def tested4():
impedance_inline99 = np.zeros(
(test_Inline99_seismic.shape[0], test_Inline99_seismic.shape[1]))
model = BSsequential_net_D().to(device)
mode_patch = './model_file/pre_trained_network_model_model1/pre_trained_network_model_SMI_2020_07_28_0%d.pth' % date_num
model.load_state_dict(torch.load(mode_patch))
# num_params = 0
# for param in model.parameters():
# num_params += param.numel()
# print(num_params)
for trace_number in range(0, test_Inline99_seismic.shape[0]):
print(trace_number)
# 计算相关系数
coef_seismic = np.zeros((105, Xline1_110_label_impedance.shape[1]))
coef_seismic[0, :] = test_Inline99_seismic[trace_number, :]
coef_seismic[1:105, :] = train_well_seismic[:, :]
temp_coef = np.corrcoef(coef_seismic)
# 优选出相关系数大于阈值并且半径范围内的井
tempval_1 = np.zeros(0)
temp_train_well_1 = np.zeros(0)
temp_train_well_seisic_1 = np.zeros(0)
absCORcoef = np.abs(temp_coef[0, 1:105])
if which_choose_well == 1:
num = 0
for k in range(0, 104):
if absCORcoef[k] > coefval:
# 井数据的坐标
wellxline = Xline_Inline_number[0, k]
wellinline = Xline_Inline_number[1, k]
# 目标地震数据的坐标
seismicinline = np.mod(trace_number + 1, 142)
seismicxline = (trace_number + 1 - seismicinline) / 142 + 1
R = np.sqrt((seismicxline - wellxline) *
(seismicxline - wellxline) +
(seismicinline - wellinline) *
(seismicinline - wellinline))
if R < Rval:
tempval_1 = np.append(tempval_1, absCORcoef[k])
temp_train_well_1 = np.append(temp_train_well_1,
train_well[k, :])
temp_train_well_seisic_1 = np.append(
temp_train_well_seisic_1, train_well_seismic[k, :])
num = num + 1
temp_train_well = np.zeros(0)
temp_train_well_seisic = np.zeros(0)
if num < num_well:
num = num_well
tempval = np.zeros(0)
for max_num in range(0, num):
temp_tempval = max(absCORcoef)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, 104):
if temp_tempval == absCORcoef[max_num2]:
absCORcoef[max_num2] = 0
temp_train_well = np.append(
temp_train_well, train_well[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
train_well_seismic[max_num2, :])
else:
tempval = np.zeros(0)
temp_train_well_1 = torch.from_numpy(temp_train_well_1)
temp_train_well_1 = temp_train_well_1.view(num, -1)
temp_train_well_1 = temp_train_well_1.cpu().detach().numpy()
temp_train_well_seisic_1 = torch.from_numpy(
temp_train_well_seisic_1)
temp_train_well_seisic_1 = temp_train_well_seisic_1.view(
num, -1)
temp_train_well_seisic_1 = temp_train_well_seisic_1.cpu(
).detach().numpy()
for max_num in range(0, num_well):
temp_tempval = max(tempval_1)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, num):
if temp_tempval == tempval_1[max_num2]:
tempval_1[max_num2] = 0
temp_train_well = np.append(
temp_train_well,
temp_train_well_1[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
temp_train_well_seisic_1[max_num2, :])
else:
num = num_well
tempval = np.zeros(0)
temp_train_well = np.zeros(0)
temp_train_well_seisic = np.zeros(0)
for max_num in range(0, num):
temp_tempval = max(absCORcoef)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, 104):
if temp_tempval == absCORcoef[max_num2]:
absCORcoef[max_num2] = 0
temp_train_well = np.append(temp_train_well,
train_well[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
train_well_seismic[max_num2, :])
num = num_well
maxval = max(tempval)
minval = min(tempval)
max_minlen = maxval - minval
