def _get_hessian(self):
"""
Build the Hessian matrix by Gauss-Newton approximation.
"""
log.info(" calculating Hessian matrix:")
start = time.time()
if not self.sparse:
hess = numpy.dot(self.jacobian_T, self.jacobian)
else:
hess = self.jacobian_T * self.jacobian
log.info(" time: %s" % (utils.sec2hms(time.time() - start)))
return hess
def _iterator(dms, regs, solver):
start = time.time()
try:
for i, chset in enumerate(solver(dms, regs)):
yield chset["estimate"], chset["residuals"][0]
except numpy.linalg.linalg.LinAlgError:
raise ValueError, ("Oops, the Hessian is a singular matrix." + " Try applying more regularization")
stop = time.time()
log.info(" number of iterations: %d" % (i))
log.info(" final data misfit: %g" % (chset["misfits"][-1]))
log.info(" final goal function: %g" % (chset["goals"][-1]))
log.info(" time: %s" % (utils.sec2hms(stop - start)))
def _solver(dms, solver, log):
start = time.time()
try:
for i, chset in enumerate(solver(dms, [])):
continue
except numpy.linalg.linalg.LinAlgError:
raise ValueError, ("Oops, the Hessian is a singular matrix." +
" Try applying more regularization")
stop = time.time()
log.info(" number of iterations: %d" % (i))
log.info(" final data misfit: %g" % (chset['misfits'][-1]))
log.info(" final goal function: %g" % (chset['goals'][-1]))
log.info(" time: %s" % (utils.sec2hms(stop - start)))
return chset['estimate'], chset['residuals'][0]
def _get_jacobian(self):
"""
Build the Jacobian (sensitivity) matrix using the travel-time data
stored.
"""
log.info(" calculating Jacobian (sensitivity matrix):")
start = time.time()
srcs, recs = self.srcs, self.recs
if not self.sparse:
jac = numpy.array([ttime2d.straight([cell], "", srcs, recs, velocity=1.0) for cell in self.mesh]).T
else:
shoot = ttime2d.straight
nonzero = []
extend = nonzero.extend
for j, c in enumerate(self.mesh):
extend((i, j, tt) for i, tt in enumerate(shoot([c], "", srcs, recs, velocity=1.0)) if tt != 0)
row, col, val = numpy.array(nonzero).T
shape = (self.ndata, self.nparams)
jac = scipy.sparse.csr_matrix((val, (row, col)), shape)
log.info(" time: %s" % (utils.sec2hms(time.time() - start)))
return jac
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = mesher.SquareMesh(area, shape)
# Fetch the image from the online docs
urllib.urlretrieve("http://fatiando.readthedocs.org/en/latest/_static/logo.png", "logo.png")
model.img2prop("logo.png", 4000, 10000, "vp")
# Make some travel time data and add noise
log.info("Generating synthetic travel-time data")
src_loc = utils.random_points(area, 80)
rec_loc = utils.circular_points(area, 30, random=True)
srcs, recs = utils.connect_points(src_loc, rec_loc)
start = time.time()
tts = seismic.ttime2d.straight(model, "vp", srcs, recs, par=True)
log.info(" time: %s" % (utils.sec2hms(time.time() - start)))
tts, error = utils.contaminate(tts, 0.01, percent=True, return_stddev=True)
# Make the mesh
mesh = mesher.SquareMesh(area, shape)
# and run the inversion
estimate, residuals = seismic.srtomo.run(tts, srcs, recs, mesh, damping=10 ** 9)
# Convert the slowness estimate to velocities and add it the mesh
mesh.addprop("vp", seismic.srtomo.slowness2vel(estimate))
# Calculate and print the standard deviation of the residuals
# it should be close to the data error if the inversion was able to fit the data
log.info("Assumed error: %g" % (error))
log.info("Standard deviation of residuals: %g" % (numpy.std(residuals)))
vis.mpl.figure(figsize=(14, 5))
vis.mpl.subplot(1, 2, 1)
lons, lats, heights = gridder.regular(area, shape, z=250000)
# Divide the model into nproc slices and calculate them in parallel
log.info('Calculating...')
def calculate(chunk):
return gravmag.tesseroid.gz(lons, lats, heights, chunk)
def split(model, nproc):
chunksize = len(model)/nproc
for i in xrange(nproc - 1):
yield model[i*chunksize : (i + 1)*chunksize]
yield model[(nproc - 1)*chunksize : ]
start = time.time()
nproc = 8
pool = Pool(processes=nproc)
gz = sum(pool.map(calculate, split(model, nproc)))
pool.close()
print "Time it took: %s" % (utils.sec2hms(time.time() - start))
log.info('Plotting...')
