An Automated sparse constraint model builder for ubc-gif gravity and magnetic inversions
Bibliographic record
Abstract
Inversion of geophysical data seeks to extract a model, or suite of models, representing the subsurface physical properties that can explain an observed geophysical dataset. Due to the inherent non-uniqueness of inversion, any recovered property distribution is only one of an infinite number of possible distributions that could explain the observed data. The most desirable solutions are those that can explain the observed geophysical data and also reproduce known geological features; a goal that can only be achieved by including any available geological information into the inversions as constraints. One approach to achieving this goal of integration is to supply a full 3D model of geological observations and interpretations to the inversion and test the hypothesis that those interpretations are consistent with the geophysical data (McGaughey, 2007; McInerney et al., 2007; Oldenburg and Pratt, 2007). However, in greenfields mineral exploration where limited geological knowledge exists, it may be impossible to define such a 3D model everywhere in the region of interest. An alternate approach is to supply only the available sparse geological observations to the inversion to recover a prediction about the subsurface distribution of geological features that may be required to satisfy both the known geological constraints and the observed geophysical data. This postpones much of the geological interpretation until after the inversions have been performed and reduces the lead time to recover an inversion result and enable the results of inversions to be used in decisions to acquire further geological and geophysical data or to assist with geological interpretation. We describe a new method for preparing the geological constraints required for this sparse data approach. It is specifically targeted for use with the University of British Columbia - Geophysical Inversion Facility (UBC-GIF) GRAV3D and MAG3D gravity and magnetic inversion programs (Li and Oldenburg, 1996, 1998). The UBC-GIF inversion approach allows constraints to be assigned to each cell using four sets of parameters: ? A reference physical property which provides the best estimate of the arithmetic mean physical property in the cell. ? A smallness weight which provides an estimate of the reliability of the assigned reference physical property. The weight is a unitless value >= 1 with increasing values indicating higher confidence. ? Lower and upper physical property bounds indicating the absolute limits on the property range that can be assigned to the cell. These effectively represent a confidence interval on the supplied reference property. ? Smoothness weights controlling the variation in properties between each adjacent cell in each direction. Values > 1 promote smoother property variations between cells. Values < 1 (but > 0) promote discontinuities in properties between cells. The inversion will recover a physical property model with properties for each cell that lie between the defined bounds and are as close as possible to the supplied reference physical properties, while still reproducing the observed geophysical data. If possible, the reference physical properties will be matched more closely in those cells that have the highest smallness weights.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".