def test_ricker(self):
x = array([0.88273185, 0.90950743, 0.9330604 , 0.95324475, 0.96993473,
0.9830259 , 0.99243608, 0.99810603, 1. , 0.99810603,
0.99243608, 0.9830259 , 0.96993473, 0.95324475, 0.9330604 ,
0.90950743])
y = ricker(0.064, 0.004, np.arange(1, 10))[1]
self.assertTrue(np.allclose(x, y))
def test_do_convolve( self ):
# Make a spike dataset [samp, trace, theta]
data = np.zeros( (1000,100,10) )
data[500,50,:] = 1.0
dt = 0.001
duration = .2
f = (20,40,60)
wavelets = ricker( duration, dt, f )
con = do_convolve( wavelets, data )
truth = np.zeros( (1000,100,10,3) )
for i in range(10):
truth[:wavelets.shape[0], 50, i, :] += wavelets
truth = np.roll( truth, 401, axis=0 )
self.assertTrue( np.allclose( truth, con ) )
def test_ricker(self):
x = array([
0.88273185, 0.90950743, 0.9330604, 0.95324475, 0.96993473,
0.9830259, 0.99243608, 0.99810603, 1., 0.99810603, 0.99243608,
0.9830259, 0.96993473, 0.95324475, 0.9330604, 0.90950743
])
y = ricker(0.064, 0.004, np.arange(1, 10))[1]
self.assertTrue(np.allclose(x, y))
def _make_synthetic(size=256, top=0.4, base=0.6, value=1, freq=25, phase=0):
"""Make a synthetic. Return the wavelet, the model, the RC, and the synthetic.
"""
v = np.ones(size) - value
v[int(top*size):int(base*size)] = value
rc = np.diff(v)
w = ricker(0.256, 0.001, freq)
if phase != 0:
w = rotate_phase(w, phase, degrees=True)
syn = np.convolve(rc, w, mode='same')
return w, v, rc, syn
def test_wavelet(self):
# Make a dipping reflectivity field
data = np.zeros((150, 100))
data[0, :] = np.arange(100) / 10.0
dips = np.arange(0, 100, 2)
dips = np.concatenate([dips, dips[::-1]])
window_size = 10
freq = 20.0
duration = 1
wavelet = ricker(window_size, duration, freq)
for i in range(dips.size):
data[:, i] = np.convolve(wavelet,
np.roll(data[:, i], dips[i]),
mode='same')
stepout = 1
maxlag = 2
dip = dipsteer(data,
window_size,
stepout=stepout,
maxlag=maxlag,
overlap=0.5)
undipped = np.zeros(dips.shape)
for i in range(dips.size):
undipped[i] = (dip[dips[i], i])
check = undipped[maxlag * stepout:-maxlag * stepout]
test = np.zeros(check.shape)
test[0:test.size // 2 - 1] = -2.0
test[(test.size // 2) - 1] = -1
test[(test.size // 2)] = 1
test[(test.size // 2) + 1:] = 2
self.assertTrue(np.allclose(test, check))
def test_do_convolve(self):
# Make a spike dataset [samp, trace, theta]
data = np.zeros((1000, 100, 10))
data[500, 50, :] = 1.0
dt = 0.001
duration = .2
f = (20, 40, 60)
wavelets = ricker(duration, dt, f)
con = do_convolve(wavelets, data)
truth = np.zeros((1000, 100, 10, 3))
for i in range(10):
truth[:wavelets.shape[0], 50, i, :] += wavelets
truth = np.roll(truth, 401, axis=0)
self.assertTrue(np.allclose(truth, con))
def test_wavelet(self):
# Make a dipping reflectivity field
data = np.zeros((150, 100))
data[0, :] = np.arange(100) / 10.0
dips = np.arange(0, 100, 2)
dips = np.concatenate([dips, dips[::-1]])
window_size = 10
freq = 20.0
duration = 1
wavelet = ricker(window_size, duration, freq)
for i in range(dips.size):
data[:, i] = np.convolve(wavelet,
np.roll(data[:, i], dips[i]),
mode='same')
stepout = 1
maxlag = 2
dip = dipsteer(data, window_size, stepout=stepout,
maxlag=maxlag, overlap=0.5)
undipped = np.zeros(dips.shape)
for i in range(dips.size):
undipped[i] = (dip[dips[i], i])
check = undipped[maxlag*stepout:-maxlag*stepout]
test = np.zeros(check.shape)
test[0: test.size//2 - 1] = -2.0
test[(test.size//2) - 1] = -1
test[(test.size//2)] = 1
test[(test.size//2)+1:] = 2
self.assertTrue(np.allclose(test, check))
