In this short post I show how to adapt
Agile Scientific‘s Python tutorial x lines of code, Wedge model and adapt it to make 100 synthetic models in one shot: X impedance models times X wavelets times X random noise fields (with I vertical fault).
You can download the notebook with the full code from GitHub. N.B. the code is optimized for Python 2.7.
I begin by making a 6-layer model, shown in Figure 1:
model = numpy.zeros((50,49), dtype=np.int)
model[8:16,:] = 1
model[16:24,:] = 2
model[24:32,:] = 3
model[32:40,:] = 4
model[40:,:] = 5
Figure 1. Initial 6-layer model
next I make some Vp-rho pairs (rock 0, rock 1, … , rock5):
rocks = numpy.array([[2700, 2750], # Vp, rho
and then create 10 slightly different variations of the Vp-rho pairs one of which are is shown in Figure 2:
rnd = numpy.random.rand(10,6,2)*0.2
manyrocks = np.array([rocks + rocks*rn for rn in rnd], dtype=np.int)
earth = manyrocks[model]
Figure 2. A Vp-rho pair (earth model)
at which point I can combine Vp-rho pairs to make 10 impedance models, then insert a vertical fault with:
impedances = [np.apply_along_axis(np.product, -1, e).astype(float) for e in earth]# Python 2
faulted = copy.deepcopy(impedances)
for r, i in zip(faulted, np.arange(len(faulted))):
temp = np.array(r)
rolled = np.roll(np.array(r[:,:24]), 4, axis = 0)
Figure 3. Four faulted impedance models.
next I calculate reflection coefficients (Figure 4)and convolve them with a list of 10 Ricker wavelets (generated using
Agile’s Bruges) to make synthetic seismic models, shown in Figure 5. rc = [(flt[1:,:] - flt[:-1,:]) / (flt[1:,:] + flt[:-1,:]) for flt in faulted]
ws = [ bruges . filters . ricker ( duration = 0.098 , dt = 0.002 , f = fr )
for fr in [ 35 , 40 , 45 , 50 , 55 , 60 , 65 , 70 , 75 , 80 ]]
synth = np . array([ np . apply_along_axis( lambda t: np . convolve( t, w, mode = 'same'), axis = 0,
arr = r) for r in rc for w in ws ])
Figure 4. Four reflection coefficients series.
Figure 5. Four synthetic seismic models with vertical fault.
The last bit is the addition of noise, with the result is shown in Figure 6:
blurred = sp.ndimage.gaussian_filter(synth, sigma=1.1)
noisy = blurred + 0.5 * blurred.std() * np.random.random(blurred.shape)
Figure 6. Four synthetic seismic models with vertical fault and noise.
, let me know if you find this useful or if you come up with more modeling ideas. The notebook with the full code is on GitHub
UPDATE, April 22, 2019.
I received an email from a reader, Will, asking some clarification about this article and the making of many impedance models. I’m re-posting here what I wrote back.
I think the key to understand this is how we multiply velocity by density in each of the possible earth model.
Looking at the notebook, the earth model array has shape:
print ( np . shape ( earth ))
>>> (10, 50, 49, 2)
with the last axis having dimension 2: one Vp and one Rho, so in summary 10 models of size 50×49, each with a Vp and a Rho.
So with this other block of code:
impedances = [ np . apply_along_axis ( np . product ,
- 1 , e ) . astype ( float ) for e in earth ]
numpy.apply_along_axis to multiply Vp by Rho along the last dimension,
-1 , using
numpy.product, and the list comprehension
[... for e in earth] to do it for all models in the array
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