def test_general():
"""
General test of the class SRTomo, as it is in the docs of this class.
"""
model = SquareMesh((0, 10, 0, 10), shape=(2, 1), props={'vp': [2., 5.]})
src = (5, 0)
srcs = [src, src]
recs = [(0, 0), (5, 10)]
ttimes = ttime2d.straight(model, 'vp', srcs, recs)
mesh = SquareMesh((0, 10, 0, 10), shape=(2, 1))
tomo = srtomo.SRTomo(ttimes, srcs, recs, mesh)
assert_array_almost_equal(tomo.fit().estimate_, np.array([2., 5.]), 9)
def test_jacobian():
"""
srtomo.SRTomo.jacobian return the jacobian of the model provided. In this
simple model, the jacobian can be easily calculated.
"""
model = SquareMesh((0, 10, 0, 10), shape=(2, 1), props={'vp': [2., 5.]})
src = (5, 0)
srcs = [src, src]
recs = [(0, 0), (5, 10)]
ttimes = ttime2d.straight(model, 'vp', srcs, recs)
mesh = SquareMesh((0, 10, 0, 10), shape=(2, 1))
tomo = srtomo.SRTomo(ttimes, srcs, recs, mesh)
assert_array_almost_equal(tomo.jacobian().todense(),
np.array([[5., 0.], [5., 5.]]), 9)
def test_predicted():
"""
Test to verify srtomo.SRTomo.predicted function. Given the correct
parameters, this function must return the result of the forward data.
"""
model = SquareMesh((0, 10, 0, 10), shape=(2, 1), props={'vp': [2., 5.]})
src = (5, 0)
srcs = [src, src]
recs = [(0, 0), (5, 10)]
ttimes = ttime2d.straight(model, 'vp', srcs, recs)
mesh = SquareMesh((0, 10, 0, 10), shape=(2, 1))
tomo = srtomo.SRTomo(ttimes, srcs, recs, mesh)
# The parameter used inside the class is slowness, so 1/vp.
tomo.p_ = np.array([1. / 2., 1. / 5.])
assert_array_almost_equal(tomo.predicted(), ttimes, 9)
"""
Seismic: 2D straight-ray tomography using smoothness regularization
"""
import numpy as np
from fatiando.mesher import SquareMesh
from fatiando.seismic import ttime2d, srtomo
from fatiando.inversion.regularization import Smoothness2D, LCurve
from fatiando.vis import mpl
from fatiando import utils
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = SquareMesh(area, shape)
vel = 4000 * np.ones(shape)
vel[5:25, 5:25] = 10000
model.addprop('vp', vel.ravel())
# Make some travel time data and add noise
seed = 0 # Set the random seed so that points are the same every time
src_loc = utils.random_points(area, 80, seed=seed)
rec_loc = utils.circular_points(area, 30, random=True, seed=seed)
srcs, recs = utils.connect_points(src_loc, rec_loc)
tts = ttime2d.straight(model, 'vp', srcs, recs)
tts, error = utils.contaminate(tts,
0.02,
percent=True,
return_stddev=True,
seed=seed)
# Make the mesh
mesh = SquareMesh(area, shape)
# and run the inversion
