Beyond first arrivals: improved microseismic event localization using both direct-path and head-wave arrivals
Bibliographic record
Abstract
Many important unconventional shale assets are hosted within regions where geological formations near the target layer create multiple paths through which the energy from a single microseismic event can reach an array of geophones positioned near target depth. These indirect arrivals, which include reflections and head waves, have traditionally been viewed as a nuisance in microseismic analysis, potentially causing additional false-positive event detections and erroneous localizations into the layers which generate them. In particular, the effects and potential use of head waves in microseismic analysis have been studied recently (Fuller et al., 2010; Zimmer, 2010, 2011a,b); however, these studies have focused on pre-survey design to avoid observing head waves and have discussed the use of direct-path and head-wave arrivals in combination for event localization only in limited cases. In this work, we demonstrate how head waves can be used to greatly improve microseismic event localization accuracy, particularly in the depth dimension, by analyzing them in addition to direct-path arrivals whenever they are observed. We then demonstrate an application of this multiple-arrival localization method to a data set collected from the Montney formation in Alberta, where previous conventional microseismic analysis produced event localizations spanning 400 meters in depth, extending deep into the high-velocity Belloy formation below, and where localizations incorporating both direct-path and head-wave information produced events within the Montney, in regions of expected fracture formation as indicated by surveyed seismic incoherence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".