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Record W2195070486 · doi:10.1190/leedff.23.1282_1

Detection of subtle basement faults with gravity and magnetic data in the Alberta Basin, Canada

2004· article· en· W2195070486 on OpenAlexaffabout
H.V. Lyatsky, Dinu Pană, Reg Olson, Lorraine Godwin

Bibliographic record

VenueThe Leading Edge · 2004
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsAlberta EnergyGeological Survey of CanadaGeoscience BC
Fundersnot available
KeywordsGeologyBasementStructural basinSeismologyPaleontologyGeodesyGeophysicsGeographyArchaeology

Abstract

fetched live from OpenAlex

PreviousNext No AccessThe Leading EdgeVolume 23, Issue 12Detection of subtle basement faults with gravity and magnetic data in the Alberta Basin, CanadaHenry LyatskyLyatsky Geoscience Research & Consulting, Calgary, CanadaSearch for more papers by this author, Dinu PanaAlberta Energy & Utilities Board/Alberta Geological Survey, Edmonton, CanadaSearch for more papers by this author, Reg OlsonAlberta Energy & Utilities Board/Alberta Geological Survey, Edmonton, CanadaSearch for more papers by this author, and Lorraine GodwinGeosoft, Toronto, CanadaSearch for more papers by this authorhttps://doi.org/10.1190/leedff.23.1282_1 SectionsAboutFull TextPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit FiguresReferencesRelatedDetailsCited bySedimentary cover and structural trends affecting the groundwater flow in the Nubian Sandstone Aquifer System: Inferences from geophysical, field and geochemical data17 April 2023 | Frontiers in Earth Science, Vol. 11Interpretation of aeromagnetic data to detect the deep-seated basement faults in fold thrust belts: NW part of the petroliferous Fars province, Zagros belt, IranMarine and Petroleum Geology, Vol. 133Fault detection by reflected surface waves based on ambient noise interferometryEarthquake Research Advances, Vol. 1, No. 4Gravity data as a faulting assessment tool for unconventional reservoirs regional exploration: The Sergipe–Alagoas Basin exampleJournal of Natural Gas Science and Engineering, Vol. 94Ambient Noise Surface Wave Reverse Time Migration for Fault Imaging22 December 2020 | Journal of Geophysical Research: Solid Earth, Vol. 125, No. 12Basement faults deduction at a dumpsite using advanced analysis of gravity and magnetic anomalies17 March 2020 | Near Surface Geophysics, Vol. 18, No. 3Faults and associated karst collapse suggest conduits for fluid flow that influence hydraulic fracturing-induced seismicity8 October 2018 | Proceedings of the National Academy of Sciences, Vol. 115, No. 43Geophysical evidence for an igneous dike swarm, Buffalo Creek, Northeast Alberta27 December 2017 | GSA Bulletin, Vol. 130, No. 7-8Giant mounded drifts in the Argentine Continental Margin: Origins, and global implications for the history of thermohaline circulationMarine and Petroleum Geology, Vol. 27, No. 7Gravity characterization of the La Rioja Valley Basin, ArgentinaMario Ernesto Gimenez, Myriam Patricia Martinez, Teresa Jordan, F. Ruíz, and Federico Lince Klinger23 April 2009 | GEOPHYSICS, Vol. 74, No. 3Magnetic imaging of intrasedimentary anomalies and their association with hydrocarbon producing fields in the Niger Delta, NigeriaS.B. Ojo, S. Oladele, and B.D. Ako14 October 2009Interpretação de Dados Aeromagnetométricos na Região do Gráben de São João, Rio de Janeiro, BrasilGuilherme B. Magioli*, Flávia Cabral Pereira, and Paulo T. L. Menezes14 September 2005 Volume 23Issue 12Dec 2004Pages: 1209–1320ISSN (print):1070-485X ISSN (online):1938-3789 publication data© 2004 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 27 Apr 2012Published in print: 01 Dec 2004 CITATION INFORMATION Henry Lyatsky, Dinu Pana, Reg Olson, and Lorraine Godwin, (2004), "Detection of subtle basement faults with gravity and magnetic data in the Alberta Basin, Canada," The Leading Edge 23: 1282-1288. https://doi.org/10.1190/leedff.23.1282_1 Plain-Language Summary PDF Download Metrics Loading ...

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

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