Depth Sensitivity of Apparent Magnetic Susceptibility Measurements using Multi-coil and Multi-frequency Electromagnetic Induction
Bibliographic record
Abstract
ABSTRACT Apparent magnetic susceptibility (MSa) as recorded by electromagnetic induction (EMI) instruments could offer relevant information about non-soil subsurface features. It is less affected by natural soil properties than its prominent counterpart, i.e., apparent electrical conductivity (ECa). Hence, MSa is generally a promising approach to investigate artificial inclusions and structures in soil. However, while the origin depth of EMI based ECa is widely accepted, the depth sensitivity (DS) of MSa measurements remains poorly understood. The depth interpretation of MSa is particularly challenging due to negative values especially for objects that are randomly distributed over different depths. Here we assessed the performance of both multi-coil (MC) and multi-frequency (MF) EMI sensors for identifying and determining the DS of MSa measurements in shallow soils through detection of buried small targets of known conductivity. Two experiments were conducted in a sandy loam podzolic soil in western Newfoundland, Canada. Materials of different conductivities, including metal and plastic targets, were buried at depths between 20 and 80 cm. Three inter-coil separations (32, 71 and 118 cm) of the MC sensor and four factory-calibrated frequencies (18, 38, 49 and 80 kHz) of the MF sensor were tested in both horizontal and vertical coil orientations. The MC sensor clearly detected all four metal targets from three coil separations in both coil orientations while the MF sensor identified more anomalies than targets limiting its information value. Based on the measurements from MC and the theoretical DS function, a criterion was developed and validated to assess the potential depth origin of MSa. We found that negative or less than the background values occur, if the depth of the target is shallower than 0.36 times the coil distance of the employed EMI sensor. According to this criterion, the depth origins of metallic targets were correctly identified under the assumption of low induction numbers, even if values were negative.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".