MétaCan
Menu
Back to cohort
Record W7010643099

Integrated seismic interpretation of the Larder Lake area, Southern Abitibi greenstone belt, Ontario, Canada

2020· dissertation· en· W7010643099 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2020
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGreenstone beltShear zoneArcheanSeismic inversionInversion (geology)Seismic tomographyInterpretation (philosophy)Pluton
DOInot available

Abstract

fetched live from OpenAlex

The southern Abitibi greenstone belt is characterized by a series of complex metavolcanic and metasedimentary rocks intruded by granitic plutons and batholiths, which makes the area a hardrock environment. In the Larder Lake area, these stratigraphic units are truncated by two major breaks, the Lincoln Nipissing shear zone (LNSZ) and Cadillac-Larder Lake deformation zone (CLLDZ) which trend NE-SW and E-W, respectively. This thesis is focused on the quantitative interpretation of a ∼44 km long seismic transect acquired as part of the Metal Earth project in the Larder Lake area. Seismic imaging and interpretation in a hardrock environment are challenging due to the lack of continuity of reflections and smaller acoustic impedance contrasts between different stratigraphic units. Hence, structural interpretation of the seismic data is favoured rather than stratigraphic interpretation. The application of curvelet transforms and seismic attribute analysis significantly increased the signal to noise ratio (SNR) of the seismic data. Seismic data have pitfalls in imaging the subsurface geology in a hardrock environment due to a strong degree of structural heterogeneity and complex geometry of targets. These pitfalls are overcome by the integration of seismic data with complementary geophysical methods. This study aims to determine the structural architecture of the area using seismic data and other depth resolving geophysical methods using an integrated modelling approach. The integrated modelling is achieved by extracting anomalies from the geophysical inversion models. The study uses the empirical relationship between physical properties derived from all the datasets and integrates these in a spatial domain. Physical properties from the individual models are differentiated by applying unsupervised learning algorithms for characterization. The recovered physical properties show correlations with the seismic data along deformation zones.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.163
Teacher spread0.157 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueLu Zone Ul (Laurentian University)Same topicSeismic Imaging and Inversion TechniquesFrench-language works237,207