1D layered velocity models and microseismic event locations: synthetic examples for a case with a single linear receiver array
Bibliographic record
Abstract
We discuss various aspects of 1D velocity-model building for application to microseismic data analysis. We generate simple synthetic example data using a widely used single linear array geometry. The synthetic data contain 30 sources with known locations for a reference model based on previous studies of the Barnett shale. We investigate several key factors that should be considered, including selection of the calibration technique, inclusion of a priori information such as lateral heterogeneity and parameter ranges, and choice of algorithm for travel time computations. For the source–receiver geometry considered here, hypocenter location errors (±6 m in X and ±12 m in Z) can result from differently calibrated models only and without including the errors in picked arrival times and polarization estimates. We find that the errors in hypocenter locations are reduced (±3 m in X and ±6 m in Z) when a model calibrated with multiple shots simultaneously is used. Using four different models (vertical fault, dipping layers, channels, and these effects combined), we demonstrate that systematic errors in hypocenter locations can result when a 1D layered model is used in lieu of a laterally heterogeneous subsurface. Finally, we show that event locations from a velocity model calibrated using direct-arrival times are more stable than from a model calibrated with first-arrival times.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".