A Case Study Using Seismic Reflection and Well Logs to Reduce and Quantify Uncertainty During a Hydrogeological Assessment
Bibliographic record
Abstract
Building a representative three-dimensional conceptual model of the hydrostratigraphic units is the critical first step when undertaking a hydrogeological assessment. The construction of such conceptual model requires integrating all geological data available for reducing the uncertainty of the conceptual model. In addition, the different types of data collected do not have the same level of uncertainty, and this has to be considered during the modeling. This manuscript presents a geostatistical workflow developed to integrate high resolution seismic reflection data with geological well markers and well logs. The proposed workflow allows reducing and quantifying the uncertainty when developing the conceptual model of hydrostratigraphic units and estimating the spatial distribution of hydraulic conductivity needed to simulate groundwater flow. The study involves the field investigation and numerical modeling for understanding the hydraulic connection between a local sand and gravel esker aquifer overlying a fractured bedrock regional aquifer. Seismic reflection data recorded in 2019 and 2021 significantly modify the conceptual model originally envisioned at site by discovering unknown deep esker extensions. Seismic data also help reducing the spatial uncertainty by delimiting the lateral extents, thickness, and depth of the esker aquifer between existing boreholes. Thickness maps of major hydrostratigraphic units are computed by first interpolating the data with higher uncertainty, which are then locally deformed by successive kriging with external drift to honor data with lower uncertainty. Well logs are then combined with seismic reflection data to better represent the spatial distribution of hydrofacies within the esker aquifer, and identify subunits in the bedrock aquifer. These modifications in the conceptual model of hydrostratigraphic units and the better understanding of the vertical heterogeneity of the hydraulic conductivity significantly impact the simulation of groundwater flow, both regionally and locally at site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".