model = pipeline.Model(
vs=np.arange(3.5, 4.8, 0.1),
all_deps=deps,
idep=np.array([25, 50, 60, 70, 80, 90, 100, 102, 104, 106, 108, 110, 112]),
std_rf=0,
lam_rf=0,
std_swd=0)
#model = pipeline.Model(vs = np.array([1.8, 2.4, 3.4, 4.5, 4.7, 4.65]), all_deps = deps,
# idep = np.array([10, 32, 41, 60, 96, 120]),
# std_rf = 0, lam_rf = 0, std_swd = 0)
#model = pipeline.Model(vs = np.array([3.4, 4.5]), all_deps = deps,
# idep = np.array([60, 96]),
# std_rf = 0, lam_rf = 0, std_swd = 0)
rf_obs = pipeline.SynthesiseRF(pipeline.MakeFullModel(model))
swd_obs = pipeline.SynthesiseSWD(pipeline.MakeFullModel(model),
1 / np.arange(0.02, 0.1, 0.01), 1e6)
all_lims = pipeline.Limits(vs=(0.5, 5.5),
dep=(0, 200),
std_rf=(0, 0.05),
lam_rf=(0.05, 0.5),
std_swd=(0, 0.15))
out = pipeline.JointInversion(rf_obs, swd_obs, all_lims, max_it, rnd_sd)
actual_model = pipeline.SaveModel(pipeline.MakeFullModel(model), out[1][:, 0])
#%%
all_models = out[1]
good_mods = all_models[:, np.where(all_models[0, ] > 0)[0]]
nit = good_mods.shape[1]
#%%
all_models = np.load('testsave.npy')
good_mods = all_models[:, np.where(all_models[0, ] > 0)[0]]
nit = good_mods.shape[1]
good_mods = good_mods[:, -int(nit / 5):]
mean_mod = np.mean(good_mods, axis=1)
std_mod = np.std(good_mods, axis=1)
good_mod = pipeline.Model(vs=mean_mod,
all_deps=all_models[:, 0],
idep=np.arange(0, mean_mod.size),
lam_rf=0,
std_rf=0,
std_swd=0)
fullmodel = pipeline.MakeFullModel(good_mod)
fig1 = plt.figure()
ax1 = plt.subplot(131)
for k in range(all_models[1, ].size - 1):
colstr = str(0.75 - k / 2 / all_models[1, ].size)
ax1.plot(all_models[:, k],
all_models[:, 0],
'-',
linewidth=1,
color=colstr)
ax1.set_ylim((195, 0))
#ax1.plot(actual_model,all_models[:,0],'r-',linewidth=3)
ax1.set_xlim((2, 5.5))
ax1.set_xlabel('Shear Velocity (km/s)')
basedir = 'D:/Scattered_Waves/RJMCMC/'
#'C:/Users/Emily/Documents/RJMCMC/'
savename = open(os.path.join(basedir, 'input_data.py'), mode='w')
deps = np.concatenate(
(np.arange(0, 10, 0.2), np.arange(10, 60, 1), np.arange(60, 201, 5)))
model = pipeline.Model(vs=np.array([3.7, 4.3, 4.7, 4.6]),
all_deps=deps,
idep=np.array([10, 30, 90, 110]),
std_rf_sc=1,
lam_rf=0.2,
std_swd_sc=1)
fullmodel = pipeline.MakeFullModel(model)
savemodel = pipeline.SaveModel(fullmodel, deps)
stdsw = np.array([
0.7880, 0.3990, 0.5520, 0.4860, 0.3620, 0.2210, 0.0070, 0.0120, 0.0130,
0.0160, 0.0150, 0.0100
])
swd_in = pipeline.SurfaceWaveDisp(
period=np.array([
9,
10.12,
11.57,
13.5,
16.2,
20.25,
25,