Double-Pair Double-Difference Relocation Improves Depth Precision and Highlights Detailed 3D Fault Geometry for Induced Seismicity in Alberta, Canada
Bibliographic record
Abstract
Abstract Precise earthquake locations with well-constrained uncertainties can improve our understanding of faulting. Double-difference relocation methods, particularly event-pair double-difference relocations, are well established and have been applied to large earthquake catalogs to provide fault geometries. Previous adaptations of the event-pair double-difference method include data space extensions to use additional information from station pairs, referred to as double-pair double-difference relocation. We apply double-pair double-difference relocation to data from a dense network of borehole geophones for induced seismicity monitoring. This experiment was acquired in an area with strong lithological variation and sharp velocity contrasts, and most previous studies using this dataset are subject to poorly constrained focal depths. We compare the double-pair double-difference to event-pair double-difference relocations and study the effectiveness and uncertainties of both methods. Although double-pair double-difference relocation does not improve absolute locations, substantially improved relative locations and reduced uncertainties are obtained. The method reduces the impact of path effects in the source region, which is essential for applications where reservoir units in the source region can exhibit strong velocity contrasts, anisotropy, and fractures. From the improved relocation, we produce a detailed 3D fault interpretation of the dataset that constrains the geological interpretation. The improved catalog shows excellent depth constraints with seismicity that is restricted to specific geological units. We interpret that seismicity activated pre-existing faults in the reservoir layer and adjacent units. Notably, the results show no evidence of induced seismicity activating basement structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".