Joint inversions with the SimPEG framework
Bibliographic record
Abstract
Joint inversions seek to take advantage of the multiple geophysical surveys to produce spatially consistent results. There are many different approaches to joint inversion that all have their own characteristics. Performing a joint inversion is normally a cumbersome process that does not allow the practitioner to easily test different joint inversion methods. We have extended the open source python package SimPEG’s modular framework to support several different methods of joint inversion that can be used interchangeably, notably cross-gradient, joint total variation, and petrophysically guided inversion. We use this framework to investigate gravity and magnetic joint inversion characteristics for a mining carbon mineralization project that is searching for serpentinized rock. The framework allowed for us to rapidly produce different joint inversion algorithms for each method with minimal differences between the three codes. All of the joint inversions were successful at producing strongly correlated density and susceptibility models. These unified models allow us to have a higher confidence in the interpretations.
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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) | 0.027 | 0.009 |
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; both teacher heads agree on what is shown here.
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".