mpl.figure(figsize=(10, 4))
mpl.title('Crust gravity signal at 250km height')
bm = mpl.basemap(area, 'robin')
mpl.contourf(lons, lats, gz, shape, 35, basemap=bm)
cb = mpl.colorbar()
cb.set_label('mGal')
bm.drawcoastlines()
bm.drawmapboundary()
bm.drawparallels(range(-90, 90, 45), labels=[0, 1, 0, 0])
bm.drawmeridians(range(-180, 180, 60), labels=[0, 0, 0, 1])
mpl.show()
def migrate(x, y, z, gz, zmin, zmax, meshshape, power=0.5, scale=1):
"""
3D potential field migration (Zhdanov et al., 2011).
Actually uses the formula of Fedi and Pilkington (2012), which are
comprehensible.
.. note:: Only works on **gravity** data for now.
.. note:: The data **do not** need to be leveled or on a regular grid.
.. note:: The coordinate system adopted is x->North, y->East, and z->Down
Parameters:
* x, y : 1D-arrays
The x and y coordinates of the grid points
* z : float or 1D-array
The z coordinate of the grid points
* gz : 1D-array
The gravity anomaly data at the grid points
* zmin, zmax : float
The top and bottom, respectively, of the region where the physical
property distribution is calculated
* meshshape : tuple = (nz, ny, nx)
Number of prisms in the output mesh in the x, y, and z directions,
respectively
* power : float
The power law used for the depth weighting. This controls what depth
the bulk of the solution will be.
* scale : float
A scale factor for the depth weights. Simply changes the scale of the
physical property values.
Returns:
* mesh : :class:`fatiando.mesher.PrismMesh`
The estimated physical property distribution set in a prism mesh (for
easy 3D plotting)
"""
nlayers, ny, nx = meshshape
mesh = _makemesh(x, y, (ny, nx), zmin, zmax, nlayers)
log.info("3D migration of gravity data:")
# This way, if z is not an array, it is now
z = z*numpy.ones_like(x)
log.info(" number of data: %d" % (len(gz)))
log.info(" mesh zmin and zmax: %g, %g" % (zmin, zmax))
log.info(" mesh shape: %s" % (str(meshshape)))
log.info(" depth weighting power law: %g" % (power))
log.info(" depth weighting scale factor: %g" % (scale))
tstart = time.clock()
dx, dy, dz = mesh.dims
# Synthetic tests show that its not good to offset the weights with the data
# z coordinate. No idea why
depths = mesh.get_zs()[:-1] + 0.5*dz
weights = numpy.abs(depths)**power/(2*G*numpy.sqrt(numpy.pi))
density = []
for l in xrange(nlayers):
sensibility_T = numpy.array(
[pot_prism.gz(x, y, z, [p], dens=1) for p in mesh.get_layer(l)])
density.extend(scale*weights[l]*numpy.dot(sensibility_T, gz))
tend = time.clock()
log.info(" total time for imaging: %s" % (utils.sec2hms(tend - tstart)))
mesh.addprop('density', numpy.array(density))
return mesh
def geninv(x, y, z, data, shape, zmin, zmax, nlayers):
"""
Generalized Inverse imaging in the frequency domain (Cribb, 1976).
Calculates a physical property distribution given potential field data on a
**regular grid**.
.. note:: Only works on **gravity** data for now.
.. note:: The data **must** be leveled, i.e., on the same height!
.. note:: The coordinate system adopted is x->North, y->East, and z->Down
.. warning:: The Generalized Inverse does **not** use depth weights. This
means that the solution will tend to be concentrated on the surface!
Parameters:
* x, y : 1D-arrays
The x and y coordinates of the grid points
* z : float or 1D-array
The z coordinate of the grid points
* data : 1D-array
The potential field at the grid points
* shape : tuple = (ny, nx)
The shape of the grid
* zmin, zmax : float
The top and bottom, respectively, of the region where the physical
property distribution is calculated
* nlayers : int
The number of layers used to divide the region where the physical
property distribution is calculated
Returns:
* mesh : :class:`fatiando.mesher.PrismMesh`
The estimated physical property distribution set in a prism mesh (for
easy 3D plotting)
"""
mesh = _makemesh(x, y, shape, zmin, zmax, nlayers)
log.info("Generalized Inverse imaging of gravity data:")
# This way, if z is not an array, it is now
z = z*numpy.ones_like(x)
log.info(" data z coordinate: %g" % (z[0]))
log.info(" data shape: %s" % (str(shape)))
log.info(" mesh zmin and zmax: %g, %g" % (zmin, zmax))
log.info(" number of layers in the mesh: %d" % (nlayers))
tstart = time.clock()
freq, dataft = _getdataft(x, y, data, shape)
dx, dy, dz = mesh.dims
# Remove the last z because I only want depths to the top of the layers
depths = mesh.get_zs()[:-1] + 0.5*dz - z[0] # Offset by the data height
density = []
for depth in depths:
density.extend(
numpy.real(
numpy.fft.ifft2(
numpy.exp(-freq*depth)*freq*dataft/(numpy.pi*G)
).ravel()
))
tend = time.clock()
log.info(" total time for imaging: %s" % (utils.sec2hms(tend - tstart)))
mesh.addprop('density', numpy.array(density))
return mesh
def sandwich(x, y, z, data, shape, zmin, zmax, nlayers, power=0.5):
"""
Sandwich model (Pedersen, 1991).