# the properties of the upper layer are an average from # the shale points in the well vp0, vs0, rho0 = L[['VP', 'VS', 'RHO']][sh].mean().values # the properties of the lower layer come from RPM phi, vsh = .15, .8 _, _, K0 = vrh(vsh, K_qz, K_sh) _, _, MU0 = vrh(vsh, MU_qz, MU_sh) RHO0 = vsh * RHO_qz + (1 - vsh) * RHO_sh Kdry, MUdry = softsand(K0, MU0, phi, phic=.5, Cn=12, P=45, f=.3) vp1, vs1, rho1, _ = vels(Kdry, MUdry, K0, RHO0, K_g, RHO_g, phi) n_samples = 500 interface = int(n_samples / 2) ang = np.arange(31) wavelet = ricker(.25, 0.001, 25) model_ip, model_vpvs, rc0, rc1 = (np.zeros(n_samples) for _ in range(4)) model_z = np.arange(n_samples) model_ip[:interface] = vp0 * rho0 model_ip[interface:] = vp1 * rho1 model_vpvs[:interface] = np.true_divide(vp0, vs0) model_vpvs[interface:] = np.true_divide(vp1, vs1) avo = shuey2(vp0, vs0, rho0, vp1, vs1, rho1, ang) rc0[interface] = avo[0] rc1[interface] = avo[-1] synt0 = np.convolve(rc0, wavelet, mode='same') synt1 = np.convolve(rc1, wavelet, mode='same') clip = np.max(np.abs([synt0, synt1])) clip += clip * .2
def twolayer(vp0,vs0,rho0,vp1,vs1,rho1):
from bruges.reflection import shuey2 # TODO: install bruges.reflection
from bruges.filters import ricker # TODO: install bruges.filters
n_samples = 500
interface=int(n_samples/2)
ang=np.arange(31)
wavelet=ricker(.25, 0.001, 25)
model_ip,model_vpvs,rc0,rc1 = ( np.zeros(n_samples) for _ in range(4) )
model_z = np.arange(n_samples)
model_ip[:interface]=vp0*rho0
model_ip[interface:]=vp1*rho1
model_vpvs[:interface]=np.true_divide(vp0,vs0)
model_vpvs[interface:]=np.true_divide(vp1,vs1)
avo=shuey2(vp0,vs0,rho0,vp1,vs1,rho1,ang)
rc0[interface]=avo[0]
rc1[interface]=avo[-1]
synt0=np.convolve(rc0,wavelet,mode='same')
synt1=np.convolve(rc1,wavelet,mode='same')
clip=np.max(np.abs([synt0, synt1]))
clip += clip*.2
opz0={'color':'b', 'linewidth':4}
opz1={'color':'k', 'linewidth':2}
opz2={'linewidth':0, 'alpha':0.5}
f = plt.subplots(figsize=(10, 4))
ax0 = plt.subplot2grid((1,16), (0,0), colspan=2) # ip
ax1 = plt.subplot2grid((1,16), (0,2), colspan=2) # vp/vs
ax2 = plt.subplot2grid((1,16), (0,4), colspan=2) # synthetic @ 0 deg
ax3 = plt.subplot2grid((1,16), (0,6), colspan=2) # synthetic @ 30 deg
ax4 = plt.subplot2grid((1,16), (0,9), colspan=7) # avo curve
ax0.plot(model_ip, model_z, **opz0)
ax0.set_xlabel('IP')
ax0.locator_params(axis='x', nbins=2)
ax1.plot(model_vpvs, model_z, **opz0)
ax1.set_xlabel('VP/VS')
ax1.locator_params(axis='x', nbins=2)
ax2.plot(synt0, model_z, **opz1)
ax2.fill_betweenx(model_z, 0, synt0, where=synt0>0, facecolor='black', **opz2)
ax2.set_xlim(-clip,clip)
ax2.set_xlabel('angle={:.0f}'.format(ang[0]))
ax2.locator_params(axis='x', nbins=2)
ax3.plot(synt1, model_z, **opz1)
ax3.fill_betweenx(model_z, 0, synt1, where=synt1>0, facecolor='black', **opz2)
ax3.set_xlim(-clip,clip)
ax3.set_xlabel('angle={:.0f}'.format(ang[-1]))
ax3.locator_params(axis='x', nbins=2)
ax4.plot(ang, avo, **opz0)
ax4.axhline(0, color='k', lw=2)
ax4.set_xlabel('angle of incidence')
ax4.tick_params(which='major', labelsize=8)
for aa in [ax0,ax1,ax2,ax3]:
aa.set_ylim(350,150)
aa.tick_params(which='major', labelsize=8)
aa.set_yticklabels([])
plt.subplots_adjust(wspace=.8,left=0.05,right=0.95)
# QC the calculation - pass
test0 = model_AI[0, 0, 0] - model_den[0, 0, 0] * model_Vp[0, 0, 0]
test1 = model_SI[0, 0, 0] - model_den[0, 0, 0] * model_Vs[0, 0, 0]
# Test data save/load
model_path = r'C:\Users\N.Srisutthiyakorn\OneDrive - Shell\Documents\GitHub\ShellHackathon\SampleInputs\IntermediateSteps'
model_fn_out = os.path.join(model_path, 'density_output_test.npy')
np.save(model_fn_out, model_den)
model_den_qc = np.load(model_fn_out)
# =============================================================================
# Test wavelet
from bruges.filters import ricker
delta_t = 0.001 # sample rate of the data
wavelet, tw = ricker(0.128, delta_t, 25.0, return_t=True)
plt.plot(wavelet, '.')
