Seismic Moment Tensors of Microseismic Events: General Solution vs Double-Couple Solutions
Bibliographic record
Abstract
Summary Injection of fluids into a reservoir generates microseismic events. When multiple arrays of instruments are used to record the acoustic energy generated by the movement of the rock, the nature and orientation of this deformation can be modeled with the seismic moment tensor (SMT). A general solution for the SMT allows for the determination of volumetric change as well as shear-slip at an angle to the failure plane but, in order to solve for these failure components requires sensors at several azimuths to the event to constrain this more complicated model. In many acquisition scenarios, the array geometries preclude the robust calculation of general solutions. In these cases, a simpler model based on a shear failure, equivalent to a double-couple (DC) source, which requires the slip to be parallel to the failure plane, can be utilized Here, we compare the failure planes from these two seismic moment tensor models using microseismic data recorded during a hydraulic fracture stage in the Horn River Basin. The full general solution provides an accurate representation of the source mechanism. When robust general solutions are unavailable, DC solutions provide an approximate representation of the failure plane when the events have an event mixture of shear/tensile crack openings and closures. The obtained solutions are compared and we assess the effectiveness of applied approaches to identify fracture orientations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".