The ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic conductivity characterization using transient piezometric head data. Its implementation is well suited for a parallel computing environment. A parallel code has been designed that uses parallelization both in the forecast step and in the analysis step. In the forecast step, each member of the ensemble is sent to a different processor, while in the analysis step, the computations of the covariances are distributed between the different processors. An important aspect of the parallelization is to limit as much as possible the communication between the processors in order to maximize execution time reduction. Four tests are carried out to evaluate the performance of the parallelization with different ensemble and model sizes. The results show the savings provided by the parallel EnKF, especially for a large number of ensemble realizations.
['Teng Xu', 'J. Jaime Gómez-Hernández', 'Liangping Li', 'Haiyan Zhou']
Parallelized ensemble Kalman filter for hydraulic conductivity characterization