Target delineation using Full Tensor Gravity Gradiometry data
Bibliographic record
Abstract
SummaryFTG Gravity data acquired on airborne and marine platforms measure 5 independent Tensor components that collectively describe a total gravity field. The components capture unique signature patterns related to specific attributes of target geology that when collectively interpreted enable detailed imagery of the target itself in terms of geometry, composition and depth of burial.The horizontal tensor components Txx, Tyy, Txy, Txz & Tyz are commonly used to identify and map lineaments associated with structural and/or stratigraphic changes or target geometry in a survey area. The vertical tensor component, Tzz, is used to estimate depth and predict compositional information related to target geology. However, these components have traditionally been interpreted separately from one another and often run the risk of missing out on key information.This paper describes application of a semi-automated approach that combines the individual components into singular representations to best extract the signature pattern common to all components as revealed by the underlying geology. The examples presented are taken from an Air-FTG® survey onshore Brazil to image the structural framework and identify target geology ahead of a seismic programme, and a Marine-FTG® survey offshore Norway to resolve salt body geometries imaging areas of overhang development.The resultant interpretation enables the end-user to fasttrack the exploration initiative by quickly evaluating target geology for detailed follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".