Integrated imaging: A powerful but undervalued tool
Bibliographic record
Abstract
Abstract Following the 2018 SEG Annual Meeting, the Gravity and Magnetics Committee held a postconvention workshop titled “Integrated imaging.” The half-day workshop attracted nearly 50 participants from various backgrounds. Three primary objectives of the workshop were to explore the nonseismic toolbox, highlight real examples of integrated projects that benefitted (or did not benefit) from nonseismic data, and provide geoscientists from all backgrounds a learning opportunity to see how they might optimize the value of their imaging projects via integration with relatively low-cost nonseismic methods. The workshop had a highly interactive format that differed from traditional presentation-based settings. After eight brief case studies were presented, three concurrent guided discussions ensued. Participants were divided into three groups, and each group focused on one discussion topic at a time. The groups rotated, allowing everyone to discuss all three topics. The first discussion was centered on two general questions: what is integrated imaging, and what tools are available for it? The second discussion provided an opportunity to examine the relationships between different physical properties that must be managed during integrated multiphysics analysis. The third discussion focused on the costs and benefits of a multiparameter data acquisition. According to feedback from participants, these discussions were the most valuable part of the workshop. The participants agreed that an integrated approach in geophysical data analysis is a powerful but currently undervalued tool. Also noted were the value of integration with nonseismic methods illustrated in the case studies and the need for the integrated approach in data analysis to be taught in schools in addition to the classic overview of individual geophysical methods.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".