Minimisation of the Gravity Response from Mine Infrastructure: An Example from Sons of Gwalia Mine, WA
Bibliographic record
Abstract
In near mine exploration, gravity surveys are generally detailed and mine infrastructure such as waste dumps, tailings dams and open pits, if not taken into account, can often mask the gravity response from the bedrock geological sources.This was the case at the Sons of Gwalia Mine near Leonora, Western Australia, where a gravity survey on a nominal 100x100m spacing was undertaken to assist in improving the geological framework. Large scale open pit and underground mining activities over the previous 20 years had resulted in significant mine infrastructure. Standard reductions of the gravity data showed a number of anomalous responses that correlated with the waste dumps and tailings dams. Hence there was a requirement to remove the gravity response of the mine infrastructure in order to maximise the response from bedrock sources and thus improve the interpretation.The methodology used to minimise the effect of mine infrastructure on the gravity data involved three-dimensional forward modelling and removal of the gravity response of the waste dumps and tailing dams prior to conventional terrain correction. For the estimated densities adopted, the maximum infrastructure response was 3.6 mgal.The results indicate that the bulk of the effects from mine infrastructure have been removed, allowing a clearer picture of the gravity response from bedrock geological sources. Some residual gravity response from the infrastructure, particularly the southwestern tailings dam, is apparent. Its removal would require a refinement of the forward modelling of the mine infrastructure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".