Calculates a physical property distribution given potential field data on a
**regular grid**. Uses depth weights.
.. note:: Only works on **gravity** data for now.
.. note:: The data **must** be leveled, i.e., on the same height!
.. note:: The coordinate system adopted is x->North, y->East, and z->Down
Parameters:
* x, y : 1D-arrays
The x and y coordinates of the grid points
* z : float or 1D-array
The z coordinate of the grid points
* data : 1D-array
The potential field at the grid points
* shape : tuple = (ny, nx)
The shape of the grid
* zmin, zmax : float
The top and bottom, respectively, of the region where the physical
property distribution is calculated
* nlayers : int
The number of layers used to divide the region where the physical
property distribution is calculated
* power : float
The power law used for the depth weighting. This controls what depth
the bulk of the solution will be.
Returns:
* mesh : :class:`fatiando.mesher.PrismMesh`
The estimated physical property distribution set in a prism mesh (for
easy 3D plotting)
"""
mesh = _makemesh(x, y, shape, zmin, zmax, nlayers)
log.info("Sandwich model of gravity data:")
# This way, if z is not an array, it is now
z = z*numpy.ones_like(x)
log.info(" data z coordinate: %g" % (z[0]))
log.info(" data shape: %s" % (str(shape)))
log.info(" mesh zmin and zmax: %g, %g" % (zmin, zmax))
log.info(" number of layers in the mesh: %d" % (nlayers))
log.info(" depth weighting power law: %g" % (power))
tstart = time.clock()
freq, dataft = _getdataft(x, y, data, shape)
dx, dy, dz = mesh.dims
# Remove the last z because I only want depths to the top of the layers
depths = mesh.get_zs()[:-1]
weights = (numpy.abs(depths) + 0.5*dz)**(power)
density = []
# Offset by the data z because in the paper the data is at z=0
for depth, weight in zip(depths - z[0], weights):
density.extend(
numpy.real(numpy.fft.ifft2(
weight*(numpy.exp(-freq*depth) - numpy.exp(-freq*(depth + dz)))
*freq*dataft /
(numpy.pi*G*
reduce(numpy.add,
[w*(numpy.exp(-freq*h) - numpy.exp(-freq*(h + dz)))**2
+ 10.**(-10) # To avoid zero division when freq[i]==0
for h, w in zip(depths, weights)])
)
).ravel()))
tend = time.clock()
log.info(" total time for imaging: %s" % (utils.sec2hms(tend - tstart)))
mesh.addprop('density', numpy.array(density))
return mesh
def harvest(data, seeds, mesh, compactness, threshold):
"""
Run the inversion algorithm and produce an estimate physical property
distribution (density and/or magnetization).
Paramters:
* data : list of data (e.g., :class:`~fatiando.gravmag.harvester.Gz`)
The data that will be inverted. Data used must match the physical
properties given to the seeds (e.g., gravity data requires seeds to have
``'density'`` prop)
* seeds : list of :class:`~fatiando.gravmag.harvester.Seed`
Lits of seeds used to start the growth process of the inversion. Use
:func:`~fatiando.gravmag.harvester.sow` to generate seeds.
* mesh : :class:`fatiando.mesher.PrismMesh`
The mesh used in the inversion. Will estimate the physical property
distribution on this mesh
* compactness : float
The compactness regularing parameter (i.e., how much should the estimate
be consentrated around the seeds). Must be positive. To find a good
value for this, start with a small value (like 0.001), run the inversion
and increase the value until desired compactness is achieved.
* threshold : float
Control how much the solution can grow (usually downward). In order for
estimate to grow by the accretion of 1 prism, this prism must decrease
the data misfit measure by *threshold* decimal percent. Depends on the
size of the cells in the *mesh* and the distance from a cell to the
observations. Use values between 0.001 and 0.000001.
