3-D joint inversion of airborne gravity gradiometry and magnetic data using a probabilistic method
Bibliographic record
Abstract
SUMMARY A probabilistic approach is presented for jointly inverting gravity gradient and magnetic data for 3-D subsurface distributions of density and magnetic susceptibility. The coupling of the physical property models is incorporated in the inversion by using a cross-covariance matrix of density and magnetic susceptibility. This enables structural similarity such as the orientation and spatial extent of the sources and cross-variance between the two physical properties to be incorporated. A user-defined correlation coefficient can control the level of similarity between the two models. By applying a marginalizing algorithm in the joint inversion, the inversion domain is allowed to be partitioned into various zones, each of which can have its own covariance, cross-covariance matrix, as well as correlation coefficient, depending upon the feature and similarity of sources. Thus, sources with different shapes, sizes and relationships between the two physical properties can be simultaneously recovered. The validity of the method is verified using three synthetic examples, which demonstrate how incorrect parameters of the cross-covariance matrix affect the inverted results. Finally, the proposed method is successfully applied to full tensor gradiometry and magnetic data collected over the Budgell Harbour Stock (BHS) intrusion in north-central Newfoundland, Canada. Compared with models generated from independent inversions, better definition and localization of the main intrusion, as well as associated lamprophyre dykes at shallow depth, are achieved by using the joint inversion. The resolved physical properties for the intrusions show good agreement with field observations of lamprophyre dykes in proximity to the BHS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".