import urllib
import time
import numpy
from fatiando.mesher import SquareMesh
from fatiando.seismic import ttime2d, srtomo
from fatiando.inversion.regularization import Damping
from fatiando.vis import mpl
from fatiando import utils
area = (0, 500000, 0, 500000)
shape = (30, 30)
model = 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
seed = 0 # Set the random seed so that points are the same everythime
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, 0.01, percent=True, return_stddev=True)
# Make the mesh
mesh = SquareMesh(area, shape)
# and run the inversion
start = time.time()
tomo = srtomo.SRTomo(tts, srcs, recs, mesh) + 10**6*Damping(mesh.size)
estimate = tomo.fit().estimate_
residuals = tomo.residuals()
from os import path
import numpy
from fatiando.mesher import SquareMesh
from fatiando.seismic import ttime2d, srtomo
from fatiando.inversion.regularization import TotalVariation2D
from fatiando.vis import mpl
from fatiando import utils
area = (0, 100000, 0, 100000)
shape = (20, 20)
model = SquareMesh(area, shape)
# Fetch the image from the online docs
urllib.urlretrieve(
'http://fatiando.readthedocs.org/en/latest/_static/logo.png', 'logo.png')
vmin, vmax = 4000, 10000
model.img2prop('logo.png', vmin, vmax, 'vp')
# Make some travel time data and add noise
seed = 0 # Set the random seed so that points are the same everythime
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, 0.01, percent=True, return_stddev=True)
# Make the mesh
mesh = SquareMesh(area, shape)
# and run the inversion
tomo = (srtomo.SRTomo(tts, srcs, recs, mesh) +
1*TotalVariation2D(10**-8, mesh.shape))
# Since Total Variation is a non-linear function, then the tomography becomes
# non-linear. So we need to configure fit to use the Levemberg-Marquardt
