3-D inversion of magnetic induced polarization data
Bibliographic record
Abstract
The magnetic induced polarization (MIP) method is an exploration technique used to obtain information relating to the induced polarization characteristics of the subsurface through measurements of the primary magnetic field associated with steady-state current flow in the earth. According to Seigel, the secondary magnetic field due to polarization current can be expressed as a sum of the products of chargeability and the derivative of primary magnetic field, due to ohmic current, with respect to the logarithmic conductivity (or sensitivity). The magnetic field and the sensitivity matrix can be computed by subsequently solving Poisson’s equation and a magnetostatic problem in terms of potentials using a finite-volume algorithm. The MIP response is a function of chargeability difference (η-η0) and relative conductivity (σ/σ0), where η0 and σ0 are constants.When solving the inverse problem we need to impose positivity of the solution but the fact that MIP responses depend only upon the difference in chargeability means we have options regarding how we set up the inversion. We can: (1) invert for η without constraints and add a constant to the final result, (2) invert for η while imposing positivity, or (3) work with In η. We compare all three methods here. Our inversion problem is formulated as an optimization problem where the objective function of the model is minimized subject to the constraints mat the model adequately reproduces the data. We use a Gauss-Newton method to obtain the model perturbation at each iteration. The system of equations is solved using a conjugate gradient least squares method. In order to make the inversion produce depth or distance information, a depth weighting or sensitivity-based weighting is required.Through synthetic model studies, we have shown mat the conductivity ratio between a target and its host has a large effect on the MIP response. Ratios greater man two orders of magnitude difference will eventually make the MIP response undetectable. However, if the ratio is in the range of 0.1 to 10, the effect on the recovered chargeability is limited. The inversion algorithm is demonstrated by inverting the data set from Binduli, Australia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".