Passive seismic multicomponent imaging using geometrical optics theory
Bibliographic record
Abstract
Summary Here, a method of passive seismic imaging using multicomponent (3-C) data is presented. It is a Beamforming/Kirchhoff type migration, which is based upon the isotropic elastic wave equation within geometrical optics theory. To account for the effects of the source mechanism, polarity corrections are applied. Mathematically, the goal in a passive seismic survey is to characterize the source term in the elastic wave equation, given seismic velocities and measured displacements at some number of observation points. Following Haldorsen et al. (2013) approach, using Helmholtz decomposition (Muller, 2007), the source wavefield can be decomposed into a curl-free longitudinal component (L) and divergence-free transverse (T) components. They are utilized to locate and characterize the seismic event that sourced the wavefield. The method can be implemented for both surface and downhole receiver array geometries. Here we are presenting the method as it applies to downhole surveys. Both synthetic and field data examples are demonstrated. The synthetic example proves the feasibility of the imaging technique, by producing the resulting image coincident with the modeled synthetic event. The accuracy of the approach with a real world example is validated by quality-control of the imaging procedure, by the relative position to a treatment well of the event locations, and by the match of the imaged perforation shot to its known location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".