Regional airborne gravity surveys in Western Australia: Considerations for the end user
Bibliographic record
Abstract
SummaryRegional airborne gravity surveys are being acquired over much of the State of Western Australia by the Geological Survey of Western Australia (GSWA) and Geoscience Australia (GA) to provide coverage where existing ground gravity coverage is sparse. The acquisition and processing of these surveys poses several challenges.The data acquired by Sander Geophysics (SGL) using the AIRGrav system in Western Australia during 2018 was done so without control lines for reasons of cost efficiency, relying on the ground gravity to provide the necessary levelling corrections. Methodologies have been developed to achieve effective levelling under these circumstances, although the final result varies depending on the methodology used. Data acquired on earlier surveys with control lines are being used to compare and contrast to data acquired without them. Ongoing power spectrum analysis suggests a way in which the different methods may be judged objectively.Horizontal components of gravity are also acquired by AIRGrav. Levelling these components is a challenge under all circumstances. The relationships between the components expressed in potential field theory allow the different components data to be compared and checked for consistency.Digital elevation model (DEM) data acquired during the surveys provide a means for checking other sources of DEM typically employed for applying terrain corrections. The impact of inaccurate DEM data on the corrected gravity data overall is small but can be locally significant. Data quality of the regional surveys is high, but the end user should be aware of the limitations posed by the choices made in data acquisition and processing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".