Uncertainty in surface microseismic monitoring
Bibliographic record
Abstract
Uncertainty in a migration based approach to surface and near surface microseismic monitoring occurs in two ways: uncertainty in the validity of detected events and uncertainty in the estimated position of the event. Synthetic modelling and comparison to case studies show that sign-to-noise-ratio is a key indicator of both types of the uncertainties. In this paper we present an analysis of both types of uncertainty using synthetic modelling to illustrate the performance characteristics of the migration process in terms of signal detection and false-alarm rates, along with uncertainties in positional estimates.Examples from two case studies will illustrate that this kind of performance is achievable in actual monitoring surveys. Signal-to-noise-ratio (SNR) is a key indicator of the uncertainty in migration based imaging of microseismic events. Reliability, in terms of the ability to detect the complete set of events is a nearly binary function of SNR. Events with SNR above a threshold of 2-3 are readily detected, while events with SNR below the threshold are missed. Positional uncertainties likewise are driven by SNR. While vertical uncertainty is more sensitive to noise, both horizontal and vertical uncertainties decrease rapidly with increasing SNR.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".