Seismic Imaging in Crystalline Terrains of the Superior Province, Canada
Bibliographic record
Abstract
The imaging of structures in sedimentary basins has typically been carried out using conventional seismic reflection techniques. In complex geological architectures, such as those within the Superior Province in Canada, conventional seismic imaging yields suboptimal results due to factors stemming from complex energy scattering and crooked seismic geometries. Full-waveform inversion (FWI) is a nonlinear inverse technique capable of retrieving quantitative images of velocity structures. Judicious data preconditioning, and multiscale inversion strategies allow FWI to effectively estimate velocity variations in the first few kilometers of the subsurface. Non-conventional seismic reflection processing, based on azimuthal binning and enhanced migration velocity models, improves energy focusing and imaging quality of structures at depth.\nI demonstrate that the serious nonlinearity of FWI in crystalline zones (in Larder Lake and in the northeastern portion of the Sudbury Structure), is alleviated by implementing a “multiscale layer-stripping” strategy. The strategy uses a i) combination of explosive and vibroseis sources to retrieve low-wavenumber features of the velocity background, ii) hierarchical minimization of logarithmic phase-only and conventional phase-amplitude residuals to mitigate large dynamic variations within the data, and iii) progressive inclusion of higher frequencies and late arrivals to obtain a natural transition between low- and high- wavenumber features. I illustrate that the implementation of optimum binning strategies effectively enhances signal alignment, generating in-phase stacked sections. The use of near-surface FWI P-wave velocity estimations in the construction of migration velocity models, improves the strength and continuity of reflections at depth.\nWith the application of these imaging techniques, I unravel the geometry of prominent structures in structurally-controlled mineralized zones. In Larder Lake, the Larder Lake-Cadillac Deformation Zone (LLCDZ), and the Lincoln-Nipissing Shear Zone (LNSZ), are imaged. The LNSZ is retrieved as a north-dipping fault, extending to depths not previously identified of 8 km. The Sudbury velocity model reveals the internal character of the structure, agreeing with known geology and borehole information. New estimates of thicknesses and dips are given for the northeastern portion of the Sudbury Structure.\nKeywords: Full-waveform inversion, Finite-difference modeling, Seismic imaging, Azimuthal binning, Energy focusing, Reflection processing, Crystalline environments, Larder Lake, Sudbury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".