# correspond to the selection criteria
# - *alpha*, *corr_length* control the spatial smoothing term
# - *beta*, *lambda_* control the weighted norm penalization term
#
# Note that if no value is given for some parameter, then the
# inversion will use the default value defined in the configuration
# file.
#
# (See doc of VelocityMap for a complete description of the input
# arguments.)
v = pstomo.VelocityMap(dispersion_curves=curves,
period=period,
verbose=False,
lonstep=lon_step,
latstep=lat_step,
minspectSNR=minspectSNR,
correlation_length=corr_length,
alpha=alpha,
beta=beta,
lambda_=lambda_)
# creating a figure summing up the results of the inversion:
# - 1st panel = map of velocities or velocity anomalies
# - 2nd panel = map of interstation paths and path densities
# - 3rd panel = resolution map
#
# See doc of VelocityMap.plot(), VelocityMap.plot_velocity(),
# VelocityMap.plot_pathdensity(), VelocityMap.plot_resolution()
# for a detailed description of the input arguments.
title = (
# - *beta*, *lambda_* control the weighted norm penalization term
#
# Note that if no value is given for some parameter, then the
# inversion will use the default value defined in the configuration
# file.
#
# (See doc of VelocityMap for a complete description of the input
# arguments.)
try:
v = pstomo.VelocityMap(dispersion_curves=curves,
period=period,
skipstations=SKIP_STATIONS,
skippairs=skippairs,
verbose=False,
lonstep=GRID_STEPS[passnb],
latstep=GRID_STEPS[passnb],
minspectSNR=MINPECTSNRS[passnb],
correlation_length=CORR_LENGTHS[passnb],
alpha=ALPHAS[passnb],
beta=BETAS[passnb],
lambda_=LAMBDAS[passnb])
except CannotPerformTomoInversion as err:
print("Cannot perform tomo inversion: {}".format(err))
for fig in periodfigs:
plt.close(fig)
# next period
break
if passnb == 0:
# pairs whose residual is > 3 times the std dev of
# the residuals are rejected from the next pass