If cells are small and *threshold* is large (0.001), the seeds won't
grow. If cells are large and *threshold* is small (0.000001), the seeds
will grow too much.
Returns:
* estimate, predicted_data : a dict and a list
*estimate* is a dict like::
{'physical_property':array, ...}
*estimate* contains the estimates physical properties. The properties
present in *estimate* are the ones given to the seeds. Include the
properties in the *mesh* using::
mesh.addprop('density', estimate['density'])
This way you can plot the estimate using :mod:`fatiando.vis.myv`.
*predicted_data* is a list of numpy arrays with the predicted (model)
data. The list is in the same order as *data*. To plot a map of the fit
for visual inspection and a histogram of the residuals::
from fatiando.vis import mpl
mpl.figure()
# Plot the observed and predicted data as contours for visual
# inspection
mpl.subplot(1, 2, 1)
mpl.axis('scaled')
mpl.title('Observed and predicted data')
levels = mpl.contourf(x, y, gz, (ny, nx), 10)
mpl.colorbar()
# Assuming gz is the only data used
mpl.contour(x, y, predicted[0], (ny, nx), levels)
# Plot a histogram of the residuals
residuals = gz - predicted[0]
mpl.subplot(1, 2, 2)
mpl.title('Residuals')
mpl.hist(residuals, bins=10)
mpl.show()
# It's also good to see the mean and standard deviation of the
# residuals
print "Residuals mean:", residuals.mean()
print "Residuals stddev:", residuals.std()
"""
log.info('Harvesting inversion results:')
nseeds = len(seeds)
log.info(' compactness: %g' % (compactness))
log.info(' threshold: %g' % (threshold))
log.info(' # of seeds: %d' % (nseeds))
log.info(' # of data types: %d' % (len(data)))
tstart = time.time()
# Initialize the estimate with the seeds
estimate = dict((s.i, s.props) for s in seeds)
# Initialize the neighbors list
neighbors = []
for seed in seeds:
neighbors.append(_get_neighbors(seed, neighbors, estimate, mesh, data))
# Initialize the predicted data
predicted = _init_predicted(data, seeds, mesh)
# Start the goal function, data-misfit function and regularizing function
totalgoal = _shapefunc(data, predicted)
totalmisfit = _misfitfunc(data, predicted)
regularizer = 0.
log.info(' initial goal function: %g' % (totalgoal))
log.info(' initial data misfit: %g' % (totalmisfit))
# Weight the regularizing function by the mean extent of the mesh
mu = compactness*1./(sum(mesh.shape)/3.)
# Begin the growth process
log.info(' Running...')
accretions = 0
for iteration in xrange(mesh.size - nseeds):
grew = False # To check if at least one seed grew (stopping criterion)
for s in xrange(nseeds):
best, bestgoal, bestmisfit, bestregularizer = _grow(neighbors[s],
data, predicted, totalmisfit, mu, regularizer, threshold)
# If there was a best, add to estimate, remove it, and add its
# neighbors
if best is not None:
if best.i not in estimate:
estimate[best.i] = {}
estimate[best.i].update(best.props)
totalgoal = bestgoal
totalmisfit = bestmisfit
regularizer = bestregularizer
for p, e in zip(predicted, best.effect):
p += e
neighbors[s].pop(best.i)
neighbors[s].update(
_get_neighbors(best, neighbors, estimate, mesh, data))
del best
grew = True
accretions += 1
if not grew:
break
log.info(' # of accretions: %d' % (accretions))
log.info(' final goal function: %g' % (totalgoal))
log.info(' final compactness regularizing function: %g' % (regularizer))
log.info(' final data misfit: %g' % (totalmisfit))
log.info(' time it took: %s' % (utils.sec2hms(time.time() - tstart)))
return _fmt_estimate(estimate, mesh.size), predicted
shape = (50, 50)
lons, lats, heights = gridder.regular(area, shape, z=250000)
start = time.time()
fields = [
gravmag.tesseroid.potential(lons, lats, heights, model),
gravmag.tesseroid.gx(lons, lats, heights, model),
gravmag.tesseroid.gy(lons, lats, heights, model),
gravmag.tesseroid.gz(lons, lats, heights, model),
gravmag.tesseroid.gxx(lons, lats, heights, model),
gravmag.tesseroid.gxy(lons, lats, heights, model),
gravmag.tesseroid.gxz(lons, lats, heights, model),
gravmag.tesseroid.gyy(lons, lats, heights, model),
gravmag.tesseroid.gyz(lons, lats, heights, model),
gravmag.tesseroid.gzz(lons, lats, heights, model)
]
print "Time it took: %s" % (utils.sec2hms(time.time() - start))
titles = [
'potential', 'gx', 'gy', 'gz', 'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz'
]
mpl.figure()
bm = mpl.basemap(area, 'ortho')
for i, field in enumerate(fields):
mpl.subplot(4, 3, i + 3)
mpl.title(titles[i])
mpl.contourf(lons, lats, field, shape, 15, basemap=bm)
bm.drawcoastlines()
mpl.colorbar()
mpl.show()
def run(ttimes, srcs, recs, mesh, solver=None, sparse=False, damping=0.0, smooth=0.0, sharp=0.0, beta=10.0 ** (-5)):
"""
Perform a 2D straight-ray travel-time tomography. Estimates the slowness
(1/velocity) of cells in mesh (because slowness is linear and easier)
Regularization is usually **not** optional. At least some amount of damping
is required.
