Moment-Tensor Analysis of Seismicity Related to Hydraulic Fracturing in North America
Bibliographic record
Abstract
Various types of seismic arrays are being used to monitor seismicity associated with oil and gas operations. These different types of recording systems exhibit significantly different characteristics in azimuthal coverage, magnitude detection threshold, signal-to-noise ratio and waveform frequency content. Taking these factors into account, this thesis focuses on implementing source-mechanism inversions for three typical monitoring geometries including the regional seismic network, sparse surface array and dense shallow borehole array. With the aim of investigating potential indicators for the discrimination between fluid-injection induced seismicity and natural earthquakes in the Western Canada Sedimentary Basin, the waveform-fitting-based moment-tensor inversion is performed for eight induced earthquakes with M > 3 and a nearby M 5.3 inferred natural earthquake, all of which were recorded by regional seismic networks. Based on the inverted parameters, the focal depth is found to be the most robust for parameter to distinguish the induced seismicity from natural earthquakes since the induced events considered here are significantly shallower than one observed nature event and most intraplate earthquakes in the Canadian Shield. Moreover, in addition to a dominant double-couple (DC) mechanism this is common to nearly all events, non-negligible non-DC components (typically > 25%) are observed within the moment-tensor solutions for most of the induced events. To overcome limitations of typically low signal-to-noise ratio and poor azimuthal coverage for the sparse surface array, a novel regularized approach is developed to estimate a composite focal mechanism from a set of microearthquakes recorded by this type of array. It operates by minimizing the weighted misfits of both SH/P amplitude ratios (in absolute sense and logarithmic scale) and P-wave polarities, using a regularization parameter determined from the trade-off curve for these values. The regularized approach reduces the multiplicity of solutions and avoids the use of signed amplitude ratios, which may be ambiguous for data with low signal-to-noise ratio. For the hydraulic-fracturing induced event sequences recorded by a dense shallow borehole array near Fox Creek area, a least-squares inversion based on 3C P-wave amplitudes has been implemented, which avoids fitting the relatively high-frequency waveforms and the picking of S-wave amplitudes that are contaminated by P-wave coda. The recovered source mechanisms are dominantly strike-slip with sub-vertical nodal planes, although a distinct cluster of events is characterized by more complex mechanisms with slip on a shallow-dipping plane accompanied by significant (> 30%) non-DC components. The non-DC components may be due to tensile crack opening and/or co-slipping on several fault strands that have been mapped using 3D seismic data. In addition, in the absence of direct stress measurements, moment-tensor solutions of the hydraulic-fracturing induced event sequences are used to estimate the local stress field near the Fox Creek area. The estimated orientation of SHmax differs from the median regional SHmax direction by ~ 15º, but it agrees with the nearest available borehole measurement from the World Stress Map. Mohr circle analysis indicates that N-S trending faults, which hosted the largest events (MW > 1.5), are mis-oriented for slip and required a relatively large increase in pore pressure (12 ± 4 MPa) in order to be brought to a state of incipient failure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".