Ambient noise tomography of an iron-oxide copper–gold (IOCG) deposit under thick cover
Bibliographic record
Abstract
As most outcropping and shallow mineral deposits have been found, new technology is imperative to finding the hidden critical mineral deposits required for the renewable energy transition. One such seismic technique, called ambient noise tomography (ANT), has shown promise in recent years as a low cost and low environmental impact passive method of three-dimensional (3D) imaging of shear-wave velocity structure of the Earth. Over the last twenty years the method has been well-established in academia to image crustal and regional scale geological features but has seldom been used at the mineral exploration deposit-scale. In this paper we show the application of seismic ANT at an IOCG deposit in South Australia under more than 750 m of sedimentary cover. A 100-site survey in a 10 by 10 grid with site spacing of 1 km, using 3-component nodal seismometers with a natural-frequency of 5 Hz, was conducted over a two-week period. Data were inverted to generate a 3D velocity model to a depth of 2 km. When compared to drillholes in the survey area, the tomographic model delineates cover sequence lithologies and the depth of crystalline basement. A velocity anomaly in the basement has characteristics of a potential IOCG deposit and is aligned with a gravity anomaly due to brecciated haematite. The results of the paper indicate that ANT is a useful tool for deep cover mineral exploration that can potentially expedite the discovery of new deposits.
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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 | 0.000 |
| 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 | 0.000 | 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 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".