Interpretation of transient electromagnetic data using zero-level curves
Bibliographic record
Abstract
Geophysical data has long been used to gain insight into the geologic structure and composition of the subsurface. The geometrical characteristics of geophysical data, such as the locations of the minimum, maximum, and zero-crossings, gives insight into the subsurface based on the physics of the measured geophysical field. These characteristics provide a preliminary understanding of subsurface properties without performing expensive 3D inversions. When examining the zero-level curve (or zero-crossings) of magnetic data collected at the surface, one finds that the zero-level curve corresponds to a conic section with properties related to the location and orientation of a subsurface magnetic dipole. The zero-level curve can be inverted using a least-squares fitting to find a corresponding magnetic dipole location and direction in the subsurface. This understanding is extended in our work to time-domain electromagnetics, where current is induced and flows inside a confined conductor. The induced current creates a magnetic dipole in the subsurface conductor that can be characterized by the zero-level curve. As the current decays, the magnetic dipole in the conductor changes. At times directly after the transmitter is turned off, the direction of current flow depends primarily on the geometry of the transmitter and conductor. As time advances, the current flow tends towards the long-axis of the conductor, and the magnetic dipole aligns perpendicularly to that long axes. Inverting the zero-level curve of the electromagnetic data at every time channel of interest enables the tracking of the magnetic dipole through time, giving insight to the orientation and location of the conductor. We examine the method of interpreting zero-level curves using synthetic examples. We also apply it to field data measured over a compact volcanogenic massive sulfide deposit at the Lalor Mine site in Canada, and demonstrate that the geometrical information obtained is consistent with the results from previous studies in the area.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".