A multi‐parameter approach for recognition of anthropogenic noise in aeromagnetic data collected over populated areas: Erzgebirge, Germany
Bibliographic record
Abstract
ABSTRACT Aeromagnetic data are routinely acquired by mineral exploration programmes. The objective is to obtain a raster image of the spatial variations of magnetic field intensity; these variations are associated with mineralogical variations in the subsurface. When the survey is conducted in a populated area, much of the signal, however, may be associated with anthropogenic sources such as buildings and roads. Identification and minimization of the anthropogenic‐related signal then are essential to derive a useful product for geological mapping. In this work, we examine a scalar magnetic dataset from Geyer, Saxony, and we apply five approaches for locating regions of anomalous anthropogenic signal: signal amplitude, absolute fourth difference, signal standard deviation, enhanced horizontal gradient and curvedness. All are shown to produce similar responses, and the summation of the five results compares favourably with the standard Keating kimberlite (circular anomaly) approach for detecting anthropogenic signals. Complications arise when geological features produce signals of similar amplitude to anthropogenic sources. Differentiating the probable origin of any specific pattern can be assessed by using a 2D shape index and increased flight height. Verification of an anthropogenic anomaly is achieved by comparison of anomalous solution grids with Geographic Information System‐based reference 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".