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sokrg.py
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sokrg.py
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# python imports
import os, shutil
from string import Template
from math import log10
import subprocess
import time
# global library imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# local imports
from krg_utils import *
from utils import plot_2d_image
from tinti import tinti
from srf import FaultSegment, PointSource, FiniteFaultSource, write
from flat_earth import convert_local_idx_to_geo
meters_per_kilometer = 1e3
centimeters_per_meter = 1e2
kgm_to_gcm = 1e-3
def main(kwargs=None):
print('Generating rupture model using SO-KRG v1.0')
print('========================================\n')
params = kwargs or {}
plot_on = params['plot_on']
tapering = params['tapering']
writing = params['writing']
layered = params['layered']
generate_fields = params['generate_fields']
resample = False
write_template = False
force_slip_to_zero = True
debug = False
if params:
for k, v in params.items():
print(f'{k}: {v}')
print('resample: ' + str(resample))
print('write_template: ' + str(write_template))
print('force_slip_to_zero: ' + str(force_slip_to_zero))
print('debug: ' + str(debug))
print()
if generate_fields:
print('Generating random fields...')
cmnd = [
"RScript",
"--vanilla",
"generic_sim_tottori.R",
str(params['output_path']),
str(params['seed']),
str(params['nsim']),
str(params['dx']),
str(params['fault_length']),
str(params['fault_width'])
]
return_code = subprocess.run(" ".join(cmnd), capture_output=True, shell=True)
print(" ".join(cmnd))
print(return_code.stdout.decode('utf-8'))
if return_code.returncode != 0:
print('\tError generating random fields. Exiting program.')
print(return_code.stderr.decode('utf-8'))
exit(1)
else:
print('Skipping random field generation. Using pre-existing simulations.')
# generate strike, dip, and rake
nhat1 = np.fromfile("nhat1", "f").reshape(801, 2601)
nhat2 = np.absolute(np.fromfile("nhat2", "f").reshape(801, 2601))
nhat3 = np.fromfile("nhat3", "f").reshape(801, 2601) # make vector point "up"
# note: starting at x=1000 to reduce model size for small model
# should implement this calculation outside
nhat1 = nhat1[::4]
nhat2 = nhat2[::4]
nhat3 = nhat3[::4]
#fienen "the three-point problem"
# project onto horizontal plane, calculate angle between
print('Computing strike dip and rake...')
dip = get_dip(nhat1, nhat2, nhat3)
strike = get_strike(nhat1, nhat3, mean_strike=params['strike'])
# rake = np.ones(strike.shape)*180.0 # constant rake
rake = strike - 90 # strike is 270 and rake is 180
# using array 1 index
for src_idx in range(1, params['nsim']+1):
print(f'Preparing source model {src_idx}...')
src_dir = f'./source_models/'
output_name = f'sokrg-bbp_source{src_idx}'
out_dir = f'./source_models/source{src_idx}'
if not os.path.isdir( out_dir ):
os.makedirs( out_dir )
# don't think we will use resampling to improve simultion times so hard-coding it out here
params['nx'] = params['fault_length'] // params['dx'] + 1
params['nz'] = params['fault_width'] // params['dx'] + 1
params['inx'] = params['nx']
params['inz'] = params['nz']
print(params['nx'], params['nz'])
# read normal score transforms, change to quantile transform
slip_sc = pd.read_csv('slip_nscore_transform_table.csv')
psv_sc = pd.read_csv('psv_nscore_transform_table.csv')
vrup_sc = pd.read_csv('vrup_nscore_transform_table.csv')
# extract data
slip = np.fromfile(src_dir + f'slip_sim{src_idx}.bin').reshape(params['inz'], params['inx'])
# flag used when choosing magnitudes
if force_slip_to_zero:
slip = slip - slip.mean()
if not debug:
psv = np.fromfile(src_dir + f'psv_sim{src_idx}.bin').reshape(params['inz'], params['inx'])
vrup = np.fromfile(src_dir + f'vrup_sim{src_idx}.bin').reshape(params['inz'], params['inx'])
if resample:
slip=resample2d(slip, shape=[params['nz'],params['nx']])
psv=resample2d(psv, shape=[params['nz'],params['nx']])
vrup=resample2d(vrup, shape=[params['nz'],params['nx']])
else:
