Simultaneous waveform inversion of seismic-while-drilling data for P-wave velocity, density, and source parameters
Bibliographic record
Abstract
Full waveform inversion (FWI), as an optimization-based approach in estimating subsurface models, is limited by incomplete acquisition and illumination of the subsurface. Adding data corresponding to new and independent ray paths as input could lead to significant increases in the reliability of FWI models. In principle, seismic-while drilling (SWD) technology can supply these additional ray paths, however, it introduces a new suite of unknowns, namely precise source locations (i.e., drilling path), source signature, and radiation characteristics. Here we formulate a new elastic FWI algorithm in which source positions and radiation patterns join the velocity and density values of the grid cells as unknowns to be determined. We then carry out a numerical inversion experiment with the SWD sources located along a plausible well-trajectory through a synthetic model. These SWD sources are supplemented by explosive sources and multicomponent receivers at the surface, simulating a conventional acquisition geometry. The subsurface model and SWD source properties are recovered and analyzed. After adding SWD data, both the inversion of elastic properties and source mechanisms get considerably enhanced, and the inversion shows a directional preference on the well trajectories. The analysis suggests that, in principle, SWD participation improves the accuracy of FWI models. However, further related study is required to provide more comprehensive radiation patterns of the SWD sources.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".