Secondary indirect effects in gravity anomaly data inversion or interpretation
Bibliographic record
Abstract
The application of topographic corrections to gravity anomalies and disturbances, and their use in formulating and solving the gravimetric inverse problem are reinvestigated. The gravity anomaly, whose definition is based on the disturbing potential by means of the fundamental gravimetric equation, rather than by the vertical derivative of the disturbing potential, differs from the gravity disturbance, which also has implications to the application of the topographic correction. We demonstrate that the application of the topographic correction to the gravity anomaly gives origin to the secondary indirect topographic effect (SITE) and that the formulation of a rigorous relation between the attraction of anomalous subsurface mass density distribution and the gravity anomaly gives rise to a secondary indirect effect of the anomalous mass density distribution (SIEAM). The SITE is shown to be numerically significant in mountainous areas, where it can reach 100 mGal. Because of secondary indirect effects, the gravity anomaly in its rigorous sense is not well suited for the gravimetric inversion. Instead, the topo‐corrected gravity disturbance best fits the needs of gravity data inversion or interpretation, as it exactly matches the attraction of the Earth's subsurface anomalous density distribution. It is pointed out that, in geophysics, the gravity data used for inversion or interpretation, although called the “Bouguer gravity anomaly,” even if preceded by the adjective “ellipsoidal” as by the newly proposed standards for the North American database, are by the standards of rigor the “topographically corrected gravity disturbance.”
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".