params['nx'] = params['inx']
params['nz'] = params['inz']
# cut size of model down for computational ease
slip = slip[:-1, :-1]
psv = psv[:-1, :-1]
# psv=(psv-psv.mean())/psv.std()
vrup = vrup[:-1, :-1]
# update parameters
params['nx'] -= 1
params['nz'] -= 1
if layered:
# bbp model storerd using kilometers
material = expand_bbp_velocity_model(
np.loadtxt(params['velocity_model_path']),
params['nx'],
params['nz'],
params['dx'] * 1e-3
)
# convert to meters
vp = material[0]*1e3
vs = material[1]*1e3
rho = material[2]*1e3
else:
vs = 3464*np.ones((params['nz'], params['nx']))
rho = 2700*np.ones((params['nz'], params['nx']))
# transform from normal-scores change this to normalized versions
slip = transform_normal_scores(slip, slip_sc)
psv = transform_normal_scores(psv, psv_sc)
vrup = transform_normal_scores(vrup, vrup_sc)
if tapering:
avg_slip_pre = slip.mean()
# from simulations, slip tapers larger
taper_width = params['taper_width_slip']
slip = boundary_taper(slip,
taper_width=taper_width,
free_surface=True,
values=np.array(((0.60, 0.05), (0.05, 0.05))) )
avg_slip_post = slip.mean()
slip_taper_ratio = avg_slip_pre / avg_slip_post
slip = slip * slip_taper_ratio
# taper to 30% of mean along-strike psv at z = taper_width * dx
taper_width = params['taper_width_psv']
ny,nx=psv.shape
baseline = np.ones( (ny-4*taper_width, nx-2*taper_width) )
padded = np.pad( baseline,
((3*taper_width,taper_width), (taper_width,taper_width)),
'linear_ramp',
end_values=np.array(((0.30, 0.05), (0.05, 0.05))) )
psv = padded * psv
vrup = vrup * vs
else:
vrup = vrup * vs
# compute moment
print('Computing moment...')
moment = get_moment(slip, vs, rho, params)
moment_ratio = params['target_moment'] / moment.sum()
if moment_ratio >= 1.1 or moment_ratio <= 0.9:
print('Warning: greater than 10 percent different between simulated moment and target moment. Consider adjusting fault area.')
print(f"\tTarget moment: {params['target_moment']}, Simulated moment: {moment.sum()}, Ratio: {moment_ratio}")
# material model and fault area is constant; therefore, only change comes from slip
slip = slip * moment_ratio
moment = get_moment(slip, vs, rho, params)
print(f'moment: {moment.sum()}\nmw: {2./3 * (log10(moment.sum()) - 9.05)}')
print()
trup = compute_trup(vrup, params)
# replace large nan values with large number; ie., they didn't rupture
inds = np.where(np.isnan(trup))
trup[inds] = 999.
# compute new psv given tinti kinematic parameters
# 1) cap max(slip/psv) = 2
psv_eff=psv.copy()
inds=np.where(slip/psv_eff > 2)
psv_eff[inds]=slip[inds] / 2
# 2) cap min(psv) = 0.1
inds=np.where(psv_eff < 0.1)
psv_eff[inds]=0.1
# estimate dcp based on mean of psv_eff and regression analysis, where vpeak/dcp = 2.46*fs_max
fs_max = params['fs_max']
ratio_vpeak_dcp = fs_max*2.46
dc_est = 1.0/ratio_vpeak_dcp * psv_eff.mean()
# compute ts on fault using dc_est
ratio_dcp_est_psv_eff = dc_est / psv_eff
ts = 1.55 * ratio_dcp_est_psv_eff
# this was chosen as a reasonable upper bound, but this needs to be defined more explicitly
# truptot=18.5
# savran and olsen, 2020 defines this as the average trup on the fault boundary
per = 97.5
truptot = np.percentile(
np.hstack([trup[0,:], trup[-1,:], trup[:,0], trup[:,-1]]),
per
)
treff=truptot-trup
# compute tr
tr = 3.62 * slip + 0.07 * treff;
tr[tr < 0] = 0
# prevent bad parameters
inds = np.where(tr < ts)
tr[inds] = 1.5 * ts[inds]
# compute test tr using eq.11 from tinti et al, 2005
tr_eq11 = (1.3*ts)/(dc_est/slip)**2
tr_eq7 = slip**2 / psv_eff**2 / (1.3*ts)
# compute psv of tinti functions
inds=np.where(tr > 0)
psv_tinti=np.zeros(psv_eff.shape)
psv_tinti[inds] = 1.04*slip[inds] / ( ((1.3*ts[inds])**0.54) * (tr[inds]**0.47) )
print(f'slip: min, max, mean ({slip.min():.2f}, {slip.max():.2f}, {slip.mean():.2f})')
print(f'psv: min, max ({psv.min():.2f}, {psv.max():.2f})')
print(f'psv/dcp: ({ratio_vpeak_dcp:.2f}) mean(psv_eff): ({psv_eff.mean():.2f}) dcp: ({dc_est:.2f})')
print(f'vrup: min, max ({vrup.min():.2f}, {vrup.max():.2f})')
print(f'trup: min, max ({trup.min():.2f}, {trup.max():.2f})')
