Improved use of the local wavenumber in potential-field interpretation
Bibliographic record
Abstract
Abstract Fast interpretation of potential field data (magnetic data are a typical example) often uses simple geometries to describe a complex geologic reality. Many of these techniques assume that the potential field arising from the source body is homogeneous. The degree of homogeneity of a source is characteristic of its geometry. However, very few source geometries are known to generate a homogeneous field. The contact, thin sheet, horizontal cylinder, pole, and dipole all cause a homogeneous magnetic field. More complex geometries such as the thick dike or rectangular prism do not. Therefore, a major problem is to check for the validity of the homogeneity hypothesis when these types of interpretation techniques are used. The local wavenumber of a potential field calculated at a series of increasing heights above the measurement datum can be used to directly compute the depth to a source and its degree of homogeneity. In addition, the vertical derivative of the local wavenumber can provide an estimate of the depth to sources without knowledge of their degree of homogeneity. The proposed technique also allows us to test if the source is homogeneous or not, and it applies to any type of potential field data. The technique breaks down on synthetic magnetic data when anomalous sources are closer than about four times their depths. This behavior is expected from interpretation techniques that use upward continuation. The technique can be applied to profile and gridded data. Its main advantage is that it allows testing the homogeneity hypothesis and therefore the validity of the interpretation.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".