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| Type: | Resource | |
| Storage: | The size of this resource is 1.6 KB | |
| Created: | Feb 08, 2023 at 7:35 p.m. | |
| Last updated: | Feb 08, 2023 at 7:36 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 173 |
| Downloads: | 187 |
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Abstract
This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.
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Additional Metadata
| Name | Value |
|---|---|
| DOI | 10.1002/wrcr.20226 |
| Depth | 200 |
| Scale | 11 - 101 km² |
| Layers | 7 |
| Purpose | Scientific investigation (not related to applied problem) |
| GroMoPo_ID | 376 |
| IsVerified | True |
| Model Code | MODFLOW |
| Model Link | https://doi.org/10.1002/wrcr.20226 |
| Model Time | |
| Model Year | 2013 |
| Model Authors | Laloy, E; Rogiers, B; Vrugt, JA; Mallants, D; Jacques, D |
| Model Country | Belgium |
| Data Available | Report/paper only |
| Developer Email | elaloy@sckcen.be |
| Dominant Geology | Model focuses on multiple geologic materials |
| Developer Country | Belgium; CA USA; Netherlands; Australia |
| Publication Title | Efficient posterior exploration of a high- dimensional groundwater model from two- stage Markov chain Monte Carlo simulation and polynomial chaos expansion |
| Original Developer | No |
| Additional Information | |
| Integration or Coupling | |
| Evaluation or Calibration | Unsure |
| Geologic Data Availability | No |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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