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Record W2253286847 · doi:10.2110/pec.04.80.0139

Development of a 3-D Depositional Model of Braided-River Gravels and Sands to Improve Aquifer Characterization

2004· book-chapter· en· W2253286847 on OpenAlexaff
I. Lunt, John Bridge, Robert S. Tye

Bibliographic record

VenueSEPM (Society for Sedimentary Geology) eBooks · 2004
Typebook-chapter
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsSedimentary depositional environmentGeologyAquiferGeochemistryCharacterization (materials science)GeomorphologyGeotechnical engineeringGroundwaterMaterials science

Abstract

fetched live from OpenAlex

Abstract Braided-river gravels and sands form important aquifers in the Quaternary fluvioglacial outwash deposits of many parts of the world. A detailed understanding of these deposits is vital for modeling groundwater flow and contaminant transport. A quantitative, 3D depositional model that can aid characterization of gravelly fluvial aquifers is developed based on existing published information and extensive new data from the Sagavanirktok River in northern Alaska. Sagavanirktok River deposits were studied using trenches, cores, wireline logs, porosity and permeability measurements, and ground-penetrating radar profiles. The mode of origin of the deposits was interpreted using knowledge of: (1) channel geometry and mode of erosion and deposition derived from annual aerial photos, and (2) bed texture and bed topography during erosion-deposition events (floods). Recognition of different scales of bedform and associated stratification is essential to the accurate modeling of fluvial deposits. Within a channel belt, the deposits of compound braid bars, point bars, and major channel fills are represented by compound sets of large-scale inclined strata. These compound sets fine upward, fine upward then coarsen upward, or show little vertical variation in grain size, and commonly have open-framework gravel near their bases. Unit bars and minor channel fills (associated with cross-bar channels) are represented by simple sets of large-scale inclined strata. These simple sets generally fine upward, and open-framework gravel commonly occurs at the bases and downstream ends of these sets. Superimposed simple sets form compound sets. Dunes and bed-load sheets that migrate over bars and in channels are represented by sets of medium-scale trough cross strata and gravelly planar strata, respectively. Cross strata in a medium-scale set can alternate between open-framework and closed-framework gravel. Ripples and upper-stage plane beds are represented by sets of small-scale trough cross-stratified sand and planar-laminated sand, respectively. At the top of the channel belt, these sands contain drifted plant remains, roots, and burrows. The 3-D depositional model represents the geometry and spatial distribution of the different scales of strata that occur in all river deposits. Furthermore, the length : thickness ratios of different scales of strata are similar to the length : height ratios of the formative bed forms (e.g., bars, dunes) and scale with the channel geometry, suggesting that the model can be applied to different scales of river deposits. Distributions of porosity and permeability are related to sediment textures and can be included in the model by predicting the spatial distribution of sediment textures within different scales of strata. Of particular importance is the distribution of high-permeability open-framework gravel strata that may be continuous for tens to hundreds of meters. Permeabilities of open-framework gravels can be two or three orders of magnitude greater than permeabilities of surrounding sediments, and significantly influence fluid flow and contaminant transport within the aquifer. Stochastic predictions of the spatial distribution of different scales of strata and their associated porosities and permeabilities in an aquifer will benefit from site-specific data (e.g., geophysical profiles, borehole logs, wireline logs, and pumping tests) combined with this three-dimensional model of gravelly fluvial deposits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.194
Teacher spread0.187 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations34
Published2004
Admission routes1
Has abstractyes

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