3D Inversion of Gravity Data with Lanczos Bidiagonalization and Unstructured Mesh
Bibliographic record
Abstract
Summary When inverting potential-field geophysical data an appropriate discretization of the model is required to accurately construct complicated geometries of the causative sources. Rectangular prisms (structured meshes) have limitations to recover and preserve the edges of realistic geological sources. We use an isoparametric finite-element methodology to design an unstructured mesh for use in inverse modeling of gravity data. The calculation of the sensitivity kernel of the forward operator uses Gauss-Legendre quadrature rather than the analytic formulation. For the sake of instability of inversion operator in gravity data and to solve the Tikhonov norms due to improvements in the regularization principle associated with the stabilizing term, the Lanczos bidiagonalization technique is used. This method is accompanied with weighted generalized cross-validation (WGCV) for selecting the optimum amount of the regularization parameter. The depth weighting function is also incorporated in the formulation of the objective function to suppress the impact of shallow features and recover sources at an appropriate depth. Our algorithm is applied to real case study where a gravity survey is used for iron exploration in Yazd province, central Iran. The real example recovers geologically reasonable complex structures.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".