Iterative migration of gravity and gravity gradiometry data at Bathurst Mining Camp
Bibliographic record
Abstract
Three-dimensional (3D) inversion of full tensor gradiometry (FTG) data continues to be an active area of research and development. We have recently developed a method of potential field migration, which extends to the case of the potential field the general principles of seismic and electromagnetic migration. This new approach provides a rapid method for direct transformation of observed gravity and gravity gradient data into spatial distributions of density. We demonstrate in this paper that migration can be applied iteratively to get more accurate subsurface distributions of the physical properties of rocks. We show that, the iterative migration is practically equivalent to the basic gradient-type inversion algorithms with one very important difference: the gradient directions on each iteration are determined by migration of the corresponding gravity, and gravity gradient data. This is significant because the last transformation is very well developed in the theory of potential field interpretation. In other words, the iterative migration makes it possible to use the powerful and stable technique of upward continuation for the solution of the inverse problem. We present a model study and a case study for the 3D iterative imaging of FTG data from New Brunswick, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".