def get_models(args):
if args.test_checkpoint is None:
inverse_net = inverse_model(in_channels=len(args.incident_angles),
nonlinearity=args.nonlinearity)
else:
try:
inverse_net = torch.load(args.test_checkpoint)
except FileNotFoundError:
print(
"No checkpoint found at '{}'- Please specify the model for testing"
.format(args.test_checkpoint))
exit()
#Set up forward model
# For wavelet info, refer to https://github.com/agile-geoscience/bruges/blob/master/bruges/filters/wavelets.py
# For simpicity, the same wavlet is used for all incident angles
wavelet, wavelet_time = wavelets.ormsby(args.wavelet_duration,
args.dt,
args.f,
return_t=True)
wavelet = torch.tensor(wavelet).unsqueeze(dim=0).unsqueeze(dim=0).float()
forward_net = forward_model(wavelet=wavelet)
if torch.cuda.is_available():
inverse_net.cuda()
forward_net.cuda()
return inverse_net, forward_net
# Uncomment this next line if you want to save the figure
# fig02.savefig('figure_1.png', dpi=500)
phase_spec = np.angle(np.fft.rfft(s))
plt.plot(f, phase_spec, 'm')
plt.yticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])
plt.xlabel('frequency (Hz)', fontsize=14)
plt.ylabel('phase', fontsize=14)
plt.show()
from bruges.filters.wavelets import ormsby, ricker
duration = 0.256 # seconds
orms = ormsby(duration, dt=0.002, f=freqs)
tw = np.arange(-len(orms)//2,len(orms)//2,1)*dt
fig03 =plt.figure(figsize=(8,3))
ax = fig03.add_subplot(111)
ax.plot(tw, orms, 'blue', lw=2, alpha=0.75,
label='Ormsby (%i-%i-%i-%i Hz)' %(freqs[0],freqs[1],freqs[2],freqs[3]))
ax.legend(loc=1)
ax.set_xlim(-0.064,0.064)
ax.set_xlabel('time (s)', fontsize=14)
ax.set_ylabel('amplitude', fontsize=14)
ax.grid()
plt.show()
# Wavelet estimation by autocorrelation
dw = 64 # number of samples to display on either side of zero
def change_phase(w, phase, dt, degrees=False):
# seismic = np.load('wavelet-est-SEG/seismic.npy')
spec = np.fft.rfft(w, len(w))
comp = spec * np.exp(1j * phase)
w_new = np.fft.ifft(comp, n=len(w))
return w_new
###############################################################################
# Run code! Run!!
global freq, RC, seis, w
RC = np.load("wavelet-est-SEG/rpp.npy")
seis = np.load("wavelet-est-SEG/seismic.npy")
dt = .002 # sample rate in seconds
t = np.arange(seis.size) * dt
###############################################################################
# create wavelet
freqs = np.array([5, 8, 130, 160])
w = wv.ormsby(0.5, dt, freqs)
x0 = np.deg2rad(np.arange(0, 90, len(w)))
res = _minimize_pso(objWavelet, x0)