mod_space[range(mod_space.shape[0]), inds] += 1
fig2 = plt.figure()
ax2 = plt.subplot(121)
ax2.imshow(np.log10(mod_space[-1::-1] + 1e-1),
cmap='viridis',
aspect=allvels[-1] / evendeps[-1],
extent=[allvels[0], allvels[-1], evendeps[0], evendeps[-1]])
ax2.invert_yaxis()
ax2.set_xlabel('Shear Velocity (km/s)')
ax2.set_ylabel('Depth (km)')
ax2.xaxis.set_label_position('top')
ax2.xaxis.tick_top()
ax2.set_xlim((1.5, 5))
synth_swd = pipeline.SynthesiseSWD(fullmodel, swd_obs.period, 1e6)
synth_rf = pipeline.SynthesiseRF(fullmodel)
plt.figure(figsize=(10, 8))
plt.subplot(121)
plt.title('Receiver Function\n real: red; synth: grey')
rft = np.arange(0, rf_obs.dt * rf_obs.amp.size, rf_obs.dt)
plt.plot(rf_obs.amp, rft, 'r-', linewidth=2)
plt.plot(synth_rf.amp, rft, '-', color='0.25', linewidth=2)
plt.plot(rf_obs.amp - 0.02, rft, 'r--', linewidth=1)
plt.plot(rf_obs.amp + 0.02, rft, 'r--', linewidth=1)
plt.ylim(30, 0)
plt.subplot(122)
plt.title('Surface Wave Dispersion\n real: red; synth: grey')
plt.plot(swd_obs.period, swd_obs.c, 'r-', linewidth=2)
allvels = np.arange(all_lims.vs[0], all_lims.vs[1], 0.01)
evendeps = np.arange(0, all_models[-1, 0], 0.1)
i_ed = np.zeros(evendeps.shape, dtype=int)
for k in range(all_models[:, 0].size - 1, 0, -1):
i_ed[all_models[k, 0] >= evendeps] = k
mod_space = np.zeros((evendeps.size, allvels.size))
for k in range(1, good_mods.shape[1]):
even_vels = good_mods[i_ed, -k]
inds = np.round(even_vels - all_lims.vs[0], 2) / 0.01 - 1
inds = inds.astype(int)
mod_space[range(mod_space.shape[0]), inds] += 1
#%%
synth_swd = pipeline.SynthesiseSWD(fullmodel, swd_obs, 1e6)
if nit * save_every < 1e5:
synth_swd_coarse = pipeline.SynthesiseSWD(fullmodel, swd_obs, 1)
synth_rf = pipeline.SynthesiseRF(fullmodel, rf_obs)
plt.figure(figsize=(6, 8))
ax2 = plt.subplot(111)
ax2.imshow(np.log10(mod_space[-1::-1] + 1e-1),
cmap='viridis',
aspect=allvels[-1] / evendeps[-1],
extent=[allvels[0], allvels[-1], evendeps[0], evendeps[-1]])
ax2.invert_yaxis()
ax2.set_xlabel('Shear Velocity (km/s)')
ax2.set_ylabel('Depth (km)')
ax2.xaxis.set_label_position('top')
ax2.xaxis.tick_top()
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]
good_mods = good_mods[:, -int(nit / 5):]
mean_mod = np.mean(good_mods, axis=1)
10.12,
11.57,
13.5,
16.2,
20.25,
25,
32,
40,
50,
60,
80,
]),
c=np.zeros(12, ),
std=stdsw, #0.01*np.ones(12,)
)
swd = pipeline.SynthesiseSWD(fullmodel, swd_in, 1e6)
stdrf = np.array([
0.012, 0.026, 0.033, 0.039, 0.043, 0.045, 0.047, 0.042, 0.037, 0.042,
0.045, 0.043, 0.042, 0.041, 0.041, 0.038, 0.038, 0.038, 0.037, 0.036,
0.035, 0.034, 0.032, 0.029, 0.025, 0.022, 0.019, 0.017, 0.016, 0.015,
0.013, 0.012, 0.013, 0.013, 0.014, 0.013, 0.012, 0.012, 0.012, 0.011,
0.011, 0.011, 0.013, 0.016, 0.021, 0.026, 0.030, 0.031, 0.035, 0.036,
0.033, 0.029, 0.023, 0.016, 0.014, 0.016, 0.017, 0.016, 0.017, 0.019,
0.019, 0.019, 0.018, 0.018, 0.018, 0.018, 0.018, 0.015, 0.015, 0.011,
0.010, 0.014, 0.019, 0.022, 0.021, 0.020, 0.019, 0.019, 0.024, 0.034,
0.047, 0.059, 0.068, 0.076, 0.077, 0.073, 0.068, 0.058, 0.046, 0.034,
0.024, 0.022, 0.032, 0.043, 0.054, 0.062, 0.065, 0.066, 0.063, 0.059,
0.059, 0.068, 0.078, 0.084, 0.088, 0.085, 0.081, 0.073, 0.060, 0.049,
0.037, 0.036, 0.037, 0.038, 0.040, 0.040, 0.041, 0.043, 0.045, 0.047
])
rf_in = [
