-
Notifications
You must be signed in to change notification settings - Fork 5
/
02_inversion.py
241 lines (218 loc) · 9.79 KB
/
02_inversion.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
import numpy as np
import torch
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("agg")
from scipy import integrate
import sys
import os
sys.path.append("../../../")
from ADFWI.propagator import *
from ADFWI.model import *
from ADFWI.view import *
from ADFWI.utils import *
from ADFWI.survey import *
from ADFWI.fwi import *
import warnings
warnings.filterwarnings("ignore")
if __name__ == "__main__":
project_path = "./data/"
if not os.path.exists(os.path.join(project_path,"model")):
os.makedirs(os.path.join(project_path,"model"))
if not os.path.exists(os.path.join(project_path,"waveform")):
os.makedirs(os.path.join(project_path,"waveform"))
if not os.path.exists(os.path.join(project_path,"survey")):
os.makedirs(os.path.join(project_path,"survey"))
if not os.path.exists(os.path.join(project_path,"inversion")):
os.makedirs(os.path.join(project_path,"inversion"))
#------------------------------------------------------
# Basic Parameters
#------------------------------------------------------
device = "cuda:0"
dtype = torch.float32
ox,oz = 0,0
nz,nx = 80,180
dx,dz = 10, 10
nt,dt = 1000,0.001
nabc = 50
f0 = 30
free_surface = True
# Load the Marmousi model dataset from the specified directory.
# velocity model
vp_true = np.ones((nz,nx))*3000
vs_true = np.ones((nz,nx))*1500
rho_true = np.ones((nz,nx))*2450
epsilon_true = np.ones((nz,nx))*0.1
gamma_true = np.ones((nz,nx))*0
delta_true = np.ones((nz,nx))*(-0.1)
# anomaly 0
center_x = nx//2
center_z = nz//2
center_r = 10
mask = vp_true == -1
for i in range(nz):
for j in range(nx):
if np.sqrt((i-center_z)**2 + (j-center_x)**2)<center_r:
mask[i,j] = True
epsilon_true[mask] = 0.15
# anomaly 1
center_x = nx//4
center_z = nz//2
mask = vp_true == -1
length_square = 20
mask[center_z-length_square//2:center_z+length_square//2,center_x-length_square//2:center_x+length_square//2] = True
epsilon_true[mask] = 0.28
# anomaly 2
center_x = 3*nx//4
center_z = nz//2
mask = vp_true == -1
length_square = 20
mask[center_z-length_square//2:center_z+length_square//2,center_x-length_square//2:center_x+length_square//2+5] = True
epsilon_true[mask] = 0.2
# init model
vp_init = np.ones((nz,nx))*3000
vs_init = np.ones((nz,nx))*1500
rho_init = np.ones((nz,nx))*2450
epsilon_init = np.ones((nz,nx))*0.1
gamma_init = np.ones((nz,nx))*0
delta_init = np.ones((nz,nx))*(-0.1)
model = AnisotropicElasticModel(
ox,oz,nx,nz,dx,dz,
vp=vp_init,vs=vs_init,rho=rho_init,
eps=epsilon_init,gamma=gamma_init,delta=delta_init,
vp_grad=False,vs_grad=False,rho_grad=False,
eps_grad=True,gamma_grad=False,delta_grad=False,
eps_bound=[epsilon_true.min(),epsilon_true.max()],
free_surface=free_surface,
anisotropic_type='vti',
abc_type="PML",abc_jerjan_alpha=0.007,
auto_update_rho=False,
auto_update_vp =False,
nabc=nabc,
device=device,dtype=dtype)
model.save(os.path.join(project_path,"model/init_model.npz"))
print(model.__repr__())
model._plot_vp_vs_rho(figsize=(12,5),wspace=0.3,cbar_pad_fraction=0.18,cbar_height=0.04,cmap='coolwarm',save_path=os.path.join(project_path,"model/init_vp_rho.png"),show=False)
model._plot_eps_delta_gamma(figsize=(12,5),wspace=0.3,cbar_pad_fraction=-0.1,cbar_height=0.04,cmap='coolwarm',save_path=os.path.join(project_path,"model/init_epsilon_gamma_delta.png"),show=False)
#------------------------------------------------------
# Source And Receiver
#------------------------------------------------------
# source
src_z = np.array([70 for i in range(1,nx-1,5)])
src_x = np.array([i for i in range(1,nx-1,5)])
src_t,src_v = wavelet(nt,dt,f0,amp0=1)
src_v = integrate.cumtrapz(src_v, axis=-1, initial=0) #Integrate
source = Source(nt=nt,dt=dt,f0=f0)
for i in range(len(src_x)):
source.add_source(src_x=src_x[i],src_z=src_z[i],src_wavelet=src_v,src_type="mt",src_mt=np.array([[1,0,0],[0,1,0],[0,0,1]]))
source.plot_wavelet(save_path=os.path.join(project_path,"survey/wavelets_init.png"),show=False)
# receiver
rcv_z = np.array([10 for i in range(0,nx,1)])
rcv_x = np.array([j for j in range(0,nx,1)])
receiver = Receiver(nt=nt,dt=dt)
for i in range(len(rcv_x)):
receiver.add_receiver(rcv_x=rcv_x[i],rcv_z=rcv_z[i],rcv_type="pr")
