Large-scale inversion of gravity gradiometry with differential equations
Bibliographic record
Abstract
Summary With recent advances in technology, geophysicists are able to acquire large-scale airborne gravity gradiometry data sets for oil and gas, and mineral exploration. The inversion of these data are advantageous by giving the interpreter a 3D model to interpret. The number of data and model parameters associated with these data sets make an inversion difficult to carry out without substantial computational resources. In this work, we present a finite-volume, differential-equation method for gravity gradiometry data inversion. The computation of the sensitivity times a vector and its adjoint is done by solving a fourth-order Poisson equation using a multigrid method. The forward modeling is set up as a solution of a linear inverse problem so that the sensitivities are never explicitly formed. This allows us to dramatically increase the storage capacity of the problem and invert regional problems. To demonstrate the effectiveness of our method, we present an inversion of the Bathurst Mining Camp region on a personal computer that consists of 1.4 million data and a mesh of 24 million cells.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".