Improved controlled source audio‐frequency magnetotelluric method apparent resistivity pseudo‐sections based on the frequency and frequency–spatial gradients of electromagnetic fields
Bibliographic record
Abstract
ABSTRACT Although most electromagnetic data can be inverted to actual resistivity, ways of quickly getting a real‐time interpretation of a data set are still valuable. Such methods are useful when we are testing instrumentation or assessing data quality during a survey, or when we need to get a general understanding of the geological structure during a field survey. Apparent resistivity is a good way to satisfy these desires. However, one of the disadvantages of apparent resistivities is that the traditional apparent resistivity formulations are poor at recognizing boundaries, mainly because abnormal responses get stretched into deeper parts of the image (a shadow effect). In order to improve the recognition ability of boundaries, we propose improved apparent resistivity pseudo‐sections based on the formulae for the frequency and frequency–spatial gradients of the fields in the far‐field region of frequency‐domain controlled‐source audio‐frequency magnetotelluric surveys. The new pseudo‐sections are found to be better than those produced from a traditional method when applied to a number of 3‐D examples. The performance of this apparent resistivity method is closely related to using an appropriate transmitter–receiver distance: when a proper value is used, good results can be obtained in which the horizontal locations of vertical boundaries and the positions of top and bottom boundaries can be identified clearly. Finally, the usefulness of the proposed method for practical applications is evaluated with a field‐data example, for which the results of the proposed apparent resistivity imaging method are compared with traditional apparent resistivities, as well as with the results from a 2‐D inversion of DC resistivity data from the same survey line and with what is known about the geology of the area. This comparison demonstrates the improved capabilities of the new apparent resistivities over traditional approaches, including an improved capability to accurately reveal the bottoms of targets.
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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 | 0.001 | 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) | 0.001 | 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".