Monte Carlo Inversion of SkyTEMTM AEM data from Lake Thetis, Western Australia
Bibliographic record
Abstract
A SkyTEM™ airborne electromagnetic dataset was inverted using a 1D reversible jump Markov chain Monte Carlo algorithm. The inversion of each dual-moment sounding generates an ensemble of 300,000 models that fit the data. The algorithm automatically varies the number of layers in the large range of models that are tested.Analysis of the statistical properties of the ensemble yields a wealth of information on the probable conductivity distribution plus the mean, mode, median and most likely summary models. Robust information on the non-uniqueness and uncertainty of the results is also afforded by the ensemble. These are conveyed on conductivity map and section products. Estimates of the probable depths to interfaces are a further outcome. These depth estimates show great potential as an aid for mapping geological surfaces.The resulting conductivity maps and sections are coherent and appear to be geologically realistic on face value. However it is demonstrated with 3D modelling that a plausible hydrogeological interpretation on the sections is likely to be an artefact of 1D inversion of a 3D geological scenario.
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 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 | 0.002 |
| 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) | 0.002 | 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".