Automation of the SLUTH method: a novel approach to airborne magnetic data interpretation
Bibliographic record
Abstract
ABSTRACT A useful property of the anomalous magnetic field associated with some simple geological bodies is that the field is homogeneous with respect to horizontal distance and height. Its horizontal and vertical derivatives are also homogeneous fields so the angle ratio of the lateral gradient over the vertical gradient is constant along rays emanating outward from a single point that represents the body location. Hence drawing these raypaths, which connect the same values at different upward continued heights along a profile, can be used to identify the location and depth of the source. This is the basis for the source location using the total‐field homogeneity (SLUTH) method. A six‐step procedure is proposed to automate the SLUTH method, allowing large amounts of data to be interpreted quickly and easily. Furthermore, the rate of decay of the field along these raypaths provides a means to estimate the type of body that is the cause of the anomaly. Once the type of body is known, a quantity related to the susceptibility can be estimated. An analysis of the results in the case when there is interaction between two proximal magnetic sheets shows a distortion when the bodies are within 600 m of each other. A similar distortion is seen when analysing field data from Timmins, Ontario, Canada, which consists of many other magnetic body models such as contacts and thin sheets. Adding noise to synthetic data shows that the estimates we obtain for the source parameters are robust for the typical noise levels seen in survey data. Because a lot of information about the characteristics of the causative geologic body can be determined using the method, it is a challenge to summarize the results on a single map. Our approach is to use different shaped symbols for each type of body, with the size of the symbol being proportional to the susceptibility‐thickness and the shade indicative of the error in estimating the position. Data from Chibougamau, Quebec, Canada are used to illustrate these display techniques.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".