tr1 = read("preprocessed_data/SULZ.LHZ.CH.2013.219.processed.SAC")[0]
tr2 = read("preprocessed_data/VDL.LHZ.CH.2013.219.processed.SAC")[0]
# bad example with only one day of correlation
dist, az, baz = gps2dist_azimuth(tr1.stats.sac.stla, tr1.stats.sac.stlo,
tr2.stats.sac.stla, tr2.stats.sac.stlo)
freq, xcorr, n_corr_wins = noise.noisecorr(tr1,
tr2,
window_length=3600.,
overlap=0.5)
smoothed = noise.velocity_filter(freq,
xcorr,
dist / 1000.,
velband=(6.0, 5.0, 1.5, 0.5),
return_all=False)
crossings,phase_vel = noise.extract_phase_velocity(freq,smoothed,dist/1000.,ref_curve,\
freqmin=0.004,freqmax=0.25, min_vel=1.5, max_vel=5.0,min_amp=0.0,\
horizontal_polarization=False, smooth_spectrum=False,plotting=True)
plt.figure(figsize=(16, 10))
plt.subplot(2, 2, 1)
plt.plot(freq, np.real(xcorr), label='original')
plt.plot(freq, np.real(smoothed), 'r', lw=2, label='smoothed')
plt.title("Cross-correlation spectrum")
plt.xlabel("Frequency")
plt.legend(numpoints=1)
plt.subplot(2, 2, 2)
corr_spectrum = []
no_spectra = 0
for corrday in corr_list:
if corrday == None:
continue
if len(corr_spectrum) == 0:
freq = corrday[0]
corr_spectrum = corrday[1]
else:
corr_spectrum += corrday[1]
no_spectra += 1
corr_spectrum /= no_spectra
smoothed = noise.velocity_filter(freq,
corr_spectrum,
dist / 1000.,
velband=(6.0, 5.5, 1.5, 0.5),
return_all=False)
crossings, phase_vel = noise.get_smooth_pv(freq,
smoothed,
dist / 1000.,
ref_curve,
freqmin=0.004,
freqmax=0.25,
min_vel=1.5,
max_vel=5.5,
filt_width=3,
filt_height=1.0,
pick_threshold=2.0,
horizontal_polarization=True,
ref_curve = np.loadtxt("avg.txt")
tr1 = read("/Users/tzompantli/Desktop/tr1.mseed")[0]
tr2 = read("/Users/tzompantli/Desktop/tr2.mseed")[0]
# bad example with only one day of correlation
#dist,az,baz = gps2dist_azimuth(tr1.stats.sac.stla,tr1.stats.sac.stlo,
# tr2.stats.sac.stla,tr2.stats.sac.stlo)
dist = 150
freq, xcorr = noise.noisecorr(tr1, tr2, window_length=30., overlap=0.3)
smoothed = noise.velocity_filter(freq,
xcorr,
dist / 1000.,
cmin=.1,
cmax=1.0,
return_all=False)
cc = np.real(np.fft.fftshift(np.fft.ifft(smoothed)))
tr = Trace(data=cc)
tr.stats.sampling_rate = 50
tr.write('test.mseed', format='MSEED')
dkjd
crossings,phase_vel = noise.extract_phase_velocity(freq,smoothed,dist/1000.,ref_curve,\
freqmin=1,freqmax=20, min_vel=.01, max_vel=2.0,min_amp=0.0,\
horizontal_polarization=False, smooth_spectrum=False,plotting=False)#True)
plt.figure(figsize=(16, 10))
print "starting FFT with", no_cores, "cores"
p = Pool(no_cores)
corr_list = p.map(multiprocess_fft, joblist)
p.close()
freq = corr_list[0][0]
corr_spectrum = np.zeros(len(corr_list[0][0]), dtype=complex)
for corrday in corr_list:
if corrday == None:
continue
corr_spectrum += corrday[1]
corr_spectrum /= float(len(corr_list))
smoothed = noise.velocity_filter(freq,
corr_spectrum,
dist / 1000.,
cmin=1.5,
cmax=5.5,
return_all=False)
crossings,phase_vel = noise.get_smooth_pv(freq,smoothed,dist/1000.,ref_curve,\
freqmin=0.004,freqmax=0.25, min_vel=1.5, max_vel=5.5,\
filt_width=3,filt_height=0.2,x_overlap=0.75,y_overlap=0.75,pick_threshold=1.7,\
horizontal_polarization=False, smooth_spectrum=False,plotting=True)
plt.figure(figsize=(16, 10))
plt.subplot(2, 2, 1)
plt.plot(freq, np.real(corr_spectrum))
plt.plot(freq, np.real(smoothed))
plt.title("Cross-correlation spectrum Love")
plt.xlabel("Frequency")
plt.subplot(2, 2, 2)