Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies
Bibliographic record
Abstract
Abstract In the quest for denser, nimbler, and lower-cost seismic surveys, the industry is seeing a revolution in the miniaturization of seismic equipment, with autonomous nodes approaching the size of a geophone and sources becoming portable by crews on foot. This has created a paradigm shift in the way seismic is acquired in difficult terrains, making zero-environmental-footprint surveys a reality while reducing cost and health, safety, and environmental risk. The simplification of survey operation and the new entry price of seismic surveys unlocked by these technologies are already benefiting industries beyond oil and gas exploration. High trace density seismic has become accessible to industries playing a key role in the net-zero era, such as geothermal and carbon capture, utilization, and storage (CCUS), to which a good understanding of the subsurface geology is crucial to their success. We describe these benefits as observed during an ultra-high-density seismic survey acquired in June 2020 through a partnership between STRYDE, Explor, and Carbon Management Canada over the Containment and Monitoring Institute site. The smallest and lightest source and receiver equipment in the industry were used to achieve a trace density of 257 million traces/km2 over this test site dedicated to CCUS studies. We discuss the operational efficiency of the seismic acquisition, innovative techniques for data transfer and surveying, and preliminary results of the seismic data processing with a focus on the near-surface model and fast-track time migration.
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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 | 0.001 | 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".