APPLICATION OF 3D CSAMT INVERSION TO VARIOUS DATA COMPONENTS AND ITS ENHANCEMENT
Bibliographic record
Abstract
We have developed 3D inversion algorithms for CSAMT data incorporating a constrained trust-region technique. It is a quasi-Newton method with a fast convergence rate. The inversion can invert, separately or jointly, the electric field, magnetic field and impedance data. There are advantages to this flexibility of inverting such combinations of data components. Inverting certain data components can provide additional insight into the information inherent in the data. It is of importance to select reliable data components to utilize in the inversion. The data selection may involve checking the noise level of each data component and the consistency of electric field and magnetic field, verifying the reliability of impedance data, and choosing frequencies to utilize. We create a synthetic survey and the data are contaminated with various levels of Gaussian random noise. We compare the results of inverting various data components and address importance of selecting appropriate components. A field data case study is presented as well. We also have investigated the possibility of enhancing inversion resolution in a situation where the strike and the dip of the structures is reasonably well known. For such cases, we have experimented with an inversion grids in which the grid cells have a prescribed strike and dip then invert for a conductivity distribution within the dipping grid. Our results appear to demonstrate that incorporating the strike and dip into an inversion significantly improve the resolution of the recovered model at depth. Thus, pointing to another aspect for inversion into research applications separate from the inversion techniques, constraining parameters or the accuracy of the forward simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".