Recent advances in optimized geophysical survey design
Bibliographic record
Abstract
Abstract Survey design ultimately dictates the quality of subsurface information provided by practical implementations of geophysical methods. It is therefore critical to design experimental procedures that cost effectively produce those data that maximize the desired information. This review cites recent advances in statistical experimental design techniques applied in the earth sciences. Examples from geoelectrical, crosswell and surface seismic, and microseismic monitoring methods are included. Using overdetermined 1D and 2D geoelectrical examples, a minor subset of judiciously chosen measurements provides a large percentage of the information content theoretically offered by the geoelectrical method. In contrast, an underdetermined 2D seismic traveltime tomography design study indicates that the information content increases almost linearly with the amount of traveltime data (source-receiver pairs) considered until the underdeterminancy is reduced substantially. An experimental design study of frequency-domain seismic-waveform inversion experiments reveals that a few optimally chosen frequencies offer as much subsurface information as the full bandwidth. A nonlinear experimental design for a seismic amplitude-versus-angle survey identifies those incidence angles most important for characterizing a reservoir. A nonlinear design example shows that designing microseismic monitoring surveys based on array aperture is a poor strategy that almost certainly leads to suboptimal designs.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".