tempval = (tempval - minval) / max_minlen
valsum = sum(tempval)
tempval = tempval / valsum
tempval = torch.from_numpy(tempval)
tempval = tempval.view(1, -1)
tempval = tempval.float()
tempval = tempval.to(device)
temp_train_well = torch.from_numpy(temp_train_well)
temp_train_well = temp_train_well.view(num, -1)
temp_train_well = temp_train_well.float()
# temp_train_well = temp_train_well.to(device)
temp_lable = torch.from_numpy(test_Inline99_seismic[trace_number, :])
temp_lable = temp_lable.float()
# temp_lable = temp_lable.to(device)
temp_lable = temp_lable.view(1, -1)
for num_batchlen in range(0, number):
# 利用优选出来的井数据，井旁道，加上一个目标道组成网络的输入
train_dataset = MyDataset2(
temp_train_well[:,
(num_batchlen *
BATCH_LEN):((num_batchlen + 1) * BATCH_LEN)],
temp_lable[:, (num_batchlen * BATCH_LEN):((num_batchlen + 1) *
BATCH_LEN)])
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
num_workers=1,
shuffle=True,
drop_last=False)
temp_seismic = test_Inline99_seismic[trace_number, (
num_batchlen * BATCH_LEN):((num_batchlen + 1) * BATCH_LEN)]
temp_seismic = torch.from_numpy(temp_seismic)
temp_seismic = temp_seismic.float()
temp_seismic = temp_seismic.to(device)
temp_seismic = temp_seismic.view(1, -1)
for itr, (train_dt, train_lable) in enumerate(train_dataloader):
train_dt, train_lable = train_dt.to(device), train_lable.to(
device)
train_dt = train_dt.float()
output = model(train_dt, temp_seismic)
# train_dt = train_dt.view(num_well, -1)
# output = tempval.mm(train_dt)
np_output = output.cpu().detach().numpy()
impedance_inline99[(trace_number *
BATCH_SIZE):((trace_number + 1) *
BATCH_SIZE),
(num_batchlen *
BATCH_LEN):((num_batchlen + 1) *
BATCH_LEN)] = np_output
pathmat = './SMI_out/pre_In99_Impedance_model1_2020_07_28_0%d.mat' % date_num
scipio.savemat(pathmat, {
'pre_In99_Impedance_model1_2020_07_28_0%d' % date_num:
impedance_inline99
})
def pre_trained(judge):
writer = SummaryWriter(
log_dir='./loss/pre_train_loss_model1/pre_train_loss_SMI_2020_07_28_0%d'
% date_num)
if judge == 0:
model = BSsequential_net_D().to(device)
model.apply(weights_init)
temp = 10000000000000
epoch_num = 1
else:
model = BSsequential_net_D().to(device)
mode_patch = './model_file/pre_trained_network_model_model1/pre_trained_network_model_SMI_2020_07_28_02.pth'
model.load_state_dict(torch.load(mode_patch))
temp = 10000000000000
epoch_num = 1
# path_temp = './Temporary_parameters/pre_temp_model1.mat'
# temp = scipio.loadmat(path_temp)
# temp = temp['temp'].item()
# path_epoch = './Temporary_parameters/pre_epoch_num_model1.mat'
# epoch_num = scipio.loadmat(path_epoch)
# epoch_num = epoch_num['epoch_num'].item()+1
if is_consistent == 0:
temp_wellnumber = np.zeros(0)
else:
is_path = 'D://Python_project/Predict_Impedence_hu2/SMI_out/map_number_2020_05_18_07.mat'
Random_path = scipio.loadmat(is_path)
temp_wellnumber = Random_path['temp_wellnumber']
count = 0 # 检验网络权重是否变化的计数器
lr = 0.001 # 学习步长
for epoch in range(epoch_num, EPOCHS + 1):
print(epoch, count)
temp_weight = model.fc60.weight # 检验网络权重是否变化的初始网络参数
temp_a = torch.sum(temp_weight.data)
# print(temp_weight)
# temp_a = torch.sum(temp_weight)
# print(a)
if np.mod(epoch + 1, 10) == 0:
lr = lr * 0.99
optimizer = optim.Adam(model.parameters(), lr=lr)
