3D minimum-structure inversion of magnetotelluric data using the finite-element method and tetrahedral grids
Bibliographic record
Abstract
Rectilinear grids which are commonly used for the inversion of magnetotelluric (MT) data lack the flexibility required for representing arbitrary structures and for local refinement of the mesh. This abstract reports preliminary results of the inversion of MT data using unstructured tetrahedral grids. A minimum-structure procedure with an iterative Gauss-Newton algorithm for optimization is used. The sensitivity matrix-vector products that are required by the iterative solver are calculated using pseudo-forward problems to reduce the required computation memory. Forward problems are formulated using an edge-based finite-element technique and a sparse direct solver is used for the solutions. This solver allows saving and reusing the factorization of the matrices which greatly reduces the computation time. An example is presented which shows the capability of the algorithm to recover an anomalous region in a halfspace. The data that is inverted is the full-tensor impedance at the observation locations. Three frequencies are used for this example and computations are performed in parallel using MPI processes. The recovered model indicates the correct position of the synthetic model and reproduces the synthetic data very well. Presentation Date: Thursday, October 20, 2016 Start Time: 10:10:00 AM Location: 141 Presentation Type: ORAL
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".