Fault Slip Tendency Analysis for a Deep-Sea Basalt CO2 Injection in the Cascadia Basin
Bibliographic record
Abstract
Offshore basalts, most commonly found as oceanic crust formed at mid-ocean ridges, are estimated to offer an almost unlimited reservoir for CO2 sequestration and are regarded as one of the most durable locations for carbon sequestration since injected CO2 will mineralize, forming carbonate rock. As part of the Solid Carbon project, the potential of the Cascadia Basin, about 200 km off the west coast of Vancouver Island, Canada, is investigated as a site for geological CO2 sequestration. In anticipation of a demonstration proposed to take place, it is essential to assess the tendency of geologic faults in the area to slip in the presence of CO2 injection, potentially causing seismic events. To understand the viability of the reservoir, a quantitative risk assessment of the proposed site area was conducted. This involved a detailed characterization of the proposed injection site to understand baseline stress and pressure conditions and identify individual faults or fault zones with the potential to slip and thereby generate seismicity. The results indicate that fault slip potential is minimal (less than 1%) for a constant injection of up to ~2.5 MT/yr. This is in part due to the thickness of the basalt aquifer and its permeability. The results provide a reference for assessing the potential earthquake risk from CO2 injection in similar ocean basalt basins.
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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 | 0.002 | 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) | 0.001 | 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".