Integrating Passive and Active Seismic Methods in Sparse Monitoring Networks Using SADAR Arrays
Bibliographic record
Abstract
Summary For geologic carbon storage (GCS) to reach full commercial capabilities, reoccurring measurement monitoring, and verification (MMV) operations need to be optimized. Co-locating multiphysics MMV capabilities within a sparse network will lower the MMV footprint and reduce redundant infrastructure providing cost savings. Integrating permanent passive seismic arrays for microseismic monitoring and active seismic surveys advances these goals. The passive network of four SADAR compact volumetric phased arrays monitoring seismicity at the Newell County Field Research Station has recently been demonstrated for active-source imaging with the objective of integrating seismic monitoring capabilities. Routinely performed VSP surveys are suitable for generating optimum-offset images using the individual SADAR phased arrays. Coherent processing of the SADAR array data provides signal enhancements that benefit both passive seismic monitoring and active-source seismic reflection functions, improving results for both over networks of single-sensors. Integrating active-source seismic acquisition with the SADAR network passive monitoring infrastructure allows for frequent conformance and containment verification at GCS projects, thereby providing early warning of anomalies. The integrated seismic system will also provide a common foundation for including other technologies into multiphysics monitoring nodes
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".