# if is_consistent == 1:
# trace_number = np.int(map_xline[0, epoch-1]*142+map_inline[0, epoch-1])
# else:
# temp_1 = np.random.randint(0, 29, 1)
# temp_2 = np.random.randint(0, 22, 1)
# trace_number = temp_2*5*142+temp_1*5
# map_xline = np.append(map_xline, temp_2 * 5)
# map_inline = np.append(map_inline, temp_1 * 5)
trace_number = np.random.randint(0, 104, 1)
temp_wellnumber = np.append(temp_wellnumber, trace_number)
# print(trace_number)
# 计算相关系数
coef_seismic = np.zeros((105, train_well_seismic.shape[1]))
coef_seismic[0, :] = train_well_seismic[trace_number, :]
coef_seismic[1:105, :] = train_well_seismic[:, :]
temp_coef = np.corrcoef(coef_seismic)
# 优选出相关系数大于阈值并且半径范围内的井
tempval_1 = np.zeros(0)
temp_train_well_1 = np.zeros(0)
temp_train_well_seisic_1 = np.zeros(0)
absCORcoef = np.abs(temp_coef[0, 1:105])
if which_choose_well == 1:
num = 0
for k in range(0, 104):
if absCORcoef[k] > coefval:
# 井数据的坐标
wellxline = Xline_Inline_number[0, k]
wellinline = Xline_Inline_number[1, k]
# 目标地震数据的坐标
seismicinline = np.mod(trace_number + 1, 142)
seismicxline = (trace_number + 1 - seismicinline) / 142 + 1
R = np.sqrt((seismicxline - wellxline) *
(seismicxline - wellxline) +
(seismicinline - wellinline) *
(seismicinline - wellinline))
if R < Rval:
tempval_1 = np.append(tempval_1, absCORcoef[k])
temp_train_well_1 = np.append(temp_train_well_1,
train_well[k, :])
temp_train_well_seisic_1 = np.append(
temp_train_well_seisic_1, train_well_seismic[k, :])
num = num + 1
temp_train_well = np.zeros(0)
temp_train_well_seisic = np.zeros(0)
if num < num_well:
num = num_well
tempval = np.zeros(0)
for max_num in range(0, num):
temp_tempval = max(absCORcoef)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, 104):
if temp_tempval == absCORcoef[max_num2]:
absCORcoef[max_num2] = 0
temp_train_well = np.append(
temp_train_well, train_well[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
train_well_seismic[max_num2, :])
else:
tempval = np.zeros(0)
temp_train_well_1 = torch.from_numpy(temp_train_well_1)
temp_train_well_1 = temp_train_well_1.view(num, -1)
temp_train_well_1 = temp_train_well_1.cpu().detach().numpy()
temp_train_well_seisic_1 = torch.from_numpy(
temp_train_well_seisic_1)
temp_train_well_seisic_1 = temp_train_well_seisic_1.view(
num, -1)
temp_train_well_seisic_1 = temp_train_well_seisic_1.cpu(
).detach().numpy()
for max_num in range(0, num_well):
temp_tempval = max(tempval_1)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, num):
if temp_tempval == tempval_1[max_num2]:
tempval_1[max_num2] = 0
temp_train_well = np.append(
temp_train_well,
temp_train_well_1[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
temp_train_well_seisic_1[max_num2, :])
else:
num = num_well
tempval = np.zeros(0)
temp_train_well = np.zeros(0)
temp_train_well_seisic = np.zeros(0)
for max_num in range(0, num):
temp_tempval = max(absCORcoef)
tempval = np.append(tempval, temp_tempval)
for max_num2 in range(0, 104):
if temp_tempval == absCORcoef[max_num2]:
absCORcoef[max_num2] = 0
temp_train_well = np.append(temp_train_well,
train_well[max_num2, :])
temp_train_well_seisic = np.append(
temp_train_well_seisic,
train_well_seismic[max_num2, :])
num = num_well
maxval = max(tempval)
minval = min(tempval)
max_minlen = maxval - minval
tempval = (tempval - minval) / max_minlen
valsum = sum(tempval)
tempval = tempval / valsum