Parameters:
* ttimes : array
The travel-times of the straight seismic rays.
* srcs : list of lists
List of the [x, y] positions of the sources.
* recs : list of lists
List of the [x, y] positions of the receivers.
* mesh : :class:`~fatiando.mesher.SquareMesh` or compatible
The mesh where the inversion (tomography) will take place.
* solver : function
A linear or non-linear inverse problem solver generated by a factory
function from a module of package :mod:`fatiando.inversion`. If None,
will use the default solver.
* sparse : True or False
If True, will use sparse matrices from `scipy.sparse`.
.. note:: If you provided a solver function, don't forget to turn on
sparcity in the inversion solver module **BEFORE** creating the
solver function! The usual way of doing this is by calling the
``use_sparse`` function. Ex:
``fatiando.inversion.gradient.use_sparse()``
.. warning:: Jacobian matrix building using sparse matrices isn't very
optimized. It will be slow but won't overflow the memory.
* damping : float
Damping regularizing parameter (i.e., how much damping to apply).
Must be a positive scalar.
* smooth : float
Smoothness regularizing parameter (i.e., how much smoothness to apply).
Must be a positive scalar.
* sharp : float
Sharpness (total variation) regularizing parameter (i.e., how much
sharpness to apply). Must be a positive scalar.
* beta : float
Total variation parameter. See
:class:`fatiando.inversion.regularizer.TotalVariation` for details
Returns:
* results : list = [slowness, residuals]:
* slowness : array
The slowness of each cell in *mesh*
* residuals : array
The inversion residuals (observed travel-times minus predicted
travel-times by the slowness estimate)
"""
if len(ttimes) != len(srcs) != len(recs):
msg = "Must have same number of travel-times, sources and receivers"
raise ValueError(msg)
if damping < 0:
raise ValueError("Damping must be positive")
# If no solver is given, generate custom ones
if solver is None:
if sharp == 0:
if sparse:
inversion.linear.use_sparse()
solver = inversion.linear.overdet(mesh.size)
else:
if sparse:
inversion.gradient.use_sparse()
solver = inversion.gradient.levmarq(initial=numpy.ones(mesh.size, dtype="f"))
log.info("Running 2D straight-ray travel-time tomography (SrTomo):")
log.info(" number of parameters: %d" % (len(mesh)))
log.info(" number of data: %d" % (len(ttimes)))
log.info(" use sparse matrices: %s" % (str(sparse)))
log.info(" regularizing parameters:")
log.info(" damping: %g" % (damping))
log.info(" smoothness: %g" % (smooth))
log.info(" sharpness: %g" % (sharp))
log.info(" beta (total variation aux parameter): %g" % (beta))
nparams = len(mesh)
# Make the data modules and regularizers
dms = [TravelTime(ttimes, srcs, recs, mesh, sparse)]
regs = []
if damping:
regs.append(inversion.regularizer.Damping(damping, nparams, sparse=sparse))
if smooth:
regs.append(inversion.regularizer.Smoothness2D(smooth, mesh.shape, sparse=sparse))
if sharp:
regs.append(inversion.regularizer.TotalVariation2D(sharp, mesh.shape, beta, sparse=sparse))
start = time.time()
try:
for i, chset in enumerate(solver(dms, regs)):
continue
except numpy.linalg.linalg.LinAlgError:
raise ValueError("Oops, the Hessian is a singular matrix." + " Try applying more regularization")
stop = time.time()
log.info(" number of iterations: %d" % (i))
log.info(" final data misfit: %g" % (chset["misfits"][-1]))
log.info(" final goal function: %g" % (chset["goals"][-1]))
log.info(" time: %s" % (utils.sec2hms(stop - start)))
slowness = chset["estimate"]
residuals = chset["residuals"][0]
return slowness, residuals
