DATA WEIGHTING SCHEMES FOR LARGE SCALE AEM INVERSION
Bibliographic record
Abstract
Inverse methods attempt to reconstruct a geophysical model which satisfies a set of geophysical data while conforming to some geologic prejudice. Since measured data always contain some level of noise, one goal of any inversion algorithm is to fit the measured data to a level such that the geologic signal is fit, but the noise is not. There several different methods for obtaining the optimal fit such as generalized cross validation, L-curve analysis, and chi squared cutoff. All of these methods assume that one global measure not only applies to the entire survey domain, but generally to every part of the survey domain. However, this is often not the case with large scale surveys. The rate of convergence and number of iterations required to adequately fit observed data is largely dependent on the geologic complexity and how close the initial model is to the final model. In large scale surveys, there may be areas where the geology is well approximated by a homogenous half-space, others areas with variable overburden, and still other areas with strong contrasts and great geologic complexity. If one uses a simple global cutoff without regard for the varying geologic regimes, it will likely lead to regions of over-fit data and regions of under-fit data. Since this issue is largely one of data fit, we suggest an adaptive data weighting scheme which takes into account the data fit over regions at the end of each iteration, and then adjusting the data weights to ensure that each geologic regime or area is fit to approximately the same level. We demonstrate the technique with a RESOLVE data set acquired near Ft. Yukon, Alaska. We compare several different methods of adaptive data weighting with the conventional chi squared cutoff approach.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".