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02_inversion_3layer-16-32-16.py
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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 *
from ADFWI.dip import *
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
if __name__ == "__main__":
project_path = "./data/"
layer_num = 3
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,f"inversion-{layer_num}layer-16-32-16")):
os.makedirs(os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16"))
#------------------------------------------------------
# Basic Parameters
#------------------------------------------------------
device = "cuda:1"
dtype = torch.float32
ox,oz = 0,0
nz,nx = 88,200
dx,dz = 40, 40
nt,dt = 1600, 0.003
nabc = 30
f0 = 5
free_surface = True
#------------------------------------------------------
# Velocity Model
#------------------------------------------------------
# Load the Marmousi model dataset from the specified directory.
marmousi_model = load_marmousi_model(in_dir="../../../datasets/marmousi2_source")
# Create coordinate arrays for x and z based on the grid size.
x = np.linspace(5000, 5000 + dx * nx, nx)
z = np.linspace(0, dz * nz, nz)
true_model = resample_marmousi_model(x, z, marmousi_model)
smooth_model = get_smooth_marmousi_model(true_model, gaussian_kernel=6)
# Initialize primary wave velocity (vp) and density (rho) for the model.
vp_init = smooth_model['vp'].T # Transpose to match dimensions
rho_init = np.power(vp_init, 0.25) * 310 # Calculate density based on vp
# Extract true model properties for comparison.
vp_true = true_model['vp'].T # Transpose for consistency
rho_true = np.power(vp_true, 0.25) * 310 # Calculate true density
# -----------------------------------
# Define DIP model
# -----------------------------------
model_shape = [nz,nx]
DIP_model = DIP_CNN(model_shape,in_channels=[16,32,16],vmin=vp_true.min()/1000,vmax=vp_true.max()/1000,device=device)
DIP_model.to(device)
# -----------------------------------
# Pretrain DIP model
# -----------------------------------
pretrain = True
load_pretrained = False
if pretrain:
if load_pretrained:
# load the model parameters
DIP_model.load_state_dict(torch.load(os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/DIP_model_pretrained.pt")))
else:
lr = 0.005
iteration = 10000
step_size = 1000
gamma = 0.5
optimizer = torch.optim.Adam(DIP_model.parameters(),lr = lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=step_size,gamma=gamma)
vp_init = numpy2tensor(vp_init,dtype=dtype).to(device)
pbar = tqdm(range(iteration+1))
for i in pbar:
vp_nn = DIP_model()
loss = torch.sqrt(torch.sum((vp_nn - vp_init)**2))
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
pbar.set_description(f'Pretrain Iter:{i}, Misfit:{loss.cpu().detach().numpy()}')
torch.save(DIP_model.state_dict(),os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/DIP_model_pretrained.pt"))
# -----------------------------------
# velocity model for FWI
# -----------------------------------
water_layer_mask = np.zeros((vp_init.shape[0],vp_init.shape[1]))
water_layer_mask[:10,:] = 1
model = DIP_AcousticModel(ox,oz,nx,nz,dx,dz,
DIP_model_vp=DIP_model,
DIP_model_rho=None,
vp_init=vp_init,rho_init=rho_init,
auto_update_rho=True,auto_update_vp=False,
water_layer_mask=water_layer_mask,
free_surface=free_surface,
abc_type="PML",abc_jerjan_alpha=0.007,
nabc=nabc,
device=device,dtype=dtype)
print(model.__repr__())
model.save(os.path.join(project_path,"model/init_model.npz"))
#------------------------------------------------------
# Source And Receiver
#------------------------------------------------------
# source
src_z = np.array([1 for i in range(2, nx-1, 5)]) # Z-coordinates for sources
src_x = np.array([i for i in range(2, nx-1, 5)]) # X-coordinates for sources
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.png"),show=False)
# receiver
rcv_z = np.array([1 for i in range(0, nx, 1)]) # Z-coordinates for receivers
rcv_x = np.array([j for j in range(0, nx, 1)]) # X-coordinates for receivers
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 = AcousticPropagator(model,survey,device=device)
damp = F.damp
plot_damp(damp,save_path=os.path.join(project_path,"model/boundary_condition_init.png"),show=False)
# load data
d_obs = SeismicData(survey)
d_obs.load(os.path.join(project_path,"waveform/obs_data.npz"))
print(d_obs.__repr__())
# optimizer
iteration = 300
optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=100,gamma=0.75,last_epoch=-1)
# Setup misfit function
from ADFWI.fwi.misfit import Misfit_global_correlation
loss_fn = Misfit_global_correlation(dt=1)
# gradient processor
grad_mask = np.ones((vp_init.shape[0],vp_init.shape[1]))
grad_mask[:10,:] = 0
gradient_processor = GradProcessor(grad_mask=grad_mask)
# gradient processor
fwi = DIP_AcousticFWI(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,
save_fig_epoch=50,
save_fig_path=os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16")
)
fwi.forward(iteration=iteration,batch_size=None,checkpoint_segments=1)
iter_vp = fwi.iter_vp
iter_loss = fwi.iter_loss
np.savez(os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/iter_vp.npz"),data=np.array(iter_vp))
np.savez(os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/iter_loss.npz"),data=np.array(iter_loss))
torch.save(model.DIP_model_vp.state_dict(),os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/DIP_model.pt"))
#------------------------------------------------------
# Visualize the Inversion Results
#------------------------------------------------------
from ADFWI.view.inverted_loss_model import plot_misfit,plot_initial_and_inverted,animate_inversion_process
# misfit
plot_misfit(iter_loss = iter_loss, save_path=os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/misfit.png"),show=False)
# inverted results
vp_init = vp_init.cpu().detach().numpy() if torch.is_tensor(vp_init) else vp_init
plot_initial_and_inverted(vp_init=vp_init,iter_vp=iter_vp,save_path=os.path.join(project_path,f"inversion-{layer_num}layer-16-32-16/inverted_res.png"),show=False)
# inversion animation
animate_inversion_process(iter_vp=iter_vp,vmin=vp_true.min(),vmax=vp_true.max(),save_path=os.path.join(project_path, f"inversion-{layer_num}layer-16-32-16/inversion_process.gif"),fps=10)