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Title: Statistic inversion of multi-zone transition probability models for aquifer characterization in alluvial fans

Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This study develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in an accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. Finally, the result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transportmore » simulations.« less
Authors:
 [1] ;  [2] ;  [3] ;  [2] ;  [4]
  1. Capital Normal Univ., Beijing (China). College of Resources, Environment and Tourism. Lab. Cultivation Base of Environment Process and Digital Simulation; Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Earth and Environmental Sciences Division
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Earth and Environmental Sciences Division
  3. Capital Normal Univ., Beijing (China). College of Resources, Environment and Tourism. Lab. Cultivation Base of Environment Process and Digital Simulation
  4. Univ. of Padua (Italy). Dept. of Civil, Environmental and Architectural Engineering
Publication Date:
OSTI Identifier:
1325631
Report Number(s):
LA-UR--15-20404
Journal ID: ISSN 1436-3240
Grant/Contract Number:
41201420; 41130744; Z111106054511097
Type:
Accepted Manuscript
Journal Name:
Stochastic Environmental Research and Risk Assessment
Additional Journal Information:
Journal Volume: 30; Journal Issue: 3; Journal ID: ISSN 1436-3240
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Capital Normal Univ., Beijing (China)
Sponsoring Org:
USDOE; National Natural Science Foundation (China); Beijing Nova Program (China); Beijing Young Talent Program (China)
Contributing Orgs:
Univ. of Padua (Italy)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES Environmental Protection; Multi-zone transition probability; alluvial fan; sediment heterogeneity; structure parameter uncertainty; statistic inversion; indicator simulation