Relative location of microseismicity
Bibliographic record
Abstract
Applying the two-point paraxial ray tracing, we develop a technique for relative location of microseismic events. Our technique assumes the availability of a perforation shot or an already located microseismic event, termed the master, for which the paraxial ray tracing has been performed. The ray-tracing output for the master makes it possible to compute the relative locations of adjacent microseismic events, as many as a data set contains, with an efficient algorithm that requires no additional ray tracing and reduces to solving a series of simple, low-dimensional and well-behaved optimization problems. The relative event-location approach discussed in our paper is especially well suited for surface microseismic monitoring because the high accuracy of the paraxial ray approximation in the directions orthogonal to the reference rays, typically spanning the stimulated horizons for surface microseismic geometries, ensures the calculation of precise event hypocenters at appreciable distances from the master. Also, since our computations operate with differences of the recorded times of microseismic events rather than with the times themselves, inaccuracies in static corrections for surface receiver stations are largely eliminated. We test the relative location technique on synthetic and field microseismic data to demonstrate its accuracy, computational efficiency, and insensitivity to velocity errors.
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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 | 0.000 | 0.003 |
| 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 | 0.002 |
| 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".