Moving between dimensions in electromagnetic inversions
Bibliographic record
Abstract
Summary Electromagnetic (EM) methods are used to characterize the electrical conductivity distribution of the earth. EM geophysical surveys are increasingly being simulated and inverted in 3D, due in part to computational advances. However, the availability of computational resources does not invalidate the use of lower dimensional formulations and methods, which can be useful depending on the geological complexity as well as the survey geometry. Due to their computational speed, simulations in 1D or 2D can also be used to quickly gain geologic insight. For example, this insight can be used in an EM inversion starting with a 1D inversion, then building higher dimensionality into the model progressively. As such, we require a set of tools that allow a geophysicists to easily explore various model dimensionalities, such as 1D, 2D, and 3D, in the EM inversion. In this study, we suggest a mapping methodology that transforms the inversion model to a physical property for use in the forward simulations. Using this general methodology, we apply an EM inversion to a suite of models in one, two, and three dimensions, and suggest the importance of choosing an appropriate model space based on the goal of the EM 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".