Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter |
| |
Authors: | Haiyan Zhou Liangping Li Harrie-Jan Hendricks Franssen J Jaime Gómez-Hernández |
| |
Institution: | (1) Cold and Arid Regions Environmental and Engineering Research Institute, CAS, 730000 Lanzhou, China;(2) Department of Geological Sciences, Florida State University, Tallahassee, FL 32306, USA;(3) Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA; |
| |
Abstract: | The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model
states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and
parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian
distributed parameters. In this work, we aim at investigating the capacity of the NS-EnKF to identify patterns in the spatial
distribution of the model parameters (hydraulic conductivities) by assimilating dynamic observations in the absence of direct
measurements of the parameters themselves. In some situations, hydraulic conductivity measurements (hard data) may not be
available, which requires the estimation of conductivities from indirect observations, such as piezometric heads. We show
how the NS-EnKF is capable of retrieving the bimodal nature of a synthetic aquifer solely from piezometric head data. By comparison
with a more standard implementation of the EnKF, the NS-EnKF gives better results with regard to histogram preservation, uncertainty
assessment, and transport predictions. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|