Enhanced Detection and Location of Microseismic Events Using a Novel Matched Filtering Method
Bibliographic record
Abstract
Summary A new Matched Filtering Algorithm is proposed for detecting microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template (‘parent’) events, and representative spatial distribution of the recorded microseismicity. Detection and extraction of ‘child’ events are based on stacked, multi-channel cross-correlation of the continuous waveform data using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multi-component waveforms into the ray-centered co-ordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent’s hypocentre. Relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. A real-data example using microseismic monitoring data from an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents yields a significant (more than fourfold increase) in the number of located events compared with the original catalog Relative locations obtained using this approach provide more easily interpretable spatio-temporal patterns of the distribution of microseismicity, which can then be incorporated into the assessment of the stimulation program.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".