THE UNIVERSITY OF CALGARY An Analysis of the Combination and Downward Continuation of Satellite, Airborne and Terrestrial Gravity Data
Bibliographic record
Abstract
An analysis of the combination and downward continuation of satellite, airborne and terrestrial gravity data is presented. The thesis encompasses theoretical investigations of the underlying model problems and a numerical study using simulated and real data. The downward continuation of gravity data is inherently unstable and requires reg-ularization, iteration or filtering methods to obtain a stable solution. This research scrutinizes several regularization methods for the downward continuation of airborne and terrestrial gravity data. All of them can be considered as filtered least-squares solutions. Additionally, an alternative numerical method is developed and compared to the other methods. It provides a considerable improvement in terms of numerical efficiency and accuracy when the assumptions for the method are satisfied. With newly available satellite gravity data, additional low-frequency information about the gravity field can be obtained. Since applications such as geoid determi-nation and resource exploration demand a much higher resolution than resolved by satellite-only models, the combination with data collected closer to the Earth’s surface is essential. In this research, local airborne and terrestrial gravity data have been used. Several combination strategies are proposed and implemented. The feasibility of the combination strategies is demonstrated for a local data area close to Ottawa, Canada. The results indicate that a combination of satellite, airborne and terrestrial gravity is beneficial both in terms of accuracy and resolution. A combined local geoid and a high-degree spherical harmonic model up to degree and order 900 are developed. The results show a geoid accuracy in the cm-range for the test area. iii
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".