# =============================================================================
# Test synthetic models
from bruges.reflection import reflectivity
theta = np.arange(46)
model_Vp_trace = model_Vp[:, 0, 0]
model_Vs_trace = model_Vs[:, 0, 0]
model_den_trace = model_den[:, 0, 0]
rc_theta = reflectivity(model_Vp_trace, model_Vs_trace, model_den_trace,
theta).T
plt.imshow(rc_theta.real, aspect='auto')
tick.label.set_fontsize(fs)
for tick in axarr[1,p].yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
#plot horizontal green line on model image, and steady state
axarr[0,p].axhline(y=t, alpha=0.5, lw=2, color = 'g')
fig.tight_layout()
return get_figure_data()
if __name__ == '__main__':
dt =0.001
gain = 1
temp = ricker(0.256,dt,25)
ntraces=50
data =np.zeros((temp.shape[0],ntraces))
for i in range(ntraces):
temp = ricker(0.256,dt,25+10*i)
data[:,i] = temp
wiggle(data,dt,skipt=1,gain=gain)
axarr[1, p].set_ylabel('amplitude', fs)
axarr[1, p].grid()
axarr[1, p].set_xlim(xax[0], xax[-1])
for tick in axarr[1, p].xaxis.get_major_ticks():
tick.label.set_fontsize(fs)
for tick in axarr[1, p].yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
#plot horizontal green line on model image, and steady state
axarr[0, p].axhline(y=t, alpha=0.5, lw=2, color='g')
fig.tight_layout()
return get_figure_data()
if __name__ == '__main__':
dt = 0.001
gain = 1
temp = ricker(0.256, dt, 25)
ntraces = 50
data = np.zeros((temp.shape[0], ntraces))
for i in range(ntraces):
temp = ricker(0.256, dt, 25 + 10 * i)
data[:, i] = temp
wiggle(data, dt, skipt=1, gain=gain)
# the properties of the upper layer are an average from # the shale points in the well vp0, vs0, rho0 = L[['VP', 'VS', 'RHO']][sh].mean().values # the properties of the lower layer come from RPM phi, vsh = .15, .8 _, _, K0 = vrh(vsh, K_qz, K_sh) _, _, MU0 = vrh(vsh, MU_qz, MU_sh) RHO0 = vsh*RHO_qz+(1-vsh)*RHO_sh Kdry, MUdry = softsand(K0, MU0, phi, phic=.5, Cn=12, P=45, f=.3) vp1, vs1, rho1, _ = vels(Kdry, MUdry, K0, RHO0, K_g, RHO_g, phi) n_samples = 500 interface = int(n_samples/2) ang = np.arange(31) wavelet = ricker(.25, 0.001, 25) model_ip, model_vpvs, rc0, rc1 = (np.zeros(n_samples) for _ in range(4)) model_z = np.arange(n_samples) model_ip[:interface] = vp0*rho0 model_ip[interface:] = vp1*rho1 model_vpvs[:interface] = np.true_divide(vp0, vs0) model_vpvs[interface:] = np.true_divide(vp1, vs1) avo = shuey2(vp0, vs0, rho0, vp1, vs1, rho1, ang) rc0[interface] = avo[0] rc1[interface] = avo[-1] synt0 = np.convolve(rc0, wavelet, mode='same') synt1 = np.convolve(rc1, wavelet, mode='same') clip = np.max(np.abs([synt0, synt1])) clip += clip*.2