# survey
survey = Survey(source=source,receiver=receiver)
print(survey.__repr__())
survey.plot(model.vp,cmap='coolwarm',save_path=os.path.join(project_path,"survey/observed_system_init.png"),show=False)
#------------------------------------------------------
# Waveform Propagator
#------------------------------------------------------
F = ElasticPropagator(model,survey,device=device)
if model.abc_type == "PML":
bcx = F.bcx
bcz = F.bcz
title_param = {'family':'Times New Roman','weight':'normal','size': 15}
plot_bcx_bcz(bcx,bcz,dx=dx,dz=dz,wspace=0.25,title_param=title_param,cbar_height=0.04,cbar_pad_fraction=-0.05,save_path=os.path.join(project_path,"model/boundary_condition_init.png"),show=False)
else:
damp = F.damp
plot_damp(damp)
# load data
d_obs = SeismicData(survey)
d_obs.load(os.path.join(project_path,"waveform/obs_data.npz"))
print(d_obs.__repr__())
from ADFWI.fwi.misfit import Misfit_waveform_L2
iteration = 500
# optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr = 0.05,betas=(0.9,0.999), weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=200,gamma=0.75,last_epoch=-1)
# Setup misfit function
loss_fn = Misfit_waveform_L2(dt=dt)
# gradient processor
gradient_processor = GradProcessor()
fwi = ElasticFWI(propagator=F,
model=model,
optimizer=optimizer,
scheduler=scheduler,
loss_fn=loss_fn,
obs_data=d_obs,gradient_processor=gradient_processor,
waveform_normalize=True,
cache_result=True,cache_gradient=True,
save_fig_epoch=50,
save_fig_path=os.path.join(project_path,"inversion"),
inversion_component=["vx","vz"]
)
fwi.forward(iteration=iteration,fd_order=4,
batch_size=None,checkpoint_segments=4,start_iter=0)
iter_vp = fwi.iter_vp
iter_vs = fwi.iter_vs
iter_rho = fwi.iter_rho
iter_eps = fwi.iter_eps
iter_delta = fwi.iter_delta
iter_loss = fwi.iter_loss
np.savez(os.path.join(project_path,"inversion/iter_vp.npz"),data=np.array(iter_vp))
np.savez(os.path.join(project_path,"inversion/iter_vs.npz"),data=np.array(iter_vs))
np.savez(os.path.join(project_path,"inversion/iter_rho.npz"),data=np.array(iter_rho))
np.savez(os.path.join(project_path,"inversion/iter_eps.npz"),data=np.array(iter_eps))
np.savez(os.path.join(project_path,"inversion/iter_delta.npz"),data=np.array(iter_delta))
np.savez(os.path.join(project_path,"inversion/iter_loss.npz"),data=np.array(iter_loss))
###########################################
# visualize the inversion results
###########################################
# the animation results
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
# plot the misfit
plt.figure(figsize=(8,6))
plt.plot(iter_loss,c='k')
plt.xlabel("Iterations", fontsize=12)
plt.ylabel("L2-norm Misfits", fontsize=12)
plt.tick_params(labelsize=12)
plt.savefig(os.path.join(project_path,"inversion/misfit.png"),bbox_inches='tight',dpi=100)
plt.close()
# plot the initial model and inverted resutls
plt.figure(figsize=(12,8))
plt.subplot(121)
plt.imshow(epsilon_init,cmap='jet_r')
plt.subplot(122)
plt.imshow(iter_eps[-1],cmap='jet_r')
plt.savefig(os.path.join(project_path,"inversion/inverted_res.png"),bbox_inches='tight',dpi=100)
plt.close()
# Set up the figure for plotting
fig, ax = plt.subplots(figsize=(8, 6))
cax = ax.imshow(iter_eps[0], aspect='equal', cmap='jet_r', vmin=vp_true.min(), vmax=vp_true.max())
ax.set_title('Inversion Process Visualization')
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Z Coordinate')
# Create a horizontal colorbar
cbar = fig.colorbar(cax, ax=ax, orientation='horizontal', fraction=0.046, pad=0.2)
cbar.set_label('Velocity (m/s)')
# Adjust the layout to minimize white space
plt.subplots_adjust(top=0.85, bottom=0.2, left=0.1, right=0.9)
# Initialization function
def init():
cax.set_array(iter_eps[0]) # Use the 2D array directly
return cax,
# Animation function
def animate(i):
cax.set_array(iter_eps[i]) # Update with the i-th iteration directly
return cax,
# Create the animation
ani = animation.FuncAnimation(fig, animate, init_func=init, frames=len(iter_eps), interval=100, blit=True)
# Save the animation as a video file (e.g., MP4 format)
ani.save(os.path.join(project_path, "inversion/inversion_process.gif"), writer='pillow', fps=10)
# Display the animation using HTML
plt.close(fig) # Prevents static display of the last frame