A simple adaptable data fusion methodology for geophysical exploration
Bibliographic record
Abstract
We present a simple and adaptive method of data fusion using grey-scale grids for general geophysical exploration. The methodology relies upon: (1) understanding the physical property variations that might be associated with the mineral exploration target, and (2) applying appropriate (forward or inverse) grey-scaling to each input dataset so that before addition of the grids the anomalous patterns all express the phenomena of interest in the same sense (i.e. all positive anomalies). If the resulting fused dataset has a Gaussian population distribution then a linear grey-scale is applied to the data within the 95% (2σ) confidence interval; if it is non-Gaussian then the linear grey scale is applied to the entire dataset.The methodology has been applied to very low frequency (VLF), aeromagnetic and radiometric data measured during the 1980s over the Hemlo disseminated lode-gold deposit. The resulting fused data derived from our methodology produces a coherent region of anomalous geophysical response that is coincident in location and geometry to the surficial extent of the known mineralized zone of the deposit. Integration of multi-sensor response has the added advantage of significantly reducing the number of false-targets. Further, this method also illustrates the continued benefits that can be obtained from re-evaluation of older data.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".