Using a multiple variogram approach to improve the accuracy of subsurface geological models
Bibliographic record
Abstract
Subsurface geological models are often used to visualize and analyze the nature, geometry, and variability of geologic and hydrogeologic units in the context of groundwater resource studies. The development of three-dimensional (3D) subsurface geological models covering increasingly larger model domains has steadily increased in recent years, in step with the rapid development of computing technology and software, and the increasing need to understand and manage groundwater resources at the regional scale. The models are then used by decision makers to guide activities and policies related to source water protection, well field development, and industrial or agricultural water use. It is important to ensure that the modelling techniques and procedures are able to accurately delineate and characterize the heterogeneity of the various geological environments included within the regional model domain. The purpose of this study is to examine if 3D stratigraphic models covering complex Quaternary deposits can be improved by splitting the regional model into multiple submodels based on the degree of variability observed between surrounding data points and informed by expert geological knowledge of the geological–depositional framework. This is demonstrated using subsurface data from the Paris Moraine area near Guelph in southern Ontario. The variogram models produced for each submodel region were able to better characterize the data variability, resulting in a more geologically realistic interpolation of the entire model domain as demonstrated by the comparison of the model output with preexisting maps of surficial geology and bedrock topography as well as depositional models for these complex glacial environments. Importantly, comparison between model outputs reveals significant differences in the resulting subsurface stratigraphy, complexity, and variability, which would in turn impact groundwater flow model predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".