tempval = torch.from_numpy(tempval)
tempval = tempval.view(1, -1)
tempval = tempval.float()
tempval = tempval.to(device)
temp_train_well = torch.from_numpy(temp_train_well)
temp_train_well = temp_train_well.view(num, -1)
temp_train_well = temp_train_well.float()
# temp_train_well = temp_train_well.to(device)
# temp_train_well = temp_train_well.view(num, -1)
# temp_train_well_seisic = torch.from_numpy(temp_train_well_seisic)
# temp_train_well_seisic = temp_train_well_seisic.float()
# temp_train_well_seisic = temp_train_well_seisic.to(device)
# temp_train_well_seisic = temp_train_well_seisic.view(num, -1)
# temp_seismic = torch.from_numpy(train1_75_seismic[trace_number, :])
# temp_seismic = temp_seismic.float()
# temp_seismic = temp_seismic.to(device)
# temp_seismic = temp_seismic.view(1, -1)
temp_lable = torch.from_numpy(train_well[trace_number, :])
temp_lable = temp_lable.float()
# temp_lable = temp_lable.to(device)
temp_lable = temp_lable.view(1, -1)
# for rand in range(0, 60 - BATCH_LEN + 1):
for num_rand in range(0, number):
rand = np.random.randint(0, 60 - BATCH_LEN + 1, 1)
temp_train_seismic = train_well_seismic[trace_number,
rand[0]:rand[0] +
BATCH_LEN]
temp_train_seismic = torch.from_numpy(temp_train_seismic)
temp_train_seismic = temp_train_seismic.float()
temp_train_seismic = temp_train_seismic.to(device)
temp_train_seismic = temp_train_seismic.view(1, -1)
# 利用优选出来的井数据，井旁道，加上一个目标道组成网络的输入
train_dataset = MyDataset2(
temp_train_well[:, rand[0]:rand[0] + BATCH_LEN],
temp_lable[:, rand[0]:rand[0] + BATCH_LEN])
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
num_workers=1,
shuffle=True,
drop_last=False)
epoch_loss = []
for itr, (train_dt, train_lable) in enumerate(train_dataloader):
train_dt, train_lable = train_dt.to(device), train_lable.to(
device)
train_dt = train_dt.float()
train_lable = train_lable.float()
model.train()
optimizer.zero_grad()
output = model(train_dt, temp_train_seismic)
if is_synseismic == 1:
syn_seismic = syn_seismic_fun2(output, wavelet)
syn_seismic = syn_seismic.float()
loss = F.mse_loss(syn_seismic,
temp_train_seismic) + F.mse_loss(
output, train_lable)
else:
loss = F.mse_loss(output, train_lable)
loss.backward()
optimizer.step()
# print(model.conv1.weight)
# print(model.conv2.weight)
# print(model.lstm.weight)
# print(model.fc1.weight.data[:, 0])
epoch_loss.append(loss.item())
temp_b = torch.sum(model.fc60.weight.data)
# print(b)
if temp_a == temp_b:
count = count + 1
else:
count = 0
if count > 50:
break
epoch_loss = np.sum(np.array(epoch_loss))
writer.add_scalar('Train/MSE', epoch_loss, epoch)
epoch_num = epoch
print('Train set: Average loss: {:.15f}'.format(epoch_loss))
if epoch_loss < temp:
path = './model_file/pre_trained_network_model_model1/pre_trained_network_model_SMI_2020_07_28_0%d.pth' % date_num
torch.save(model.state_dict(), path)
path_temp = './Temporary_parameters/pre_temp_model1.mat'
path_epoch = './Temporary_parameters/pre_epoch_num_model1.mat'
scipio.savemat(path_temp, {'temp': temp})
scipio.savemat(path_epoch, {'epoch_num': epoch_num})
if is_consistent == 0:
pathmat = './SMI_out/map_number_2020_07_28_0%d.mat' % date_num
scipio.savemat(pathmat, {'temp_wellnumber': temp_wellnumber})
writer.add_graph(model, (train_dt, temp_train_seismic))
writer.close()