The need for quantitative QC parameters for microseismic event identifications and locations
Bibliographic record
Abstract
Microseismic monitoring of hydraulic fracture treatments is performed by a variety of techniques; a downhole sensor array in a nearby offset well, a sparse network of sensors in shallow observation wells or a dense network of surface sensors. The results from all methods are provided to the interpreter as a spreadsheet of three-dimensional point distributions where each point might, or might not, be associated with individual quality-control parameters. In the absence of a quality control parameter each reported location can only treated with equal validity when it is known that this is not the case. There must be some uncertainty associated with the event location, and there is the possibility that the event is the result of noise rather than true signal, a “false positive”. Even when quality-control parameters are provided these are qualitative or poorly communicated and therefore often of only limited value in the interpretation. The two shown examples, multiple re-processing results of a downhole dataset and an example from simultaneous downhole and surface arrays illustrate the need and value of a transparent quality-control parameter. When using algorithms that require explicit arrival time identification in the raw data, the false identification of an event is highly unlikely. In contrast, when using migration type algorithms where the event is only visible in the stacked image function, the false identification of a microseismic event, i.e. ‘false positive’, is much more likely. Without the use of proper quality control parameters false positives appear inevitable which can lead to a wrong interpretation of the final map. Using a probabilistic interpretation of the image function (e.g. Mosegard and Tarantola, 2002, Lomax et al., 2009, Xuan, 2009, Xuan and Sava, 2010) appears to be an approach that provides an unbiased estimate of the true location accuracy and can also serve as a quantitative guideline in avoiding ‘False Positives’.
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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 | 0.027 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".