Integrated seismic interpretation of the Larder Lake area, Southern Abitibi greenstone belt, Ontario, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".