Gravity Gradiometer Systems – Advances and Challenges
Bibliographic record
Abstract
SummaryGravity gradiometry has been heralded as one of the top five developments in advancing airborne geophysics in the past decade. There are presently nine deployed gradiometer systems operating in various configurations (partial tensor and full tensor) on numerous platforms in support of global exploration activities. There are also numerous development programs underway with an aim of producing lower noise gradient measurements. We will review the broad scope of developments in gravity gradient instrumentation, with a view toward how the projected improved performance will require greater attention to other error sources. It is easy to see how improved gradient data will benefit the explorationist, yet lower noise sensors alone do not provide the answer. Improved operational capability will need to come from lower sensor and system noise, as well as addressing the external error sources associated with terrain and geology. This paper discusses a wide range of technologies and operational scenarios under development to achieve a robust gravity gradient measurement. The significant challenges associated with improved gravity gradiometer operational capability including vehicle dynamic noise, terrain noise, geologic noise and other noise sources will be a key focus of this paper.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".