Reliable Euler deconvolution solutions of gravity data throughout the β-VDR and THGED methods: Application to mineral exploration and geological structural mapping
Bibliographic record
Abstract
Euler deconvolution (ED) is mainly used to estimate the locations and depths of magnetic bodies. This technique can also be applied to gravity anomalies but requires caution, as Euler solutions directly obtained from gravity anomalies may provide misleading results. In addition, the traditional Euler deconvolution generates many spurious solutions and is noise-sensitive. This research presents an improved method for the ED of gravity anomalies. This method is based on a finite-difference method (β-VDR) that provides robust vertical derivatives of gravity anomalies, and the total horizontal gradient-based edge detection method (THGED) used to select the Euler solutions, filtering out spurious solutions. Our method is exemplified with two synthetic gravity models and two real datasets from the Voisey's Bay deposit (Canada) and the Hanoi basin (Vietnam). The advantage of the proposed method is that it can provide the depths more accurately and is less sensitive to noise than some modified ED methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".