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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".