Analysis of passive surface‐wave noise in surface microseismic data and its implications
Bibliographic record
Abstract
Tight gas reservoirs are projected to be a major portion of future energy resources. Because of their low permeability, hydraulic fracturing of these reservoirs is required to improve the permeability and reservoir productivity. Passive seismic monitoring is one of the few tools that can be used to characterize the changes in the reservoir due to hydraulic fracturing. Although the majority of the studies monitoring hydraulic fracturing exploit down hole microseismic data, surface microseismic monitoring is receiving increased attention because it is potentially much less expensive to acquire. Due to a broader receiver aperture and spatial coverage, surface microseismic data may be more advantageous than down hole microseismic data. The effectiveness of this monitoring technique, however, is strongly dependent on the signal-to-noise ratio of the data. Cultural and ambient noise can mask parts of the waveform that carry information about the subsurface, thereby decreasing the effectiveness of surface microseismic analysis in identifying and locating the microseismic events. Hence, time and spatially varying suppression of the surface-wave noise (ground roll) is a critical step in surface microseismic monitoring. Here, we study a surface passive dataset that was acquired over a Barnett Shale Formation reservoir during two weeks of hydraulic fracturing, in order to characterize and suppress the surface noise in this data. We apply techniques to identify the characteristics of the passive ground roll. Exploiting those characteristics, we can apply effective noise suppression technique(s) to the passive data.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".