Inductive electromagnetic data interpretation using a 3D distribution of 3D magnetic or electric dipoles
Bibliographic record
Abstract
In inductive electromagnetics, the magnetic field measured in the air at any instant can be considered to be a potential field. As such, we can invert measured magnetic fields (at a fixed time or frequency) for the causative subsurface current system. These currents can be approximated with a 3D subsurface grid of 3D magnetic (closed-loop current) or electric (line current) dipoles whose location and orientation can be solved for using a potential-field-style smooth-model inversion. Because the problem is linear, both inversions can be solved quickly even for large subsurface volumes; and both can be run on a single data set for complementary information. Synthetic studies suggest that for discrete induction dominated targets, the magnetic and electric dipole inversions can be used to determine the center and top edge of the target, respectively. Furthermore, the orientation of plate targets can be estimated from visual examination of the orientations of the 3D vector dipoles and/or using the interpreted location of the center and top edge of the target. In the first field example, ground data from a deep massive sulfide body (mineral exploration target) was inverted and the results were consistent with the conclusions drawn from the synthetic examples and with the existing interpretation of the body (shallow dipping conductor at a depth of approximately 400 m). A second example over a near-surface mine tailing (a near-surface environmental/engineering study) highlighted the strength of being able to invert data using either magnetic or electric dipoles. Although both models were able to fit the data, the electric dipole model was considerably simpler and revealed a southwest−northeast-trending conductive zone. This fast approximate 3D inversion can be used as a starting point for more rigorous interpretation and/or, in some cases, as a stand-alone interpretation tool.
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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) | 0.000 | 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".