Note
Click here to download the full example code
Refraction Manager¶
This example shows how to use the Refraction manager to generate the response of a three-layered sloping model and to invert the synthetic noisified data.
import numpy as np
import pygimli as pg
import pygimli.meshtools as mt
from pygimli.physics import TravelTimeManager
We start by creating a three-layered slope (The model is taken from the BSc thesis of Constanze Reinken (University of Bonn).
layer1 = mt.createPolygon([[0.0, 137], [117.5, 164], [117.5, 162], [0.0, 135]],
isClosed=True, marker=1, area=1)
layer2 = mt.createPolygon([[0.0, 126], [0.0, 135], [117.5, 162], [117.5, 153]],
isClosed=True, marker=2)
layer3 = mt.createPolygon([[0.0, 110], [0.0, 126], [117.5, 153], [117.5, 110]],
isClosed=True, marker=3)
slope = (164 - 137) / 117.5
geom = layer1 + layer2 + layer3
# If you want no sloped flat earth geometry .. comment out the next 2 lines
# geom = mt.createWorld(start=[0.0, 110], end=[117.5, 137], layers=[137-2, 137-11])
# slope = 0.0
pg.show(geom)
mesh = mt.createMesh(geom, quality=34.3, area=3, smooth=[1, 10])
pg.show(mesh)
Out:
(<matplotlib.axes._subplots.AxesSubplot object at 0x7f4b6aa506d8>, None)
Next we define geophone positions and a measurement scheme, which consists of shot and receiver indices.
numberGeophones = 48
sensors = np.linspace(0., 117.5, numberGeophones)
scheme = pg.physics.traveltime.createRAData(sensors)
# Adapt sensor positions to slope
pos = np.array(scheme.sensors())
for x in pos[:, 0]:
i = np.where(pos[:, 0] == x)
new_y = x * slope + 137
pos[i, 1] = new_y
scheme.setSensors(pos)
Now we initialize the TravelTime manager and asssign P-wave velocities to the layers. To this end, we create a map from cell markers 0 through 3 to velocities (in m/s) and generate a velocity vector. To check whether the model looks correct, we plot it along with the sensor positions.

We use this model to create noisified synthetic data and look at the traveltime data matrix. Note, we force a specific noise seed as we want reproducable results for testing purposes. TODO: show first arrival traveltime curves.
data = mgr.simulate(slowness=1.0 / vp, scheme=scheme, mesh=mesh,
noiseLevel=0.001, noiseAbs=0.001, seed=1337,
verbose=True)
mgr.showData(data)

Out:
((<matplotlib.image.AxesImage object at 0x7f4b6b498c88>, <matplotlib.colorbar.Colorbar object at 0x7f4b6b874da0>), None)
Now we invert the synthetic data. We need a new independent mesh without information about the layered structure. This mesh can be created manual or guessd automatic from the data sensor positions (in this example). We tune the maximum cell size in the parametric domain to 15m²
vest = mgr.invert(data, secNodes=2, paraMaxCellSize=15.0,
maxIter=10, verbose=True)
np.testing.assert_array_less(mgr.inv.inv.chi2(), 1.1)
Out:
fop: <pygimli.physics.traveltime.TravelTimeManager.TravelTimeDijkstraModelling object at 0x7f4b6aa3c2d0>
Data transformation: <pygimli.core._pygimli_.RTrans object at 0x7f4b6aa3c308>
Model transformation: <pygimli.core._pygimli_.RTransLog object at 0x7f4b6aa3c618>
min/max (data): 0.0069/0.14
min/max (error): 0.80%/14.67%
min/max (start model): 2.0e-04/0.0020
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
inv.iter 2 ... chi² = 2.34 (dPhi = 40.15%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 3 ... chi² = 1.77 (dPhi = 23.18%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 4 ... chi² = 1.39 (dPhi = 20.72%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 5 ... chi² = 1.2 (dPhi = 13.63%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 6 ... chi² = 1.02 (dPhi = 13.47%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 7 ... chi² = 0.98 (dPhi = 4.35%) lam: 20.0
################################################################################
# Abort criterion reached: chi² <= 1 (0.98) #
################################################################################
The manager also holds the method showResult that is used to plot the result. Note that only covered cells are shown by default. For comparison we plot the geometry on top.
ax, _ = mgr.showResult(cMin=min(vp), cMax=max(vp), logScale=False)
pg.show(geom, ax=ax, fillRegion=False, regionMarker=False)

Out:
(<matplotlib.axes._subplots.AxesSubplot object at 0x7f4b6b88ae10>, None)
Note that internally the following is called
ax, _ = pg.show(ra.mesh, vest, label="Velocity [m/s]", **kwargs)
Another useful method is to show the model along with its response on the data.
mgr.showResultAndFit(cMin=min(vp), cMax=max(vp))

Out:
<Figure size 640x480 with 6 Axes>
Note
Takeaway message
# A default data inversion with checking of the data consists of only few
# lines. Check out :ref:`ex:koenigsee`.
Total running time of the script: ( 0 minutes 33.862 seconds)