A field test of 3D reflection orientation analysis along a 2D crooked line in northern Finland supplemented with additional cross-spreads
Bibliographic record
Abstract
Reflection seismics is indispensable for understanding the structural framework of the crust and providing important constraints on mineral system studies. The non-uniqueness inherent in interpreting data from2D crooked seismic profiles acquired over complex geological structures can be reduced by performing 3D reflector orientation analysis (Calvert, 2017), but this requires good azimuthal coverage, which can be enhanced with the deployment of cross-spreads. A new 70-km long reflection seismic profile was acquired across the Palaeoproterozoic Peräpohja belt in northern Finland to shed new light on its structural framework and contribute to development of the new national mineral exploration program. Single-receiver and single-source acquisition was implemented, resulting in excellent data quality. Survey layout was optimized to extract 3D reflector orientations, and included eight additional cross-spreads extending up to 5 km from the survey line spaced every 7-8 km.3D reflector orientation analysis was performed for both the inline data (i.e. along the main profile) as well as with the cross-spreads included. The main challenge to processing these data is obtaining an optimal refraction statics solution: in the first pass, a combination of 2D inline statics with 2D statics for each cross-spreads was applied. In the second pass, a 3D tomostatics solution was obtained for the complete dataset. The initial results of the reflection orientation analysis suggests that while the additional effort in acquiring the cross-spreads may not be justified for obtaining the structural image (cross-spreads bring more noisy data), orientation attributes (dip and strike) are better resolved, especially at shallower levels, and where gaps in azimuthal coverage are present (i.e. the profile was too straight). With current acquisition capabilities, cross-spreads can be acquired in a cost-effective manner, yet they should be carefully planted to provide reasonable signal-to-noise ratio data, essential for 3D statics and for the orientation analysis itself.The new seismic data were acquired as a part of the REPower-CEST “Clean Energy System Transition” project, which received funding by the European Union (number 151, P5C1I2, NextGenerationEU).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".