Inversion of Conductivity Profiles from EM Using Full Solution and a 1-D Laterally Constrained Algorithm
Bibliographic record
Abstract
Abstract In highly conductive environments the apparent electrical conductivity (σa) data generated from electromagnetic (EM) instruments are known to be non-linear. This is particularly the case when high conductivity bodies are present in the subsurface. However, little attention has been given to this issue in the research literature of the environmental and hydrological sciences. In this paper we describe the development of an inversion algorithm, which consists of a 1-D inversion with 2-D smoothness constraints between adjacent 1-D models, whereby the forward response is calculated using the full solution of the induction phenomena. The robustness of the algorithm is evaluated using σa data acquired from two study areas. In the first case study, σa data is acquired with a DUALEM-21 across a golf green in Guelph, Ontario Canada. In the second case study, a DUALEM-421 is used to collect σa across an irrigated field located on a clay alluvial plain of the Lower Gwydir Valley (Australia). The general patterns of modeled true electrical conductivity (σ), as achieved from our inversion algorithm with the full solution, are shown to compare favorably with the available information and existing knowledge at each site. We also find that the models calculated with the new algorithm compare favorably with those obtained using individual 1-D inversion.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".