invert_slip(fault, xyz, alpha=data.alpha, guess=guess,
overlap_thresh=1, return_metric=True)
),
resizable=True,
)
def triangles(fault):
if isinstance(fault, basestring):
fault = geoprobe.swfault(fault)
# Iterate through triangles in internal coords and select those inside
# outline the non-convex outline of the fault...
xyz = fault._internal_xyz
rotated_tri = LinearNDInterpolator(xyz[:, :2], xyz[:, -1])
rotated_xyz = fault._internal_xyz
rotated_outline = Polygon(fault._rotated_outline)
def inside_outline(tri):
return rotated_outline.contains(Polygon(rotated_xyz[tri]))
triangles = rotated_tri.tri.vertices
return np.array([tri for tri in triangles if inside_outline(tri)])
if __name__ == '__main__':
fault = geoprobe.swfault(
'/data/nankai/data/swFaults/jdk_oos_splay_large_area_depth.swf')
hor = data.horizons[0]
horxyz = data.to_world(data.to_xyz(hor))[::100]
plot = FaultModel(fault, horxyz)
plot.configure_traits()
import geoprobe
import data
import utilities
from fault_kinematics import homogeneous_simple_shear
from process_bootstrap_results import get_result, load
fault = data.to_world(data.to_xyz(data.fault))
alpha = data.alpha
f, group = load()
for hor in data.horizons:
print hor.name
xyz = data.to_world(data.to_xyz(hor))
slip = get_result(hor.name, group)
restored = homogeneous_simple_shear.inclined_shear(fault, xyz, slip, alpha)
print 'Resampling...'
restored = data.to_model(restored)
restored = utilities.grid_xyz(restored)
new_hor = geoprobe.horizon(*restored.T)
new_hor.write('restored_horizons/' + hor.name + '.hzn')
f.close()
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)
import numpy as np
import matplotlib.pyplot as plt
from fault_kinematics.homogeneous_simple_shear import inclined_shear
from fault_kinematics.homogeneous_simple_shear import invert_slip
import data
import utilities
fault = data.to_xyz(data.fault)
fault = data.to_world(fault)
models = [[0, 0]]
i = 0
for hor in data.horizons[::-1]:
print hor.name
# Downsample horizon for faster solution...
xyz = data.to_xyz(hor)[::50, :]
xyz = data.to_world(xyz)
# Move this horizon to the last horizon's best fit offset.
# Following the path of all previous horizons...
for model in models:
dx, dy = model
xyz = inclined_shear(fault, xyz, (dx, dy), data.alpha)
model = invert_slip(fault, xyz, data.alpha, guess=(0, 0))
models.append(model)
i += 1
