Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Compute cross-correlation matrices without matrix inversion #6339

Merged
merged 1 commit into from
Oct 18, 2023
Merged

Compute cross-correlation matrices without matrix inversion #6339

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 7 additions & 9 deletions src/ert/analysis/_es_update.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -444,11 +444,10 @@ def analysis_ES(
temp_storage = _create_temporary_parameter_storage(
source, iens_active_index, param_group.name
)

if module.localization():
Y_prime = S - S.mean(axis=1, keepdims=True)
C_YY = Y_prime @ Y_prime.T / (ensemble_size - 1)
Sigma_Y = np.diag(np.sqrt(np.diag(C_YY)))
Sigma_Y = np.std(S, axis=1, ddof=1)
batch_size: int = 1000
correlation_threshold = module.localization_correlation_threshold(
ensemble_size
Expand All @@ -465,17 +464,16 @@ def analysis_ES(
batches = _split_by_batchsize(np.arange(0, num_params), batch_size)
for param_batch_idx in tqdm(batches):
X_local = temp_storage[param_group.name][param_batch_idx, :]
# Parameter standard deviations
Sigma_A = np.std(X_local, axis=1, ddof=1)
# Cross-covariance between parameters and measurements
A = X_local - X_local.mean(axis=1, keepdims=True)
C_AA = A @ A.T / (ensemble_size - 1)

# State-measurement covariance matrix
C_AY = A @ Y_prime.T / (ensemble_size - 1)
Sigma_A = np.diag(np.sqrt(np.diag(C_AA)))

# State-measurement correlation matrix
# Cross-correlation between parameters and measurements
c_AY = np.abs(
np.linalg.inv(Sigma_A) @ C_AY @ np.linalg.inv(Sigma_Y)
(C_AY / Sigma_Y.reshape(1, -1)) / Sigma_A.reshape(-1, 1)
)
# Absolute values of the correlation matrix
c_bool = c_AY > correlation_threshold
# Some parameters might be significantly correlated
# to the exact same responses.
Expand Down
Loading