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Multi-scale integrated characterization of heterogeneous hydraulic and thermal properties of a deltaic aquifer

2020· article· en· W3018787280 on OpenAlexaffabout
Jean-Marc Ballard, Cynthia Lee, Nataline Simon, Jérôme de La Bernardie, Daniel Paradis, Jasmin Raymond, Olivier Bour, René Lefebvre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsGDG EnvironnementGeological Survey of CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAquiferHydrogeologyGroundwaterGroundwater flowAquifer propertiesTRACERGroundwater modelCharacterization (materials science)WorkflowEnvironmental scienceThermalGeologyHydrology (agriculture)Soil scienceGeotechnical engineeringComputer scienceMaterials scienceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Historically, heat and temperature observations have been occasionally used to help understand aquifer systems or constrain numerical flow models. However, the development of fiber optics (FO) as part of the Distributed Temperature Sensing (DTS) technology has spun a renewed interest in the use of heat as a groundwater tracer. Recent studies have shown the possibility to carry out an active heat tracer test using fiber optics and heating cables installed by direct push and to invert the resulting thermal responses to estimate a vertical profile of groundwater fluxes. However, a better understanding of how FO-DTS results compare to other aquifer characterization methods is needed to guide its future application and integration into a practical workflow. The objective of this study was thus to compare the information provided by FO-DTS with other direct and indirect measurements used to characterize the heterogeneity of granular aquifers at multiple scales. The multiscale integrated characterization was carried out at a heterogeneous deltaic aquifer located north of Quebec City, Canada. This aquifer has been the object of a complete hydrogeological characterization and thus provides a wide range of existing data against which the acquired data can be compared. This communication will focus on the multiscale methodology for the granular aquifer characterization including FO-DTS measurements. Based on an existing numerical hydrogeological model, three sites with a range of horizontal groundwater fluxes were selected for active FO-DTS heat tracer experiments. At one of the sites, direct push monitoring wells were also installed downstream to measure the hydraulic conductivity of the hydrofacies and the arrival of the thermal front from the heat tracer test. A previous study established a relationship between the hydrofacies of the deltaic aquifer to cone penetration test (CPT) response. As such, each FO cable and monitoring well direct-push installation was preceded by a co-located CPT. Soil cores were also taken for laboratory measurements of hydraulic and thermal properties. The vertical profiles of groundwater fluxes from FO-DTS were found to correlate well with the relative magnitude of permeability of the hydrofacies identified with CPT profiles. FO-DTS could thus provide a qualitative or quantitative proxy for hydraulic conductivity and allow the recognition of hydrofacies at a fine scale. At the aquifer scale, the total flux measured by FO-DTS can also be compared to fluxes obtained from numerical models and thus provide a constraint to validate models. Overall, this study shows that not only does FO-DTS provide coherent results with other characterization methods, but it also adds the key measurement of groundwater flux that cannot be easily obtained by other means. FO-DTS thus has the potential to become a significant addition to existing characterization methods for granular aquifers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.195
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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