A new regional/residual separation for magnetic data sets using susceptibility from frequency-domain electromagnetic data
Bibliographic record
Abstract
ABSTRACT Regional-residual separation is a fundamental processing step required before interpreting any magnetic anomaly data. Numerous methods have been devised to separate deep-seated long-wavelength (regional) anomalies from the near-surface high-frequency (residual) content. Such methods range in complexity from simple wavelength filtering to full 3D inversions, but most procedures rely on the assumption that all long-wavelength anomalies are associated with deep source bodies: an incorrect assumption in some geologic environments. We evaluated a new method for determining the contributions of near-surface magnetic sources using frequency-domain helicopter-borne electromagnetic (HFEM) data. We inverted the in-phase and quadrature components of the HFEM data to produce an estimate of the spatial variation of magnetic susceptibility. Using this susceptibility information along with known topography and original survey flight path data, we calculated a magnetic intensity grid by forward modeling. There are two immediate benefits to this approach. First, HFEM systems have a limited effective depth of penetration, within the first hundred meters from the surface, so any magnetic sources detected by this method must be located in the near surface. Second, the HFEM-derived susceptibility is completely independent of magnetic remanence. In contrast, apparent susceptibility computed from the original magnetic intensity data incorporates all magnetic signal sources in its derivation. Crossplotting of MSHFEM versus MSTMI served to reveal areas where the observed magnetic field was dominated by magnetic remanence and provided an estimate of the polarity of the remanence contribution. We evaluated an example, and discussed the limitations of this method using data from an area in the Bathurst Mining Camp, New Brunswick. Though it is broadly successful, caution is needed when using this method because near-surface conductive bodies and anthropogenic sources can cause erroneous HFEM susceptibility values, which in turn produce invalid magnetic field estimates in the forward modeling exercise.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".