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Record W2610308177 · doi:10.2110/jsr.2017.26

ANATOMY OF A SHORELINE REGRESSION: IMPLICATIONS FOR THE HIGH-RESOLUTION STRATIGRAPHIC ARCHITECTURE OF DELTAS

2017· article· en· W2610308177 on OpenAlexaffabout
R. Bruce Ainsworth, Boyan K. Vakarelov, James A. MacEachern, F. Rarity, Tessa I. Lane, Rachel Nanson

Bibliographic record

VenueJournal of Sedimentary Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeologyShoreArchitectureHigh resolutionRegressionPaleontologyGeomorphologyOceanographyArchaeologyRemote sensingGeographyStatistics

Abstract

fetched live from OpenAlex

Abstract The regressive and subsequent transgressive transit of a shoreline across a clastic shelf generates the standard reservoir flow unit in most marginal to shallow marine hydrocarbon reservoirs. The stratigraphic unit produced by the shoreline transits is commonly referred to as the high-frequency (104 to 105 years), regressive–transgressive sequence (RT sequence). The unit is usually bounded top and base by marine shales marking flooding surfaces, which can act as barriers to fluid flow. Stratigraphic architecture in the RT-sequence is also known to control the internal flow behavior of many reservoirs. Hence, an ability to consistently characterize reservoirs at a sub RT-sequence scale is critical to enabling prediction of reservoir performance and optimization of resource extraction strategies. This study describes the internal architecture of one shallow-water (< 10 m), low-accommodation regressive shoreline succession from the Campanian of the Alberta Basin, Canada, based on an extensive outcrop and subsurface dataset that has been convolved into a 3D geocellular computer model. The architecture and evolution of the ancient mixed-process (waves, tides, and fluvial processes) regressive deltaic shoreline system is compared and contrasted with a partial Holocene analog from northeastern Australia. The same stratigraphic surfaces and units are identified in both the modern and the ancient regressive systems. The key architectural unit is the element complex set (ECS), which is a multi-kilometer-scale, discontinuity-bounded unit that is the product of the reorganization of the coastline, often caused by autogenic backwater-driven avulsions. Multiple avulsions during a regressive shelf transit episode lead to lateral offsets of ECS units in low-accommodation systems. These systems are termed “avulsion-driven systems.” An increasing component of vertical stacking of ECS units is observed in higher-accommodation regressive systems. The mechanisms for generating accommodation on a regressive-shoreline shelf-transit time frame may be allogenic (tectonic subsidence or eustatic sea-level change), autogenic, or a combination of the two mechanisms. A key, localized, autogenic mechanism is related to the distance of progradation during the transit of a deposystem across the shelf. In proximal shelf positions, ECS units tend to offset laterally due to limited available accommodation, whilst in more distal positions, early differential, load-induced, compactional subsidence of underlying prodelta and shelf muds can promote vertical stacking of ECS units. The critical down depositional-dip distance from the transgressive turnaround point at which ECS units become preferentially vertically stacked is a function of shelf gradient, shoreline trajectory, sandstone fraction, and prodelta and shelf mud rheology and is termed the “critical autogenic ECS stacking distance.” Vertical stacking of ECS units may also occur when ECS units overstep underlying shelf topography, such as the distal termination of an older RT sequence. Recognition criteria and nomenclature for intra-regressive-shoreline surfaces and stratigraphic units, as well as predictive models for the ancient record are detailed across a spectrum of types of deltaic systems.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.383
Teacher spread0.299 · 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 teacher head, 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".

Quick stats

Citations57
Published2017
Admission routes2
Has abstractyes

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