| Title | Directional hydrostratigraphic units simulation using MCP algorithm |
| Author | Benoit, N; Marcotte, D; Boucher, A; D'Or, D; Bajc, A; Rezaee, H |
| Source | Stochastic Environmental Research and Risk Assessment 2017 p. 1-21, https://doi.org/10.1007/s00477-017-1506-9 |
| Year | 2017 |
| Alt Series | Natural Resources Canada, Contribution Series 20170108 |
| Publisher | Springer Nature |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | Science and Technology; mathematical and computational geology; hydrogeology; modelling; groundwater flow; hydrostratigraphic units; computer simulations; Bayesian maximum entropy method (BME);
Markov-type categorical prediction (MCP); fast Fourier transform; model uncertainty |
| Illustrations | plots; formulae; diagrams; location maps |
| Program | Groundwater Geoscience,
Program Management - Groundwater Geoscience |
| Released | 2017 12 26 |
| Abstract | Understanding the geological uncertainty of hydrostratigraphic models is important for risk assessment in hydrogeology. An important feature of sedimentary deposits is the directional ordering of
hydrostratigraphic units (HSU). Geostatistical simulation methods propose efficient algorithm for assessing HSU uncertainty. Among different geostatistical methods to simulate categorical data, Bayesian maximum entropy method (BME) and its simplified
version Markov-type categorical prediction (MCP) present interesting features. In particular, the zero-forcing property of BME and MCP can provide a valuable constrain on directional properties. We illustrate the ability of MCP to simulate vertically
ordered units. A regional hydrostratigraphic system with 11 HSU and different abundances is used. The transitional deterministic model of this system presents lateral variations and vertical ordering. The set of 66 (11 × 12/2) bivariate probability
functions is directly calculated on the deterministic model with fast Fourier transform. Despite the trends present in the deterministic model, MCP is unbiased for the HSU proportions in the non-conditional case. In the conditional cases, MCP proved
robust to datasets over-representing some HSU. The inter-realizations variability is shown to closely follow the amount and quality of data provided. Our results with different conditioning datasets show that MCP replicates adequately the directional
units arrangement. Thus, MCP appears to be a practical method for generating stochastic models in a 3D hydrostratigraphic context. |
| Summary | (Plain Language Summary, not published)
This paper describes the adaptation of the geostatistical simulation method (MCP) to simulate hydrostratigraphic systems composed of numerous geological
units. The units are ordered in vertical direction and present different proportions. The MCP method supports stochastic characterisation of the hydrostratigraphic units and offers useful alternative to more complex 3D hydrostratigraphic modelling.
The outputs will be used for groundwater flow modeling and uncertainty analyses. The method was successfully applied in the South Simcoe County, Ontario. This research was carried out under the Groundwater Geoscience Program - Geological Survey of
Canada in collaboration with École Polytechnique de Montréal, Ontario Geological Survey and Advanced Resources and Risk Technology LLC (USA) and Ephesia Consult (Belgium). |
| GEOSCAN ID | 302743 |
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