print(f'ts: min, max, mean ({ts.min():.2f}, {ts.max():.2f}, {ts.mean():.2f})')
print(f'tr: min, max, mean ({tr.min():.2f}, {tr.max():.2f}, {tr.mean():.2f})')
print(f'psv_tinti: min, max, mean ({psv_tinti.min():.2f}, {psv_tinti.max():.2f}, {psv_tinti.mean():.2f})')
print(f'tr_eq11_eq11: min, max, mean ({tr_eq11.min():.2f}, {tr_eq11.max():.2f}, {tr_eq11.mean():.2f})')
print(f'tr_eq7_eq11: min, max, mean ({tr_eq7.min():.2f}, {tr_eq7.max():.2f}, {tr_eq7.mean():.2f})')
print(f'truptot: {truptot}')
print(f'avg strike: {strike.mean():.2f}')
print()
if plot_on:
x = np.arange(0,params['nx'])
z = np.arange(0,params['nz'])
plotting_data = {'data': slip, 'contour': trup}
plot_2d_image( plotting_data, out_dir + "/slip-" + output_name + ".pdf" , nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = "Slip (m)", xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = True, clim=(0, slip.max()), cmap='jet')
plot_2d_image( psv, out_dir + "/psv-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$V^{peak}$ (m/s)', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, clim=(0, psv.max()), cmap='jet' )
plot_2d_image( trup, out_dir + "/trup-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r"$t_{0}$ (s)", xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = True, clim=(0,12.5) )
plot_2d_image( vrup/vs, out_dir + "/vrup-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$V_{rup}/c_s$', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='viridis', clim=(0, 1.0) )
plot_2d_image( vs, out_dir + "/vs-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$c_s$', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet' )
plot_2d_image( ts, out_dir + "/ts-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$\tau_s$ (s)', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet' )
plot_2d_image( tr, out_dir + "/tr-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$\tau_r$ (s)', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet' )
plot_2d_image( tr_eq11, out_dir + "/treq11-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$tr_{eq11}$', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet' )
plot_2d_image( tr_eq7, out_dir + "/treq7-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$tr_{eq7}$', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet' )
plot_2d_image( psv_tinti, out_dir + "/psv_tinti-" + output_name + ".pdf", nx = params['nx'], nz = params['nz'], dx = params['dx']*1e-3,
clabel = r'$V^{peak}$ (m/s)', xlabel = "Distance (km)", ylabel = "Distance (km)",
surface_plot = False, contour_plot = False, cmap='jet', clim=(0,psv_tinti.max()), show_plots=params['show_plots'])
plt.close('all')
# write to file for input
if writing:
dtype = '<f4'
print('Writing files...')
# start at 1000 to reduce the size of the source simulation.
vs.astype(dtype).tofile(os.path.join(out_dir, output_name + '_vs.bin'))
rho.astype(dtype).tofile(os.path.join(out_dir, output_name + '_rho.bin'))
slip.astype(dtype).tofile(os.path.join(out_dir, output_name + '_slip.bin'))
psv.astype(dtype).tofile(os.path.join(out_dir, output_name + '_psv.bin'))
vrup.astype(dtype).tofile(os.path.join(out_dir, output_name + '_vrup.bin'))
trup.astype(dtype).tofile(os.path.join(out_dir, output_name + '_trup.bin'))
strike.astype(dtype).tofile(os.path.join(out_dir, output_name + '_strike.bin'))
dip.astype(dtype).tofile(os.path.join(out_dir, output_name + '_dip.bin'))
rake.astype(dtype).tofile(os.path.join(out_dir, output_name + '_rake.bin'))
moment.astype(dtype).tofile(os.path.join(out_dir, output_name + '_moment.bin'))
ts.astype(dtype).tofile(os.path.join(out_dir, output_name + '_ts.bin'))
tr.astype(dtype).tofile(os.path.join(out_dir, output_name + '_tr.bin'))
# making params.txt file
if write_template:
print('Writing parameter file to params.txt')
fin=open( 'params.tmpl' )
template=Template( fin.read() )
fin.close()
d = {
'psv_file': 'in/' + output_name + '_psv.bin',
'vs_file': 'in/' + output_name + '_vs.bin',
'rho_file': 'in/' + output_name + '_rho.bin',
'trup_file': 'in/' + output_name + '_trup.bin',
