Geoid determination using airborne vector gravimetry: Insights from a real dataset
Bibliographic record
Abstract
Abstract In this contribution, we use all three components of the gravity vector observations to compute a regional geoid and demonstrate the advantages of using the horizontal components alongside the vertical component. We apply the one-step integration method within the remove-compute-restore framework; where the long-wavelength part of the geoid is recovered from Earth’s gravitational models while the harmonicity of the computational space is ensured by removing the topographic effects. We create a system of linear equations using a discretized form of the one-step integration method and use the Tikhonov technique to deal with the numerical instability due to its implicit downward continuation and to determine the geoid at higher resolution, e.g., 1 ′ × 1′. We propose a novel method to estimate the Tikhonov regularization parameter using the discrepancy principal and a stable solution of the geoid at lower resolution, e.g., 3′ × 3′. The results reported are based on real airborne gravity vector observations collected over Colorado, USA. The scattered observations at flight level are directly inverted to the disturbing potential at grid points on the reference ellipsoid, where geoid heights are then computed using Bruns formula. We evaluate the external accuracy of the geoid by comparing it with GNSS/levelling data and estimate the location-based internal uncertainties (error) of the geoid heights through formal error propagation. As part of this contribution, the airborne gravity vector data used in this study are also available for research purposes upon request to the corresponding author.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".