3-D CSEM inversion for the complex model with topography using an efficient dual parallel approach
Bibliographic record
Abstract
There is a significant interest in improving the efficiency of 3-D CSEM inversion and obtaining more reliable inversion results. A 3-D CSEM inversion code using unstructured tetrahedral elements has been developed in order to consider the topographic effect by directly incorporating it into computational grids. In the forward modeling, the electric dipole source is divided into a set of short electric dipoles to simulate its practical shape, size and attitude. We adopt the edge-based finite-element method to discretize the electric field equation. In the inversion, the inversion grids are entirely independent of the forward grids. The lower and upper bounding constraints on model parameters are used to improve the reliability of the inversion result further. We use the Gauss-Newton algorithm to minimize the inversion objective function and obtain the underground conductivity model. The calculation of the forward modeling and the sensitivity matrix spends most of the time in the inversion. At present, most inversion codes use frequency-based parallel methods to accelerate the inversion, to further improve the efficiency of 3D CSEM inversion, except for the frequency-based parallel methods, we use the open-source software METIS to divide the model into several parts and then use the MPI-based parallel toolkits (such as PETSc and MUMPS) to solve the forward linear equations. The same parallel scheme can also be used to calculate the sensitivity matrix. Finally, we can further improve the efficiency of 3-D CSEM inversion by the dual parallel strategy based on the frequency and domain decomposition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".