'strike_file': 'in/' + output_name + '_strike.bin',
'dip_file': 'in/' + output_name + '_dip.bin',
'rake_file': 'in/' + output_name + '_rake.bin',
'slip_file': 'in/' + output_name + '_slip.bin',
'momentrate_file': '../stripe_count_160/' + output_name + '_source.bin',
'coord_file': 'in/fault_coords.bin',
'dc': dc_est,
'median_ts': 0.05333333,
'truptot': truptot,
}
template=template.substitute(d)
fout=open(os.path.join(out_dir,'params.txt'),'w')
fout.write(template)
fout.close()
# copying fault_coords.bin
print('copying fault_coords.bin into out_dir')
shutil.copy2('./fault_coords.bin', out_dir)
# write SRF files for source model
if params['generate_srf']:
srf = FiniteFaultSource()
srf.version = 2.0
seg = FaultSegment()
# segment information
# fault center lon and lat
seg.elon = params['lon_top_center']
seg.elat = params['lat_top_center']
# num points along strike and dip
seg.nstk = params['nx']
seg.ndip = params['nz']
# fault length (km)
seg.len = params['fault_length'] / meters_per_kilometer
# fault width (km)
seg.wid = params['fault_width'] / meters_per_kilometer
# fault strike
seg.stk = np.mean(strike)
# fault dip
seg.dip = np.mean(dip)
# depth to top of fault (km)
seg.dtop = params['fault_top'] / meters_per_kilometer
# along strike hypo center (km)
seg.shyp = params['ihypo'][1] * params['dx'] / meters_per_kilometer
# along dip hypo center (km)
seg.dhyp = params['ihypo'][0] * params['dx'] / meters_per_kilometer
# add to finite source
srf.segment_headers.append(seg)
# prepare subfaults
lat0 = params['lat_top_center']
lon0 = params['lon_top_center']
stk = np.deg2rad(params['strike'])
length = params['fault_length'] / meters_per_kilometer
dx = params['dx']
t = np.arange(0.0, 25.0, 0.025)
srcs = []
for idz in range(params['nz']):
for idx in range(params['nx']):
p = PointSource()
# uses flat-earth approximation centered on fault
p.lat, p.lon = convert_local_idx_to_geo(idx, lat0, lon0, params['fault_length'], params['dx'], stk)
# subfault depth (km)
p.dep = (params['fault_top'] + idz * dx) / meters_per_kilometer
# strike and dip (planar)
p.stk = strike[idz, idx]
p.dip = dip[idz, idx]
# subfault area (cm^2)
p.area = params['dx'] * params['dx'] * centimeters_per_meter * centimeters_per_meter
# t_init of source time function
p.tinit = trup[idz,idx]
# extract material parameters
# vs (cm/s)
p.vs = vs[idz, idx] * centimeters_per_meter
p.dt = params['dt']
# rho (g/cm^3)
p.den = rho[idz, idx] * kgm_to_gcm
p.rake = params['rake']
# decompose using strike, dip, and rake
stf = centimeters_per_meter * slip[idz,idx] * tinti(t, ts[idz,idx], tr[idz,idx], 0.0)
if np.any(np.isnan(stf)):
print('nan found in stf with params {ts[idz,idx]:.2f}, {tr[idz,idx]:.2f}')
# prepare source-time function
stf_trimmed = np.trim_zeros(stf, trim='b')
new_length = stf_trimmed.shape[0]
# slip1, nt1
p.sr1 = stf_trimmed
p.slip1 = slip[idz,idx] * centimeters_per_meter
p.nt1 = new_length
# slip2, nt2
p.slip2 = 0 * centimeters_per_meter
p.nt2 = 0
# slip3, nt3
p.slip3 = 0 * centimeters_per_meter
p.nt3 = 0
srcs.append(p)
srf.point_sources.append(srcs)
# write out to file
print(f'Writing source {src_idx}')
write(f'./srf/tottori-sokrg_v4-src{src_idx-1:04d}.srf', srf)
if __name__ == "__main__":
# values with units are provided using kg/m/s
params = {
'fault_length': 21000,
'fault_width': 20000,
'dx' : 100,
'target_moment': 8.62e+18,
'ihypo' : (105, 135),
'fault_top' : 100,
'taper_width_slip': 30,
'taper_width_psv': 10,
'fs_max': 12.5,
'output_path': '/Users/wsavran/Research/sokrg_bbp/source_models',
'seed': 123456,
'nsim': 64,
'velocity_model_path': './central_japan_bbp1d.txt',
'plot_on': True,
'show_plots': False,
'tapering': True,
'writing': True,
'layered': True,
'generate_fields': True,
'generate_srf': True,
'lat_top_center': 35.269,
'lon_top_center': 133.357,
'hypo_along_stk': 0.00,
'hypo_along_dip': 14.00,
'strike': 150,
'rake': 180,
'dip': 90,
'dt': 0.025
}
t0 = time.time()
main(kwargs=params)
t1 = time.time()
print()
print(f"Generated {params['nsim']} source models in {t1-t0} seconds.")