3-D joint inversion of electrical and magnetometric resistivity data
Bibliographic record
Abstract
AbstractThe DC electrical resistivity (DC) and magnetometric resistivity (MMR) are two geophysical techniques applied in exploration of mineral resources and in solving environmental and problems.Conventionally, these two techniques are used separately to infer the subsurface conductivity. Since each of these methods has its own relative merits and limitations, a joint use is expected to provide complementary information, and possibly reduce the survey costs as well. In this paper, we present a unified algorithm to compute the potential and magnetic fields that arise from buried source electrodes. This is done in two consecutive steps by solving a Poisson’s equation for a scalar electrical potential and a magnetostatic equation for a vector magnetic potential, respectively. The inverse problem is formulated as an optimization in which both observed potential and magnetic field data are fit to a certain degree, and at the same time, the recovered model has a minimum structure. The standard Gauss-Newton algorithm is used to obtain the model pertubation at each iteration. The code is verified with a synthetic model.Keywords3-DInversionMagnetometric resistivity
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".