Intelligent meshing technique for 2D resistivity inverse problems
Bibliographic record
Abstract
ABSTRACT In geophysical inverse problems, an a priori structured mesh is often used for inversion and mesh refinement is applied if needed by the user after observation of inversion results. We have developed a new intelligent self-adaptive unstructured finite-element meshing technique for electrical resistivity tomography inverse problems. This new approach uses Harris corner-and-edge detectors that are based on the local autocorrelation function of 2D distribution of pixels. This meshing technique optimizes the size of the inverse problem by refining areas where variations in the physical property structure are sensed to be important. The meshing technique also generates a more appropriate and optimum mesh for the inverse problem that is dependent on the problem itself. Tests on modeled data have demonstrated that the proposed intelligent meshing technique can reduce data misfit, produce a better reconstruction of the true physical properties, and minimize the size of the inverse problem. The synthetic model consists of a conductive dike in a resistive medium. By applying the proposed intelligent meshing technique, the inverse model of the dike is very similar to the inverse model produced using fine meshes, and it is also better reconstructed than the inverse model produced using conventional meshes. We have also applied the intelligent meshing technique to survey data collected for groundwater-saltwater mapping and characterizing the subsurface conductive structure with topography included. Our results indicate that the new meshing technique can produce solutions that are comparable with standard meshing and fine meshing techniques, while optimizing the size of the inverse problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".