def empymod_walktem(system, model1d):
"""Custom wrapper of empymod.model.bipole.
Here, we calculate WalkTEM data using the ``empymod.model.bipole`` routine
as an example. We could achieve the same using ``empymod.model.dipole`` or
``empymod.model.loop``.
We model the big source square loop by calculating only half of one side of
the electric square loop and approximating the finite length dipole with 3
point dipole sources. The result is then multiplied by 8, to account for
all eight half-sides of the square loop.
The implementation here assumes a central loop configuration, where the
receiver (1 m2 area) is at the origin, and the source is a 40x40 m electric
loop, centered around the origin.
Note: This approximation of only using half of one of the four sides
obviously only works for central, horizontal square loops. If your
loop is arbitrary rotated, then you have to model all four sides of
the loop and sum it up.
Parameters
----------
Returns
-------
WalkTEM : EMArray
WalkTEM response (dB/dt).
"""
depth = np.r_[0.0, model1d.depth[:-1]]
res = np.r_[2e14, model1d.par]
# === CALCULATE FREQUENCY-DOMAIN RESPONSE ===
# We only define a few parameters here. You could extend this for any
# parameter possible to provide to empymod.model.bipole.
length = 0.5 * system.transmitterLoop.sideLength
EM = bipole(
src=[0.0, length, length, length, 0.0,
0.0], # El. bipole source; half of one side.
rec=[0, 0, 0, 0, 90], # Receiver at the origin, vertical.
depth=depth, # Depth-model, adding air-interface.
res=res, # Provided resistivity model, adding air.
# aniso=aniso, # Here you could implement anisotropy...
# # ...or any parameter accepted by bipole.
freqtime=system.modellingFrequencies, # Required frequencies.
mrec=True, # It is an el. source, but a magn. rec.
strength=8, # To account for 4 sides of square loop.
srcpts=5, # Approx. the finite dip. with 3 points.
htarg={'fhtfilt': 'key_101_2009'},
verb=0, # Short filter, so fast.
)
# Multiply the frequecny-domain result with
# \mu for H->B, and i\omega for B->dB/dt.
EM *= 2j * np.pi * system.modellingFrequencies * 4e-7 * np.pi
# Apply filters the data for the given system
for filt in system.offTimeFilters:
EM *= filt.frequencyResponse(system.modellingFrequencies)
# === CONVERT TO TIME DOMAIN ===
EM, _ = np.squeeze(
tem(EM[:, None], np.array([1]), system.modellingFrequencies,
system.modellingTimes, -1, system.ft, system.ftarg))
# === APPLY WAVEFORM ===
return waveform(system.modellingTimes, EM, system.times,
system.waveform.time - system.delayTime,
system.waveform.amplitude)
def test_tem():
# Just ensure functionality stays the same, with one example.
for i in ['6', '7', '8']: # Signal = 0, 1, -1
res = DATAFEMTEM['out' + i][()]
tEM, _ = tem(**res['inp'])
assert_allclose(tEM, res['EM'])
