#process the quadtree file
#home='/Users/dmelgar/Slip_inv/'
#project_name='Ixtepec'
#file_in=u'/Users/dmelgar/Ixtepec2017/InSAR/T107_Ascending/ascending_quadtree.los'
#gf_list=home+project_name+'/data/station_info/ascending.gflist'
#prefix='AS'
#
#insartools.quadtree2mudpy(home,project_name,file_in,gf_list,prefix)
# making the fault geometry
fault_file='/Users/dmelgar/Slip_inv/Ixtepec/data/model_info/usgs_NP1.fault'
strike=79
dip=55
num_columns=20
dx_strike=2.0
dx_dip=2.0
hypocenter=[-95.168597, 16.494261,10.0]
number_updip=5
number_downdip=5
rise_time=1.0
forward.makefault(fault_file,strike,dip,num_columns,dx_strike,
dx_dip,hypocenter,number_updip,number_downdip,rise_time)
fout_rupt = '/Users/dmelgar/Iquique2014/GFZ_model/iquique_gfz.rupt'
n = len(f) / 2
#split in two
f1 = f[0:n, :]
f2 = f[n:, :]
lonlat = f1[:, 0:2]
lonlat = fliplr(lonlat)
ss = f1[:, 8]
ds = f2[:, 8]
S = (ss**2 + ds**2)**0.5
#now make geometry of finely discretized fault
#forward.makefault(fout,strike,dip,45,3.0,3.0,[-70.593,-19.70,29.36],13,13,0.5)
forward.makefault(fout, strike, dip, 65, 3.0, 3.0, [-70.593, -19.70, 29.36],
30, 30, 0.5)
f = genfromtxt(fout)
#Make mean rupture model
slip_out = zeros((len(f), 1))
for k in range(len(f)):
if k % 100 == 0:
print k
dist = sqrt((f[k, 1] - lonlat[:, 0])**2 + (f[k, 2] - lonlat[:, 1])**2)
slip_out[k, 0] = S[argmin(dist)]
out = c_[f[:, 0:8],
zeros((len(f), 1)), slip_out, f[:, -2:],
zeros((len(f), 2))]
savetxt(
fout_rupt,
strike = 163
dip = 74
rake = 180
f = genfromtxt(
'/Users/dmelgar/Napa2014/stochastic/inversion/v_fine1.0014.inv.total')
fout = '/Users/dmelgar/Napa2014/stochastic/inversion/napa_200m.fault'
fout_rupt = '/Users/dmelgar/Napa2014/stochastic/inversion/napa_200m.rupt'
lonlat = f[:, 1:3]
ss = f[:, 8]
ds = f[:, 9]
S = (ss**2 + ds**2)**0.5
#now make geometry of finely discretized fault
forward.makefault(fout, strike, dip, 173, 0.2, 0.2,
[-122.3174, 38.2118, 10.729], 55, 22, 0.5)
f = genfromtxt(fout)
#Make mean rupture model
slip_out = zeros((len(f), 1))
for k in range(len(f)):
if k % 100 == 0:
print k
dist = sqrt((f[k, 1] - lonlat[:, 0])**2 + (f[k, 2] - lonlat[:, 1])**2)
slip_out[k, 0] = S[argmin(dist)]
#trim edges
i = arange(50, 173)
iout = i.copy()
for k in range(1, 78):
iout = r_[iout, i + (173 * k)]
fout2 = '/Users/dmelgar/Slip_inv/wenc_2008/data/model_info/wenc_unilateral.fault'
#Number of subfaults along strike
nstrike = 74
#Number of subfaults ABOVE the hypocenter
num_updip = 2
#Number of subfaults BELOW the hypocenter
num_downdip = 5
#Define size of subfaults
dx_strike = 7 #In km
dx_dip = 7 #In km
#
epicenter = array([103.36, 31.0, 12]) #I used the USGS one
#Rise time
rise_time = 3.0
#Make the fault
makefault(strike, dip, nstrike, dx_dip, dx_strike, epicenter, num_updip,
num_downdip, rise_time, fout)
#Read the model you just made so you can make a plot and some editting
fault = genfromtxt(fout)
#Make a quick plot so you can see the model
plt.figure()
plt.scatter(fault[:, 1], fault[:, 2])
plt.scatter(epicenter[0], epicenter[1], c='r', s=80)
plt.title('Bilateral model')
plt.show()
# Ok now make unialteral model, we will keep all subfaults north of the hypocenter
# but we will only keep 2 columns of faults to the south. This is determined
# by looking at the plot of the subfaults
ndip = num_updip + num_downdip + 1 #Total number of rows of subfaults
# Script used to
###############################################################################
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from numpy import genfromtxt
from mudpy import forward
fout = '/Users/tnye/FakeQuakes/Mentawai2010_fine/data/model_info/mentawai_fine.fault'
strike = 324
dip = 7.5
rise_time = 8
epicenter = [99.805960, -3.348720, 9.079]
nstrike = 78
dx_dip = 2
dx_strike = 2
num_updip = 19
num_downdip = 17
forward.makefault(fout, strike, dip, nstrike, dx_dip, dx_strike, epicenter,
num_updip, num_downdip, rise_time)
o = genfromtxt(
'/Users/tnye/FakeQuakes/Mentawai2010/data/model_info/Mentawai_model.fault')
f = genfromtxt(
'/Users/tnye/FakeQuakes/Mentawai2010_fine/data/model_info/mentawai_fine.fault'
)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(f[:, 1], f[:, 2], -f[:, 3], c='blue')
ax.scatter(o[:, 1], o[:, 2], -o[:, 3], c='red')