Cooperative joint inversion of 3D seismic and magnetotelluric data: With application in a mineral province
Bibliographic record
Abstract
ABSTRACT The integration of different geophysical data has the potential to provide more accurate estimate of subsurface rock properties. Several methodologies and attempts have been developed over the years with the objective of reducing exploration risk. We have developed a cooperative joint-inversion approach intended to facilitate recovery of acoustic impedance (AI) using seismic and magnetotelluric (MT) data. In this approach, the MT data provided a pathway for iteratively building large-scale low-frequency information content not directly recoverable from the seismic data themselves. The MT data provided complementary information to seismic, especially in seismically complex terrains such as overthrust belts, subbasalt and subsalt, carbonate reefs or for targets below deep cover containing limestone, concretionary layers, or basalt. On the other hand, the seismic data provided structural information necessary to derive accurate resistivity models from MT inversion and small-scale features during seismic impedance inversion. The connections between resistivity and the elastic property of rocks are obtained from petrophysical relationships derived from available borehole data, or if not available, from empirical relationships. We tested our technique on synthetic and field data. The application of cooperative joint inversion to 3D seismic and MT data sets acquired in a mineral province made it possible to recover AI distribution across a wide range of geologic environments. The resulting rock property images provided a direct link to geology that is exceedingly difficult, if not impossible, to extract from the individual data sets.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".